count_variants.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count variants in each sample\n",
    "This Python Jupyter notebook counts occurrences of each barcode in each sample from Illumina barcode sequencing, and adds these counts to the codon variant table."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up analysis\n",
    "### Import Python modules.\n",
    "Use [plotnine](https://plotnine.readthedocs.io/en/stable/) for ggplot2-like plotting.\n",
    "\n",
    "The analysis relies heavily on the Bloom lab's [dms_variants](https://jbloomlab.github.io/dms_variants) package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import multiprocessing\n",
    "import multiprocessing.pool\n",
    "import os\n",
    "import warnings\n",
    "\n",
    "import alignparse\n",
    "import alignparse.targets\n",
    "\n",
    "import dms_variants.codonvarianttable\n",
    "from dms_variants.constants import CBPALETTE\n",
    "import dms_variants.illuminabarcodeparser\n",
    "import dms_variants.utils\n",
    "import dms_variants.plotnine_themes\n",
    "\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "from plotnine import *\n",
    "\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set [plotnine](https://plotnine.readthedocs.io/en/stable/) theme to the gray-grid one defined in `dms_variants`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "theme_set(dms_variants.plotnine_themes.theme_graygrid())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Versions of key software:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using alignparse version 0.1.3\n",
      "Using dms_variants version 0.6.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Using alignparse version {alignparse.__version__}\")\n",
    "print(f\"Using dms_variants version {dms_variants.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ignore warnings that clutter output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters for notebook\n",
    "Read the configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('config.yaml') as f:\n",
    "    config = yaml.safe_load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make output directory if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs(config['counts_dir'], exist_ok=True)\n",
    "os.makedirs(config['figs_dir'], exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize codon variant table\n",
    "Initialize the [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) using the wildtype gene sequence and the CSV file with the table of variants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read gene of 603 nt for SARS-CoV-2 from data/PacBio_amplicons.gb\n",
      "Initializing CodonVariantTable from gene sequence and results/variants/codon_variant_table.csv\n"
     ]
    }
   ],
   "source": [
    "# get wildtype gene sequence for primary target\n",
    "targets = alignparse.targets.Targets(seqsfile=config['amplicons'],\n",
    "                                     feature_parse_specs=config['feature_parse_specs'])\n",
    "geneseq = targets.get_target(config['primary_target']).get_feature('gene').seq\n",
    "print(f\"Read gene of {len(geneseq)} nt for {config['primary_target']} from {config['amplicons']}\")\n",
    "      \n",
    "print('Initializing CodonVariantTable from gene sequence and ' +\n",
    "      config['codon_variant_table_file'])\n",
    "      \n",
    "variants = dms_variants.codonvarianttable.CodonVariantTable(\n",
    "                geneseq=geneseq,\n",
    "                barcode_variant_file=config['codon_variant_table_file'],\n",
    "                substitutions_are_codon=True,\n",
    "                substitutions_col='codon_substitutions',\n",
    "                primary_target=config['primary_target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup to parse barcodes\n",
    "Read data frame with list of all barcode runs.\n",
    "Note how multiple R1 files are delimited by `; ` and are split out separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading list of barcode runs from data/barcode_runs.csv\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "      <th>sample_type</th>\n",
       "      <th>sort_bin</th>\n",
       "      <th>concentration</th>\n",
       "      <th>date</th>\n",
       "      <th>number_cells</th>\n",
       "      <th>R1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>6600000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L001_R1_001.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L001_R1_002.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L001_R1_003.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L001_R1_004.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L001_R1_005.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L002_R1_001.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L002_R1_002.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L002_R1_003.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L002_R1_004.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin1/200416_lib1_FITCbin1_TGGAACAA_L002_R1_005.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin2</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>3060000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin2/200416_lib1_FITCbin2_TGGCTTCA_L001_R1_001.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin2/200416_lib1_FITCbin2_TGGCTTCA_L001_R1_002.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin2/200416_lib1_FITCbin2_TGGCTTCA_L002_R1_001.fastq.gz, /fh/fast/bloom_j/SR/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin2/200416_lib1_FITCbin2_TGGCTTCA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin3</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>2511000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin3/200416_lib1_FITCbin3_TGGTGGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin3/200416_lib1_FITCbin3_TGGTGGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin3/200416_lib1_FITCbin3_TGGTGGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin3/200416_lib1_FITCbin3_TGGTGGTA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin4</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>2992000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin4/200416_lib1_FITCbin4_TTCACGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin4/200416_lib1_FITCbin4_TTCACGCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin4/200416_lib1_FITCbin4_TTCACGCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200416_lib1_FITCbin4/200416_lib1_FITCbin4_TTCACGCA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>6420000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L001_R1_004.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L002_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin1/200416_lib2_FITCbin1_TGGAACAA_L002_R1_004.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>SortSeq_bin2</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>2808000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin2/200416_lib2_FITCbin2_TGGCTTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin2/200416_lib2_FITCbin2_TGGCTTCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin2/200416_lib2_FITCbin2_TGGCTTCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin2/200416_lib2_FITCbin2_TGGCTTCA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>SortSeq_bin3</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>2574000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin3/200416_lib2_FITCbin3_TGGTGGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin3/200416_lib2_FITCbin3_TGGTGGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin3/200416_lib2_FITCbin3_TGGTGGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin3/200416_lib2_FITCbin3_TGGTGGTA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>SortSeq_bin4</td>\n",
       "      <td>SortSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200416</td>\n",
       "      <td>2530000</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin4/200416_lib2_FITCbin4_TTCACGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin4/200416_lib2_FITCbin4_TTCACGCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin4/200416_lib2_FITCbin4_TTCACGCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200416_lib2_FITCbin4/200416_lib2_FITCbin4_TTCACGCA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_01_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1098088</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b1/200420_s01-b1_AACGTGAT_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b1/200420_s01-b1_AACGTGAT_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_01_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>393166</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b2/200420_s01-b2_AAACATCG_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b2/200420_s01-b2_AAACATCG_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_01_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>718545</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b3/200420_s01-b3_ATGCCTAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b3/200420_s01-b3_ATGCCTAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_01_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3242062</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b4/200420_s01-b4_AGTGGTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b4/200420_s01-b4_AGTGGTCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b4/200420_s01-b4_AGTGGTCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s01-b4/200420_s01-b4_AGTGGTCA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_02_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1155923</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b1/200420_s02-b1_ACCACTGT_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b1/200420_s02-b1_ACCACTGT_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_02_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>428581</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b2/200420_s02-b2_ACATTGGC_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b2/200420_s02-b2_ACATTGGC_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_02_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>757956</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b3/200420_s02-b3_CAGATCTG_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b3/200420_s02-b3_CAGATCTG_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_02_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3309826</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b4/200420_s02-b4_CATCAAGT_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b4/200420_s02-b4_CATCAAGT_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b4/200420_s02-b4_CATCAAGT_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s02-b4/200420_s02-b4_CATCAAGT_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_03_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1263859</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b1/200420_s03-b1_CGCTGATC_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b1/200420_s03-b1_CGCTGATC_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_03_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>395754</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b2/200420_s03-b2_ACAAGCTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b2/200420_s03-b2_ACAAGCTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_03_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>792803</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b3/200420_s03-b3_CTGTAGCC_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b3/200420_s03-b3_CTGTAGCC_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_03_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3088611</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b4/200420_s03-b4_AGTACAAG_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b4/200420_s03-b4_AGTACAAG_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b4/200420_s03-b4_AGTACAAG_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s03-b4/200420_s03-b4_AGTACAAG_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_04_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1313645</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b1/200420_s04-b1_AACAACCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b1/200420_s04-b1_AACAACCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_04_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>491501</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b2/200420_s04-b2_AACCGAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b2/200420_s04-b2_AACCGAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_04_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>915779</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b3/200420_s04-b3_AACGCTTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b3/200420_s04-b3_AACGCTTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_04_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3016617</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b4/200420_s04-b4_AAGACGGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b4/200420_s04-b4_AAGACGGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b4/200420_s04-b4_AAGACGGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s04-b4/200420_s04-b4_AAGACGGA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_05_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1453744</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b1/200420_s05-b1_AAGGTACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b1/200420_s05-b1_AAGGTACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_05_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>339887</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b2/200420_s05-b2_ACACAGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b2/200420_s05-b2_ACACAGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_05_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>774823</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b3/200420_s05-b3_ACAGCAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b3/200420_s05-b3_ACAGCAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_05_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3016775</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b4/200420_s05-b4_ACCTCCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b4/200420_s05-b4_ACCTCCAA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b4/200420_s05-b4_ACCTCCAA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s05-b4/200420_s05-b4_ACCTCCAA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_06_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1748302</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b1/200420_s06-b1_ACGCTCGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b1/200420_s06-b1_ACGCTCGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_06_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>470059</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b2/200420_s06-b2_ACGTATCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b2/200420_s06-b2_ACGTATCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_06_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>914665</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b3/200420_s06-b3_ACTATGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b3/200420_s06-b3_ACTATGCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_06_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>2818049</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b4/200420_s06-b4_AGAGTCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b4/200420_s06-b4_AGAGTCAA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b4/200420_s06-b4_AGAGTCAA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s06-b4/200420_s06-b4_AGAGTCAA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_07_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1979689</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b1/200420_s07-b1_AGATCGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b1/200420_s07-b1_AGATCGCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_07_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>537885</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b2/200420_s07-b2_AGCAGGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b2/200420_s07-b2_AGCAGGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_07_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>890081</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b3/200420_s07-b3_AGTCACTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b3/200420_s07-b3_AGTCACTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_07_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>2516765</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b4/200420_s07-b4_ATCCTGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s07-b4/200420_s07-b4_ATCCTGTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_08_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>2169439</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b1/200420_s08-b1_ATTGAGGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b1/200420_s08-b1_ATTGAGGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_08_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>683426</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b2/200420_s08-b2_CAACCACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b2/200420_s08-b2_CAACCACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_08_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>939831</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b3/200420_s08-b3_GACTAGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b3/200420_s08-b3_GACTAGTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_08_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1933943</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b4/200420_s08-b4_CAATGGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s08-b4/200420_s08-b4_CAATGGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_09_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>2562240</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b1/200420_s09-b1_CACTTCGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b1/200420_s09-b1_CACTTCGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b1/200420_s09-b1_CACTTCGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b1/200420_s09-b1_CACTTCGA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_09_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>804434</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b2/200420_s09-b2_CAGCGTTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b2/200420_s09-b2_CAGCGTTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_09_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1241187</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b3/200420_s09-b3_CATACCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b3/200420_s09-b3_CATACCAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_09_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>770944</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b4/200420_s09-b4_CCAGTTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s09-b4/200420_s09-b4_CCAGTTCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_10_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3083369</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b1/200420_s10-b1_CCGAAGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b1/200420_s10-b1_CCGAAGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b1/200420_s10-b1_CCGAAGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b1/200420_s10-b1_CCGAAGTA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_10_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1231009</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b2/200420_s10-b2_CCGTGAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b2/200420_s10-b2_CCGTGAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_10_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1249638</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b3/200420_s10-b3_CCTCCTGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b3/200420_s10-b3_CCTCCTGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_10_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>14989</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b4/200420_s10-b4_CGAACTTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s10-b4/200420_s10-b4_CGAACTTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_11_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3515078</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b1/200420_s11-b1_CGACTGGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b1/200420_s11-b1_CGACTGGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b1/200420_s11-b1_CGACTGGA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b1/200420_s11-b1_CGACTGGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b1/200420_s11-b1_CGACTGGA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b1/200420_s11-b1_CGACTGGA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_11_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1774663</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b2/200420_s11-b2_CGCATACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b2/200420_s11-b2_CGCATACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_11_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>137284</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b3/200420_s11-b3_CTCAATGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b3/200420_s11-b3_CTCAATGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_11_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1315</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b4/200420_s11-b4_CTGAGCCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s11-b4/200420_s11-b4_CTGAGCCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_12_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>4359593</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b1/200420_s12-b1_CTGGCATA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b1/200420_s12-b1_CTGGCATA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b1/200420_s12-b1_CTGGCATA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b1/200420_s12-b1_CTGGCATA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b1/200420_s12-b1_CTGGCATA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b1/200420_s12-b1_CTGGCATA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_12_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1025749</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b2/200420_s12-b2_GAATCTGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b2/200420_s12-b2_GAATCTGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_12_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>4733</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b3/200420_s12-b3_CAAGACTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b3/200420_s12-b3_CAAGACTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_12_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1471</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b4/200420_s12-b4_GAGCTGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s12-b4/200420_s12-b4_GAGCTGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_13_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>4896651</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b1/200420_s13-b1_GATAGACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b1/200420_s13-b1_GATAGACA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b1/200420_s13-b1_GATAGACA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b1/200420_s13-b1_GATAGACA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b1/200420_s13-b1_GATAGACA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b1/200420_s13-b1_GATAGACA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_13_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>346010</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b2/200420_s13-b2_GCCACATA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b2/200420_s13-b2_GCCACATA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_13_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3460</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b3/200420_s13-b3_GCGAGTAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b3/200420_s13-b3_GCGAGTAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_13_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1340</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b4/200420_s13-b4_GCTAACGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s13-b4/200420_s13-b4_GCTAACGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_14_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>5115094</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b1/200420_s14-b1_GCTCGGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b1/200420_s14-b1_GCTCGGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b1/200420_s14-b1_GCTCGGTA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b1/200420_s14-b1_GCTCGGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b1/200420_s14-b1_GCTCGGTA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b1/200420_s14-b1_GCTCGGTA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_14_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>211469</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b2/200420_s14-b2_GGAGAACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b2/200420_s14-b2_GGAGAACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_14_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3186</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b3/200420_s14-b3_GGTGCGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b3/200420_s14-b3_GGTGCGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_14_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1243</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b4/200420_s14-b4_GTACGCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s14-b4/200420_s14-b4_GTACGCAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_15_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>4844047</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b1/200420_s15-b1_GTCGTAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b1/200420_s15-b1_GTCGTAGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b1/200420_s15-b1_GTCGTAGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b1/200420_s15-b1_GTCGTAGA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_15_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>189729</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b2/200420_s15-b2_GTCTGTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b2/200420_s15-b2_GTCTGTCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_15_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3206</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b3/200420_s15-b3_GTGTTCTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b3/200420_s15-b3_GTGTTCTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_15_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1305</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b4/200420_s15-b4_TAGGATGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s15-b4/200420_s15-b4_TAGGATGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_16_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>5012897</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b1/200420_s16-b1_TATCAGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b1/200420_s16-b1_TATCAGCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b1/200420_s16-b1_TATCAGCA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b1/200420_s16-b1_TATCAGCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b1/200420_s16-b1_TATCAGCA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b1/200420_s16-b1_TATCAGCA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_16_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>229348</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b2/200420_s16-b2_TCCGTCTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b2/200420_s16-b2_TCCGTCTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_16_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>3614</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b3/200420_s16-b3_TCTTCACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b3/200420_s16-b3_TCTTCACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_16_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200420</td>\n",
       "      <td>1494</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b4/200420_s16-b4_TGAAGAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0952_AHFCLCBCX3/Unaligned/Project_tstarr/Sample_200420_s16-b4/200420_s16-b4_TGAAGAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_01_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1038116</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b1/200422_s01-b1_AACGTGAT_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b1/200422_s01-b1_AACGTGAT_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_01_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>403045</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b2/200422_s01-b2_AAACATCG_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b2/200422_s01-b2_AAACATCG_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_01_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>723790</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b3/200422_s01-b3_ATGCCTAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b3/200422_s01-b3_ATGCCTAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_01_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3578165</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b4/200422_s01-b4_AGTGGTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b4/200422_s01-b4_AGTGGTCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b4/200422_s01-b4_AGTGGTCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s01-b4/200422_s01-b4_AGTGGTCA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_02_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1019028</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b1/200422_s02-b1_ACCACTGT_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b1/200422_s02-b1_ACCACTGT_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_02_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>396617</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b2/200422_s02-b2_ACATTGGC_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b2/200422_s02-b2_ACATTGGC_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_02_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>724996</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b3/200422_s02-b3_CAGATCTG_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b3/200422_s02-b3_CAGATCTG_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_02_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3347685</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b4/200422_s02-b4_CATCAAGT_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b4/200422_s02-b4_CATCAAGT_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b4/200422_s02-b4_CATCAAGT_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s02-b4/200422_s02-b4_CATCAAGT_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_03_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1101875</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b1/200422_s03-b1_CGCTGATC_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b1/200422_s03-b1_CGCTGATC_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_03_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>460767</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b2/200422_s03-b2_ACAAGCTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b2/200422_s03-b2_ACAAGCTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_03_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>800107</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b3/200422_s03-b3_CTGTAGCC_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b3/200422_s03-b3_CTGTAGCC_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_03_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3341125</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b4/200422_s03-b4_AGTACAAG_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b4/200422_s03-b4_AGTACAAG_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b4/200422_s03-b4_AGTACAAG_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s03-b4/200422_s03-b4_AGTACAAG_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_04_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1142980</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b1/200422_s04-b1_AACAACCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b1/200422_s04-b1_AACAACCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_04_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>465664</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b2/200422_s04-b2_AACCGAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b2/200422_s04-b2_AACCGAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_04_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>889705</td>\n",
       "      <td>[/shared/ngs/illumina/agreaney/200506_M03100_0566_000000000-CY95L/Data/Intensities/BaseCalls/lib2-s4-b3_S1_L001_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_04_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3052542</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b4/200422_s04-b4_AAGACGGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b4/200422_s04-b4_AAGACGGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b4/200422_s04-b4_AAGACGGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s04-b4/200422_s04-b4_AAGACGGA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_05_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1339281</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b1/200422_s05-b1_AAGGTACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b1/200422_s05-b1_AAGGTACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_05_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>324228</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b2/200422_s05-b2_ACACAGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b2/200422_s05-b2_ACACAGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_05_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>781546</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b3/200422_s05-b3_ACAGCAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b3/200422_s05-b3_ACAGCAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_05_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3172054</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b4/200422_s05-b4_ACCTCCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b4/200422_s05-b4_ACCTCCAA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b4/200422_s05-b4_ACCTCCAA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s05-b4/200422_s05-b4_ACCTCCAA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_06_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1605180</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b1/200422_s06-b1_ACGCTCGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b1/200422_s06-b1_ACGCTCGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_06_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>435834</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b2/200422_s06-b2_ACGTATCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b2/200422_s06-b2_ACGTATCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_06_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>899879</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b3/200422_s06-b3_ACTATGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b3/200422_s06-b3_ACTATGCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_06_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>2867494</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b4/200422_s06-b4_AGAGTCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b4/200422_s06-b4_AGAGTCAA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b4/200422_s06-b4_AGAGTCAA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s06-b4/200422_s06-b4_AGAGTCAA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_07_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1777908</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b1/200422_s07-b1_AGATCGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b1/200422_s07-b1_AGATCGCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_07_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>488462</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b2/200422_s07-b2_AGCAGGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b2/200422_s07-b2_AGCAGGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_07_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>894981</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b3/200422_s07-b3_AGTCACTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b3/200422_s07-b3_AGTCACTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_07_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>2534079</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b4/200422_s07-b4_ATCCTGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b4/200422_s07-b4_ATCCTGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b4/200422_s07-b4_ATCCTGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s07-b4/200422_s07-b4_ATCCTGTA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_08_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>2047995</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b1/200422_s08-b1_ATTGAGGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b1/200422_s08-b1_ATTGAGGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_08_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>673365</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b2/200422_s08-b2_CAACCACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b2/200422_s08-b2_CAACCACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_08_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1016558</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b3/200422_s08-b3_GACTAGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b3/200422_s08-b3_GACTAGTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_08_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>2108643</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b4/200422_s08-b4_CAATGGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s08-b4/200422_s08-b4_CAATGGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_09_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>2441808</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b1/200422_s09-b1_CACTTCGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b1/200422_s09-b1_CACTTCGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b1/200422_s09-b1_CACTTCGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_09_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>945878</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b2/200422_s09-b2_CAGCGTTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b2/200422_s09-b2_CAGCGTTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_09_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1578599</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b3/200422_s09-b3_CATACCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b3/200422_s09-b3_CATACCAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_09_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1014391</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b4/200422_s09-b4_CCAGTTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s09-b4/200422_s09-b4_CCAGTTCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_10_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3012020</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b1/200422_s10-b1_CCGAAGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b1/200422_s10-b1_CCGAAGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b1/200422_s10-b1_CCGAAGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b1/200422_s10-b1_CCGAAGTA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_10_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1424829</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b2/200422_s10-b2_CCGTGAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b2/200422_s10-b2_CCGTGAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_10_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1555906</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b3/200422_s10-b3_CCTCCTGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b3/200422_s10-b3_CCTCCTGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_10_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>21856</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b4/200422_s10-b4_CGAACTTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s10-b4/200422_s10-b4_CGAACTTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_11_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3554990</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b1/200422_s11-b1_CGACTGGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b1/200422_s11-b1_CGACTGGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b1/200422_s11-b1_CGACTGGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b1/200422_s11-b1_CGACTGGA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_11_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>2052098</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b2/200422_s11-b2_CGCATACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b2/200422_s11-b2_CGCATACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_11_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>185604</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b3/200422_s11-b3_CTCAATGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b3/200422_s11-b3_CTCAATGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_11_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1441</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b4/200422_s11-b4_CTGAGCCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s11-b4/200422_s11-b4_CTGAGCCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_12_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>5027756</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b1/200422_s12-b1_CTGGCATA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b1/200422_s12-b1_CTGGCATA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b1/200422_s12-b1_CTGGCATA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b1/200422_s12-b1_CTGGCATA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b1/200422_s12-b1_CTGGCATA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b1/200422_s12-b1_CTGGCATA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_12_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1478114</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b2/200422_s12-b2_GAATCTGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b2/200422_s12-b2_GAATCTGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_12_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>5798</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b3/200422_s12-b3_CAAGACTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b3/200422_s12-b3_CAAGACTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_12_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>12.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1773</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b4/200422_s12-b4_GAGCTGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s12-b4/200422_s12-b4_GAGCTGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_13_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>5189759</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b1/200422_s13-b1_GATAGACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b1/200422_s13-b1_GATAGACA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b1/200422_s13-b1_GATAGACA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b1/200422_s13-b1_GATAGACA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b1/200422_s13-b1_GATAGACA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b1/200422_s13-b1_GATAGACA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_13_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>711044</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b2/200422_s13-b2_GCCACATA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b2/200422_s13-b2_GCCACATA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_13_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>4043</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b3/200422_s13-b3_GCGAGTAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b3/200422_s13-b3_GCGAGTAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_13_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1684</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b4/200422_s13-b4_GCTAACGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s13-b4/200422_s13-b4_GCTAACGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_14_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>5468853</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b1/200422_s14-b1_GCTCGGTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b1/200422_s14-b1_GCTCGGTA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b1/200422_s14-b1_GCTCGGTA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b1/200422_s14-b1_GCTCGGTA_L002_R1_002.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_14_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>419255</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b2/200422_s14-b2_GGAGAACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b2/200422_s14-b2_GGAGAACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_14_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3489</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b3/200422_s14-b3_GGTGCGAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b3/200422_s14-b3_GGTGCGAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_14_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>14.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1471</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b4/200422_s14-b4_GTACGCAA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s14-b4/200422_s14-b4_GTACGCAA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_15_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>5788800</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b1/200422_s15-b1_GTCGTAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b1/200422_s15-b1_GTCGTAGA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b1/200422_s15-b1_GTCGTAGA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b1/200422_s15-b1_GTCGTAGA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b1/200422_s15-b1_GTCGTAGA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b1/200422_s15-b1_GTCGTAGA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_15_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>355156</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b2/200422_s15-b2_GTCTGTCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b2/200422_s15-b2_GTCTGTCA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_15_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3101</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b3/200422_s15-b3_GTGTTCTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b3/200422_s15-b3_GTGTTCTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_15_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1345</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b4/200422_s15-b4_TAGGATGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s15-b4/200422_s15-b4_TAGGATGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin1</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>5531498</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b1/200422_s16-b1_TATCAGCA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b1/200422_s16-b1_TATCAGCA_L001_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b1/200422_s16-b1_TATCAGCA_L001_R1_003.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b1/200422_s16-b1_TATCAGCA_L002_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b1/200422_s16-b1_TATCAGCA_L002_R1_002.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b1/200422_s16-b1_TATCAGCA_L002_R1_003.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin2</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>345182</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b2/200422_s16-b2_TCCGTCTA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b2/200422_s16-b2_TCCGTCTA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin3</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>3893</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b3/200422_s16-b3_TCTTCACA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b3/200422_s16-b3_TCTTCACA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin4</td>\n",
       "      <td>TiteSeq</td>\n",
       "      <td>4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>200422</td>\n",
       "      <td>1755</td>\n",
       "      <td>[/shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b4/200422_s16-b4_TGAAGAGA_L001_R1_001.fastq.gz, /shared/ngs/illumina/tstarr/200427_D00300_0953_BHFCMKBCX3/Unaligned/Project_tstarr/Sample_200422_s16-b4/200422_s16-b4_TGAAGAGA_L002_R1_001.fastq.gz]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Reading list of barcode runs from {config['barcode_runs']}\")\n",
    "\n",
    "barcode_runs = (pd.read_csv(config['barcode_runs'])\n",
    "                .assign(R1=lambda x: x['R1'].str.split('; '))\n",
    "                )\n",
    "      \n",
    "display(HTML(barcode_runs.to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure library / sample combinations are unique:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert len(barcode_runs) == len(barcode_runs.groupby(['library', 'sample']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure the the libraries for which we have barcode runs are all in our variant table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "unknown_libs = set(barcode_runs['library']) - set(variants.libraries)\n",
    "if unknown_libs:\n",
    "    raise ValueError(f\"Libraries with barcode runs not in variant table: {unknown_libs}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialize an [IlluminaBarcodeParser](https://jbloomlab.github.io/dms_variants/dms_variants.illuminabarcodeparser.html#dms_variants.illuminabarcodeparser.IlluminaBarcodeParser) for each library.\n",
    "\n",
    "First, get the length of the barcode from the alignment target after making sure the same length for all targets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barcodes of length 16\n"
     ]
    }
   ],
   "source": [
    "bclen = len(targets.targets[0].get_feature('barcode').seq)\n",
    "\n",
    "assert (bclen == len(target.get_feature('barcode').seq) for target in targets.targets)\n",
    "\n",
    "print(f\"Barcodes of length {bclen}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other barcode parsing params come from the config file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>upstream</td>\n",
       "      <td>GCTCGCGGCCGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>downstream</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>minq</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>upstream_mismatch</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>downstream_mismatch</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parser_params = config['illumina_barcode_parser_params']\n",
    "\n",
    "display(HTML(\n",
    "    pd.Series(parser_params, name='value')\n",
    "    .rename_axis(index='parameter')\n",
    "    .reset_index()\n",
    "    .to_html(index=False)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parser needs to know the set of valid barcodes, which are stored in the variant table and are different for each library.\n",
    "So we create a different parser for each library using these valid barcode sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of valid barcodes searched for by each parser:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>library</th>\n",
       "      <th>number of valid barcodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lib1</td>\n",
       "      <td>99648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>lib2</td>\n",
       "      <td>97691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create dict keyed by library, value is parser for library\n",
    "parsers = {lib: dms_variants.illuminabarcodeparser.IlluminaBarcodeParser(\n",
    "                    bclen=bclen,\n",
    "                    valid_barcodes=variants.valid_barcodes(lib),\n",
    "                    **parser_params)\n",
    "           for lib in variants.libraries}\n",
    "\n",
    "print('Number of valid barcodes searched for by each parser:')\n",
    "display(HTML(\n",
    "    pd.DataFrame([(lib, len(p.valid_barcodes)) for lib, p in parsers.items()],\n",
    "                 columns=['library', 'number of valid barcodes'])\n",
    "    .to_html(index=False)\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse barcodes\n",
    "We now parse the barcodes.\n",
    "Since this will take a while, we utilize multiple CPUs via the Python [multiprocessing](https://docs.python.org/3.6/library/multiprocessing.html) module.\n",
    "First, determine how many CPUs to use.\n",
    "We use the minimum of the user-specified number hardcoded below and the number actually available.\n",
    "(If you are running *interactively* on the Hutch cluster, you may need to reduce the number below in order to avoid an error as there is an enforced CPU limit on the home `rhino` nodes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 16 CPUs\n"
     ]
    }
   ],
   "source": [
    "ncpus = min(config['max_cpus'], multiprocessing.cpu_count())\n",
    "print(f\"Using {ncpus} CPUs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse the barcodes in parallel via a [multiprocessing.Pool](https://docs.python.org/3.6/library/multiprocessing.html#multiprocessing.pool.Pool) using all the available CPUs to get a list of the data frames with barcode counts / fates for each sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_func(parser, r1files, library, sample):\n",
    "    \"\"\"Convenience function to be starmapped to multiprocessing pool.\"\"\"\n",
    "    return parser.parse(r1files, add_cols={'library': library, 'sample': sample})\n",
    "\n",
    "# parallel computation of list of data frames\n",
    "with multiprocessing.pool.Pool(processes=ncpus) as pool:\n",
    "    bclist = pool.starmap(\n",
    "                process_func,\n",
    "                [(parsers[run.library], run.R1, run.library, run.sample)\n",
    "                  for run in barcode_runs.itertuples()],\n",
    "                )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now concatenate the list into data frames of barcode counts and barcode fates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few lines of counts data frame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>barcode</th>\n",
       "      <th>count</th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>ACTATCACTACGATTG</td>\n",
       "      <td>7889</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AGAATGTTTATTCAAA</td>\n",
       "      <td>7356</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AGTAAACTTAGGGAAT</td>\n",
       "      <td>7230</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TAAGGTTATTTGAATT</td>\n",
       "      <td>5556</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>TTCATCACTGTTTCCT</td>\n",
       "      <td>4947</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few lines of fates data frame:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fate</th>\n",
       "      <th>count</th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>valid barcode</td>\n",
       "      <td>22911930</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>invalid barcode</td>\n",
       "      <td>4670796</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>low quality barcode</td>\n",
       "      <td>2626939</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>failed chastity filter</td>\n",
       "      <td>2331253</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unparseable barcode</td>\n",
       "      <td>600832</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = pd.concat([samplecounts for samplecounts, _ in bclist],\n",
    "                   sort=False,\n",
    "                   ignore_index=True)\n",
    "\n",
    "print('First few lines of counts data frame:')\n",
    "display(HTML(counts.head().to_html(index=False)))\n",
    "\n",
    "fates = pd.concat([samplefates for _, samplefates in bclist],\n",
    "                  sort=False,\n",
    "                  ignore_index=True)\n",
    "\n",
    "print('First few lines of fates data frame:')\n",
    "display(HTML(fates.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine fates of parsed barcodes\n",
    "First, we'll analyze the \"fates\" of the parsed barcodes.\n",
    "These fates represent what happened to each Illumina read we parsed:\n",
    " - Did the barcode read fail the Illumina chastity filter?\n",
    " - Was the barcode *unparseable* (i.e., the read didn't appear to be a valid barcode based on flanking regions)?\n",
    " - Was the barcode sequence too *low quality* based on the Illumina quality scores?\n",
    " - Was the barcode parseable but *invalid* (i.e., not in our list of variant-associated barcodes in the codon variant table)?\n",
    " - Was the barcode *valid*, and so will be added to variant counts.\n",
    " \n",
    "First, we just write a CSV file with all the barcode fates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing barcode fates to results/counts/barcode_fates.csv\n"
     ]
    }
   ],
   "source": [
    "fatesfile = os.path.join(config['counts_dir'], 'barcode_fates.csv')\n",
    "print(f\"Writing barcode fates to {fatesfile}\")\n",
    "fates.to_csv(fatesfile, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we tabulate the barcode fates in wide format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fate</th>\n",
       "      <th>failed chastity filter</th>\n",
       "      <th>invalid barcode</th>\n",
       "      <th>low quality barcode</th>\n",
       "      <th>unparseable barcode</th>\n",
       "      <th>valid barcode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"68\" valign=\"top\">lib1</th>\n",
       "      <th>SortSeq_bin1</th>\n",
       "      <td>2331253</td>\n",
       "      <td>4670796</td>\n",
       "      <td>2626939</td>\n",
       "      <td>600832</td>\n",
       "      <td>22911930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SortSeq_bin2</th>\n",
       "      <td>1045738</td>\n",
       "      <td>1137307</td>\n",
       "      <td>1197536</td>\n",
       "      <td>248501</td>\n",
       "      <td>11262091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SortSeq_bin3</th>\n",
       "      <td>889973</td>\n",
       "      <td>943837</td>\n",
       "      <td>1012715</td>\n",
       "      <td>211971</td>\n",
       "      <td>9598488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SortSeq_bin4</th>\n",
       "      <td>1055757</td>\n",
       "      <td>1171914</td>\n",
       "      <td>1177048</td>\n",
       "      <td>256339</td>\n",
       "      <td>11220871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin1</th>\n",
       "      <td>249560</td>\n",
       "      <td>268373</td>\n",
       "      <td>277525</td>\n",
       "      <td>62290</td>\n",
       "      <td>2700607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin2</th>\n",
       "      <td>109897</td>\n",
       "      <td>119077</td>\n",
       "      <td>121114</td>\n",
       "      <td>36476</td>\n",
       "      <td>1187738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin3</th>\n",
       "      <td>150504</td>\n",
       "      <td>158933</td>\n",
       "      <td>170936</td>\n",
       "      <td>31196</td>\n",
       "      <td>1644053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin4</th>\n",
       "      <td>688025</td>\n",
       "      <td>881308</td>\n",
       "      <td>792964</td>\n",
       "      <td>153704</td>\n",
       "      <td>7287878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin1</th>\n",
       "      <td>257907</td>\n",
       "      <td>277507</td>\n",
       "      <td>289607</td>\n",
       "      <td>72278</td>\n",
       "      <td>2809360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin2</th>\n",
       "      <td>105114</td>\n",
       "      <td>115647</td>\n",
       "      <td>124187</td>\n",
       "      <td>33065</td>\n",
       "      <td>1143307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin3</th>\n",
       "      <td>187153</td>\n",
       "      <td>197639</td>\n",
       "      <td>213204</td>\n",
       "      <td>41004</td>\n",
       "      <td>2058310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin4</th>\n",
       "      <td>685007</td>\n",
       "      <td>886529</td>\n",
       "      <td>767887</td>\n",
       "      <td>167315</td>\n",
       "      <td>7313324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin1</th>\n",
       "      <td>280668</td>\n",
       "      <td>313498</td>\n",
       "      <td>326850</td>\n",
       "      <td>71373</td>\n",
       "      <td>3044424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin2</th>\n",
       "      <td>117652</td>\n",
       "      <td>127576</td>\n",
       "      <td>130633</td>\n",
       "      <td>28469</td>\n",
       "      <td>1286697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin3</th>\n",
       "      <td>191870</td>\n",
       "      <td>201841</td>\n",
       "      <td>219892</td>\n",
       "      <td>41666</td>\n",
       "      <td>2049637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin4</th>\n",
       "      <td>680189</td>\n",
       "      <td>848718</td>\n",
       "      <td>766133</td>\n",
       "      <td>162972</td>\n",
       "      <td>7149701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin1</th>\n",
       "      <td>348503</td>\n",
       "      <td>382615</td>\n",
       "      <td>383757</td>\n",
       "      <td>84566</td>\n",
       "      <td>3744362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin2</th>\n",
       "      <td>138763</td>\n",
       "      <td>148496</td>\n",
       "      <td>156487</td>\n",
       "      <td>31322</td>\n",
       "      <td>1503623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin3</th>\n",
       "      <td>212244</td>\n",
       "      <td>238704</td>\n",
       "      <td>240663</td>\n",
       "      <td>43537</td>\n",
       "      <td>2292308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin4</th>\n",
       "      <td>587904</td>\n",
       "      <td>838239</td>\n",
       "      <td>754851</td>\n",
       "      <td>144424</td>\n",
       "      <td>7016995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin1</th>\n",
       "      <td>352886</td>\n",
       "      <td>386112</td>\n",
       "      <td>393734</td>\n",
       "      <td>82707</td>\n",
       "      <td>3853093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin2</th>\n",
       "      <td>91013</td>\n",
       "      <td>102509</td>\n",
       "      <td>101341</td>\n",
       "      <td>22148</td>\n",
       "      <td>978765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin3</th>\n",
       "      <td>186949</td>\n",
       "      <td>207858</td>\n",
       "      <td>215751</td>\n",
       "      <td>44648</td>\n",
       "      <td>2116788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin4</th>\n",
       "      <td>695971</td>\n",
       "      <td>914535</td>\n",
       "      <td>790855</td>\n",
       "      <td>166334</td>\n",
       "      <td>7468906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin1</th>\n",
       "      <td>381675</td>\n",
       "      <td>417566</td>\n",
       "      <td>431501</td>\n",
       "      <td>85322</td>\n",
       "      <td>4147222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin2</th>\n",
       "      <td>128504</td>\n",
       "      <td>136297</td>\n",
       "      <td>141917</td>\n",
       "      <td>26575</td>\n",
       "      <td>1362145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin3</th>\n",
       "      <td>229107</td>\n",
       "      <td>361140</td>\n",
       "      <td>255596</td>\n",
       "      <td>45188</td>\n",
       "      <td>2372570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin4</th>\n",
       "      <td>691259</td>\n",
       "      <td>807034</td>\n",
       "      <td>787906</td>\n",
       "      <td>164022</td>\n",
       "      <td>7496634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin1</th>\n",
       "      <td>458133</td>\n",
       "      <td>511597</td>\n",
       "      <td>517075</td>\n",
       "      <td>99670</td>\n",
       "      <td>4910732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin2</th>\n",
       "      <td>113744</td>\n",
       "      <td>157422</td>\n",
       "      <td>129211</td>\n",
       "      <td>24262</td>\n",
       "      <td>1194254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin3</th>\n",
       "      <td>206271</td>\n",
       "      <td>372278</td>\n",
       "      <td>237680</td>\n",
       "      <td>40764</td>\n",
       "      <td>2137116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin4</th>\n",
       "      <td>551861</td>\n",
       "      <td>553887</td>\n",
       "      <td>617131</td>\n",
       "      <td>122640</td>\n",
       "      <td>6037524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin1</th>\n",
       "      <td>499765</td>\n",
       "      <td>589565</td>\n",
       "      <td>567191</td>\n",
       "      <td>112653</td>\n",
       "      <td>5558178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin2</th>\n",
       "      <td>154023</td>\n",
       "      <td>213444</td>\n",
       "      <td>171180</td>\n",
       "      <td>35136</td>\n",
       "      <td>1597576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin3</th>\n",
       "      <td>215697</td>\n",
       "      <td>225111</td>\n",
       "      <td>242368</td>\n",
       "      <td>47350</td>\n",
       "      <td>2359435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin4</th>\n",
       "      <td>430717</td>\n",
       "      <td>444656</td>\n",
       "      <td>484083</td>\n",
       "      <td>101405</td>\n",
       "      <td>4721167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin1</th>\n",
       "      <td>580802</td>\n",
       "      <td>756866</td>\n",
       "      <td>650007</td>\n",
       "      <td>126792</td>\n",
       "      <td>6175404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin2</th>\n",
       "      <td>173319</td>\n",
       "      <td>232777</td>\n",
       "      <td>197122</td>\n",
       "      <td>36345</td>\n",
       "      <td>1812231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin3</th>\n",
       "      <td>294079</td>\n",
       "      <td>304012</td>\n",
       "      <td>327659</td>\n",
       "      <td>66784</td>\n",
       "      <td>3190405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin4</th>\n",
       "      <td>188854</td>\n",
       "      <td>188507</td>\n",
       "      <td>215909</td>\n",
       "      <td>45969</td>\n",
       "      <td>2048085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin1</th>\n",
       "      <td>756872</td>\n",
       "      <td>981268</td>\n",
       "      <td>862715</td>\n",
       "      <td>160892</td>\n",
       "      <td>8023484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin2</th>\n",
       "      <td>281098</td>\n",
       "      <td>320450</td>\n",
       "      <td>322034</td>\n",
       "      <td>63494</td>\n",
       "      <td>3022638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin3</th>\n",
       "      <td>290190</td>\n",
       "      <td>294598</td>\n",
       "      <td>326509</td>\n",
       "      <td>69948</td>\n",
       "      <td>3159081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin4</th>\n",
       "      <td>5516</td>\n",
       "      <td>6138</td>\n",
       "      <td>6148</td>\n",
       "      <td>1470</td>\n",
       "      <td>58988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin1</th>\n",
       "      <td>1219956</td>\n",
       "      <td>1605526</td>\n",
       "      <td>1392119</td>\n",
       "      <td>253864</td>\n",
       "      <td>13017514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin2</th>\n",
       "      <td>393748</td>\n",
       "      <td>496801</td>\n",
       "      <td>448145</td>\n",
       "      <td>87951</td>\n",
       "      <td>4266103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin3</th>\n",
       "      <td>61351</td>\n",
       "      <td>65219</td>\n",
       "      <td>68819</td>\n",
       "      <td>14740</td>\n",
       "      <td>680788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin4</th>\n",
       "      <td>46569</td>\n",
       "      <td>52769</td>\n",
       "      <td>54061</td>\n",
       "      <td>9305</td>\n",
       "      <td>499246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin1</th>\n",
       "      <td>1419373</td>\n",
       "      <td>1808954</td>\n",
       "      <td>1639054</td>\n",
       "      <td>305186</td>\n",
       "      <td>15372094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin2</th>\n",
       "      <td>239351</td>\n",
       "      <td>257391</td>\n",
       "      <td>269712</td>\n",
       "      <td>61467</td>\n",
       "      <td>2660351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin3</th>\n",
       "      <td>137695</td>\n",
       "      <td>147554</td>\n",
       "      <td>154579</td>\n",
       "      <td>34856</td>\n",
       "      <td>1504560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin4</th>\n",
       "      <td>130862</td>\n",
       "      <td>134971</td>\n",
       "      <td>147346</td>\n",
       "      <td>30231</td>\n",
       "      <td>1426976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin1</th>\n",
       "      <td>1261264</td>\n",
       "      <td>1542427</td>\n",
       "      <td>1416717</td>\n",
       "      <td>284478</td>\n",
       "      <td>13578500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin2</th>\n",
       "      <td>87171</td>\n",
       "      <td>95693</td>\n",
       "      <td>98500</td>\n",
       "      <td>22148</td>\n",
       "      <td>939596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin3</th>\n",
       "      <td>105507</td>\n",
       "      <td>110039</td>\n",
       "      <td>120105</td>\n",
       "      <td>28708</td>\n",
       "      <td>1146755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin4</th>\n",
       "      <td>28706</td>\n",
       "      <td>28132</td>\n",
       "      <td>32055</td>\n",
       "      <td>6867</td>\n",
       "      <td>312806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin1</th>\n",
       "      <td>1165597</td>\n",
       "      <td>1415161</td>\n",
       "      <td>1349211</td>\n",
       "      <td>254953</td>\n",
       "      <td>12664233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin2</th>\n",
       "      <td>62204</td>\n",
       "      <td>73443</td>\n",
       "      <td>70335</td>\n",
       "      <td>16921</td>\n",
       "      <td>662029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin3</th>\n",
       "      <td>153467</td>\n",
       "      <td>172027</td>\n",
       "      <td>180392</td>\n",
       "      <td>36377</td>\n",
       "      <td>1696066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin4</th>\n",
       "      <td>5747</td>\n",
       "      <td>6347</td>\n",
       "      <td>6244</td>\n",
       "      <td>3275</td>\n",
       "      <td>57430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin1</th>\n",
       "      <td>1050819</td>\n",
       "      <td>1258461</td>\n",
       "      <td>1197625</td>\n",
       "      <td>237471</td>\n",
       "      <td>11322029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin2</th>\n",
       "      <td>63789</td>\n",
       "      <td>71555</td>\n",
       "      <td>71986</td>\n",
       "      <td>20132</td>\n",
       "      <td>682348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin3</th>\n",
       "      <td>93947</td>\n",
       "      <td>108397</td>\n",
       "      <td>107522</td>\n",
       "      <td>22866</td>\n",
       "      <td>1031656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin4</th>\n",
       "      <td>49941</td>\n",
       "      <td>51920</td>\n",
       "      <td>56575</td>\n",
       "      <td>11337</td>\n",
       "      <td>555716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin1</th>\n",
       "      <td>1147190</td>\n",
       "      <td>1360402</td>\n",
       "      <td>1279829</td>\n",
       "      <td>253843</td>\n",
       "      <td>12369694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin2</th>\n",
       "      <td>73458</td>\n",
       "      <td>83023</td>\n",
       "      <td>83012</td>\n",
       "      <td>17154</td>\n",
       "      <td>790315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin3</th>\n",
       "      <td>134284</td>\n",
       "      <td>130652</td>\n",
       "      <td>152216</td>\n",
       "      <td>29668</td>\n",
       "      <td>1451340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin4</th>\n",
       "      <td>26973</td>\n",
       "      <td>31067</td>\n",
       "      <td>28653</td>\n",
       "      <td>6630</td>\n",
       "      <td>274585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"68\" valign=\"top\">lib2</th>\n",
       "      <th>SortSeq_bin1</th>\n",
       "      <td>939431</td>\n",
       "      <td>4031189</td>\n",
       "      <td>2317300</td>\n",
       "      <td>478426</td>\n",
       "      <td>21170285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SortSeq_bin2</th>\n",
       "      <td>425911</td>\n",
       "      <td>952092</td>\n",
       "      <td>1071412</td>\n",
       "      <td>217151</td>\n",
       "      <td>10534348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SortSeq_bin3</th>\n",
       "      <td>395887</td>\n",
       "      <td>874634</td>\n",
       "      <td>985042</td>\n",
       "      <td>198148</td>\n",
       "      <td>9748300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SortSeq_bin4</th>\n",
       "      <td>395822</td>\n",
       "      <td>898642</td>\n",
       "      <td>968900</td>\n",
       "      <td>192660</td>\n",
       "      <td>9673196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin1</th>\n",
       "      <td>117390</td>\n",
       "      <td>240786</td>\n",
       "      <td>286088</td>\n",
       "      <td>68200</td>\n",
       "      <td>2890223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin2</th>\n",
       "      <td>52715</td>\n",
       "      <td>109567</td>\n",
       "      <td>126721</td>\n",
       "      <td>39998</td>\n",
       "      <td>1298503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin3</th>\n",
       "      <td>78301</td>\n",
       "      <td>168259</td>\n",
       "      <td>194403</td>\n",
       "      <td>37643</td>\n",
       "      <td>1955607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_01_bin4</th>\n",
       "      <td>360472</td>\n",
       "      <td>798982</td>\n",
       "      <td>908696</td>\n",
       "      <td>167806</td>\n",
       "      <td>8893124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin1</th>\n",
       "      <td>104277</td>\n",
       "      <td>216073</td>\n",
       "      <td>258232</td>\n",
       "      <td>66285</td>\n",
       "      <td>2592190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin2</th>\n",
       "      <td>49904</td>\n",
       "      <td>100231</td>\n",
       "      <td>122529</td>\n",
       "      <td>32274</td>\n",
       "      <td>1176759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin3</th>\n",
       "      <td>73704</td>\n",
       "      <td>157860</td>\n",
       "      <td>183496</td>\n",
       "      <td>37412</td>\n",
       "      <td>1851727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_02_bin4</th>\n",
       "      <td>301750</td>\n",
       "      <td>678824</td>\n",
       "      <td>742427</td>\n",
       "      <td>160181</td>\n",
       "      <td>7554582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin1</th>\n",
       "      <td>107621</td>\n",
       "      <td>221787</td>\n",
       "      <td>277124</td>\n",
       "      <td>64585</td>\n",
       "      <td>2696489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin2</th>\n",
       "      <td>49838</td>\n",
       "      <td>106978</td>\n",
       "      <td>122923</td>\n",
       "      <td>27236</td>\n",
       "      <td>1256119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin3</th>\n",
       "      <td>85052</td>\n",
       "      <td>177033</td>\n",
       "      <td>210010</td>\n",
       "      <td>42821</td>\n",
       "      <td>2052114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_03_bin4</th>\n",
       "      <td>332781</td>\n",
       "      <td>733462</td>\n",
       "      <td>813183</td>\n",
       "      <td>169318</td>\n",
       "      <td>8084171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin1</th>\n",
       "      <td>124766</td>\n",
       "      <td>249574</td>\n",
       "      <td>299679</td>\n",
       "      <td>69283</td>\n",
       "      <td>3031598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin2</th>\n",
       "      <td>49183</td>\n",
       "      <td>105868</td>\n",
       "      <td>121843</td>\n",
       "      <td>26286</td>\n",
       "      <td>1220820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin3</th>\n",
       "      <td>0</td>\n",
       "      <td>982650</td>\n",
       "      <td>1421722</td>\n",
       "      <td>303225</td>\n",
       "      <td>10654028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_04_bin4</th>\n",
       "      <td>294911</td>\n",
       "      <td>664313</td>\n",
       "      <td>748761</td>\n",
       "      <td>138151</td>\n",
       "      <td>7343272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin1</th>\n",
       "      <td>147164</td>\n",
       "      <td>302515</td>\n",
       "      <td>359089</td>\n",
       "      <td>76518</td>\n",
       "      <td>3641217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin2</th>\n",
       "      <td>44908</td>\n",
       "      <td>96641</td>\n",
       "      <td>107778</td>\n",
       "      <td>24127</td>\n",
       "      <td>1085269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin3</th>\n",
       "      <td>74746</td>\n",
       "      <td>159590</td>\n",
       "      <td>182116</td>\n",
       "      <td>41460</td>\n",
       "      <td>1868990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_05_bin4</th>\n",
       "      <td>315787</td>\n",
       "      <td>709814</td>\n",
       "      <td>793893</td>\n",
       "      <td>162522</td>\n",
       "      <td>8002332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin1</th>\n",
       "      <td>160247</td>\n",
       "      <td>334950</td>\n",
       "      <td>399423</td>\n",
       "      <td>85170</td>\n",
       "      <td>4017509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin2</th>\n",
       "      <td>52565</td>\n",
       "      <td>118178</td>\n",
       "      <td>126153</td>\n",
       "      <td>25123</td>\n",
       "      <td>1259230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin3</th>\n",
       "      <td>97821</td>\n",
       "      <td>208098</td>\n",
       "      <td>233527</td>\n",
       "      <td>41622</td>\n",
       "      <td>2391861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_06_bin4</th>\n",
       "      <td>308284</td>\n",
       "      <td>691786</td>\n",
       "      <td>773538</td>\n",
       "      <td>156772</td>\n",
       "      <td>7734813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin1</th>\n",
       "      <td>194244</td>\n",
       "      <td>393612</td>\n",
       "      <td>472471</td>\n",
       "      <td>99218</td>\n",
       "      <td>4702948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin2</th>\n",
       "      <td>52326</td>\n",
       "      <td>116591</td>\n",
       "      <td>127321</td>\n",
       "      <td>24333</td>\n",
       "      <td>1264620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin3</th>\n",
       "      <td>94985</td>\n",
       "      <td>198927</td>\n",
       "      <td>238033</td>\n",
       "      <td>42824</td>\n",
       "      <td>2409120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_07_bin4</th>\n",
       "      <td>462266</td>\n",
       "      <td>1034529</td>\n",
       "      <td>1143046</td>\n",
       "      <td>221380</td>\n",
       "      <td>11560628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin1</th>\n",
       "      <td>213007</td>\n",
       "      <td>465389</td>\n",
       "      <td>539457</td>\n",
       "      <td>111945</td>\n",
       "      <td>5528877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin2</th>\n",
       "      <td>69858</td>\n",
       "      <td>143615</td>\n",
       "      <td>166406</td>\n",
       "      <td>34146</td>\n",
       "      <td>1676896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin3</th>\n",
       "      <td>108385</td>\n",
       "      <td>236180</td>\n",
       "      <td>269558</td>\n",
       "      <td>53212</td>\n",
       "      <td>2724530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_08_bin4</th>\n",
       "      <td>216238</td>\n",
       "      <td>485978</td>\n",
       "      <td>542942</td>\n",
       "      <td>107967</td>\n",
       "      <td>5495579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin1</th>\n",
       "      <td>256557</td>\n",
       "      <td>542537</td>\n",
       "      <td>624923</td>\n",
       "      <td>133753</td>\n",
       "      <td>6311426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin2</th>\n",
       "      <td>95478</td>\n",
       "      <td>194541</td>\n",
       "      <td>234969</td>\n",
       "      <td>47184</td>\n",
       "      <td>2332448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin3</th>\n",
       "      <td>179993</td>\n",
       "      <td>401720</td>\n",
       "      <td>436091</td>\n",
       "      <td>91279</td>\n",
       "      <td>4414252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_09_bin4</th>\n",
       "      <td>114639</td>\n",
       "      <td>250435</td>\n",
       "      <td>289076</td>\n",
       "      <td>56223</td>\n",
       "      <td>2837083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin1</th>\n",
       "      <td>348721</td>\n",
       "      <td>729809</td>\n",
       "      <td>862388</td>\n",
       "      <td>171248</td>\n",
       "      <td>8523150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin2</th>\n",
       "      <td>149306</td>\n",
       "      <td>329905</td>\n",
       "      <td>373681</td>\n",
       "      <td>72432</td>\n",
       "      <td>3687942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin3</th>\n",
       "      <td>161087</td>\n",
       "      <td>355956</td>\n",
       "      <td>404698</td>\n",
       "      <td>78560</td>\n",
       "      <td>4057600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_10_bin4</th>\n",
       "      <td>8010</td>\n",
       "      <td>18123</td>\n",
       "      <td>19589</td>\n",
       "      <td>3829</td>\n",
       "      <td>195791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin1</th>\n",
       "      <td>363214</td>\n",
       "      <td>758845</td>\n",
       "      <td>900043</td>\n",
       "      <td>176714</td>\n",
       "      <td>8952905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin2</th>\n",
       "      <td>173629</td>\n",
       "      <td>377209</td>\n",
       "      <td>424662</td>\n",
       "      <td>84837</td>\n",
       "      <td>4283540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin3</th>\n",
       "      <td>21292</td>\n",
       "      <td>48469</td>\n",
       "      <td>52690</td>\n",
       "      <td>11038</td>\n",
       "      <td>540308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_11_bin4</th>\n",
       "      <td>20071</td>\n",
       "      <td>33682</td>\n",
       "      <td>50307</td>\n",
       "      <td>10913</td>\n",
       "      <td>500314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin1</th>\n",
       "      <td>533751</td>\n",
       "      <td>1127904</td>\n",
       "      <td>1321534</td>\n",
       "      <td>264638</td>\n",
       "      <td>13129157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin2</th>\n",
       "      <td>148520</td>\n",
       "      <td>343551</td>\n",
       "      <td>380409</td>\n",
       "      <td>80647</td>\n",
       "      <td>3905483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin3</th>\n",
       "      <td>76583</td>\n",
       "      <td>163253</td>\n",
       "      <td>189811</td>\n",
       "      <td>34082</td>\n",
       "      <td>1933368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_12_bin4</th>\n",
       "      <td>24482</td>\n",
       "      <td>57799</td>\n",
       "      <td>61567</td>\n",
       "      <td>12824</td>\n",
       "      <td>625171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin1</th>\n",
       "      <td>571104</td>\n",
       "      <td>1233749</td>\n",
       "      <td>1400493</td>\n",
       "      <td>287259</td>\n",
       "      <td>14188024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin2</th>\n",
       "      <td>82567</td>\n",
       "      <td>182114</td>\n",
       "      <td>205057</td>\n",
       "      <td>42633</td>\n",
       "      <td>2056086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin3</th>\n",
       "      <td>60881</td>\n",
       "      <td>142728</td>\n",
       "      <td>147827</td>\n",
       "      <td>33482</td>\n",
       "      <td>1451645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_13_bin4</th>\n",
       "      <td>20805</td>\n",
       "      <td>48992</td>\n",
       "      <td>49723</td>\n",
       "      <td>20826</td>\n",
       "      <td>500419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin1</th>\n",
       "      <td>403418</td>\n",
       "      <td>882397</td>\n",
       "      <td>1028022</td>\n",
       "      <td>198432</td>\n",
       "      <td>10171556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin2</th>\n",
       "      <td>45821</td>\n",
       "      <td>99094</td>\n",
       "      <td>115065</td>\n",
       "      <td>25713</td>\n",
       "      <td>1132205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin3</th>\n",
       "      <td>53711</td>\n",
       "      <td>145555</td>\n",
       "      <td>139244</td>\n",
       "      <td>30359</td>\n",
       "      <td>1345508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_14_bin4</th>\n",
       "      <td>5516</td>\n",
       "      <td>7323</td>\n",
       "      <td>12877</td>\n",
       "      <td>9214</td>\n",
       "      <td>133934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin1</th>\n",
       "      <td>523616</td>\n",
       "      <td>1142280</td>\n",
       "      <td>1305346</td>\n",
       "      <td>266661</td>\n",
       "      <td>13030442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin2</th>\n",
       "      <td>42631</td>\n",
       "      <td>94072</td>\n",
       "      <td>106319</td>\n",
       "      <td>26090</td>\n",
       "      <td>1052382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin3</th>\n",
       "      <td>43673</td>\n",
       "      <td>96550</td>\n",
       "      <td>112206</td>\n",
       "      <td>27036</td>\n",
       "      <td>1132154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_15_bin4</th>\n",
       "      <td>10657</td>\n",
       "      <td>41516</td>\n",
       "      <td>26234</td>\n",
       "      <td>9841</td>\n",
       "      <td>254097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin1</th>\n",
       "      <td>551301</td>\n",
       "      <td>1197974</td>\n",
       "      <td>1345310</td>\n",
       "      <td>268847</td>\n",
       "      <td>13702604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin2</th>\n",
       "      <td>40816</td>\n",
       "      <td>90957</td>\n",
       "      <td>102926</td>\n",
       "      <td>21442</td>\n",
       "      <td>1025637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin3</th>\n",
       "      <td>83403</td>\n",
       "      <td>176836</td>\n",
       "      <td>206767</td>\n",
       "      <td>35261</td>\n",
       "      <td>2047753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TiteSeq_16_bin4</th>\n",
       "      <td>22230</td>\n",
       "      <td>55647</td>\n",
       "      <td>54124</td>\n",
       "      <td>21181</td>\n",
       "      <td>540961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(fates\n",
    "             .pivot_table(columns='fate',\n",
    "                          values='count',\n",
    "                          index=['library', 'sample'])\n",
    "             .to_html()\n",
    "             ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we plot the barcode-read fates for each library / sample, showing the bars for valid barcodes in orange and the others in gray.\n",
    "We see that the largest fraction of barcode reads correspond to valid barcodes, and most of the others are invalid barcodes (probably because the map to variants that aren't present in our variant table since we didn't associate all variants with barcodes). The exception to this is lib2 Titeseq_03_bin3; the PCR for this sample in the original sequencing run failed, so we followed it up with a single MiSeq lane. We did not filter out the PhiX reads from this data before parsing, so these PhiX reads will deflate the fraction of valid barcode reads as expected, but does not indicate any problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420x11764 with 136 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "barcode_fate_plot = (\n",
    "    ggplot(\n",
    "        fates\n",
    "        .assign(sample=lambda x: pd.Categorical(x['sample'],\n",
    "                                                x['sample'].unique(),\n",
    "                                                ordered=True),\n",
    "                fate=lambda x: pd.Categorical(x['fate'],\n",
    "                                              x['fate'].unique(),\n",
    "                                              ordered=True),\n",
    "                is_valid=lambda x: x['fate'] == 'valid barcode'\n",
    "                ), \n",
    "        aes('fate', 'count', fill='is_valid')) +\n",
    "    geom_bar(stat='identity') +\n",
    "    facet_grid('sample ~ library') +\n",
    "    facet_grid('sample ~ library') +\n",
    "    scale_fill_manual(CBPALETTE, guide=False) +\n",
    "    theme(figure_size=(1.4 * (1 + fates['library'].nunique()),\n",
    "                       1.7 * (1.2 + fates['sample'].nunique())),\n",
    "          axis_text_x=element_text(angle=90),\n",
    "          panel_grid_major_x=element_blank()\n",
    "          ) +\n",
    "    scale_y_continuous(labels=dms_variants.utils.latex_sci_not,\n",
    "                       name='number of reads')\n",
    "    )\n",
    "\n",
    "_ = barcode_fate_plot.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add barcode counts to variant table\n",
    "Now we use the [CodonVariantTable.add_sample_counts_df](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable.add_sample_counts_df) method to add the barcode counts to the variant table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "variants.add_sample_counts_df(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variant table now has a `variant_count_df` attribute that gives a data frame of all the variant counts.\n",
    "Here are the first few lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "      <th>barcode</th>\n",
       "      <th>count</th>\n",
       "      <th>variant_call_support</th>\n",
       "      <th>codon_substitutions</th>\n",
       "      <th>aa_substitutions</th>\n",
       "      <th>n_codon_substitutions</th>\n",
       "      <th>n_aa_substitutions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>ATTTGATGTCCTCATA</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>AAGAGCGAATAACATC</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>CTACCAAGGATGCTAT</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>CATGAAACGCCAACTT</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>BM48-31</td>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "      <td>ATATACTGAAGTCCTA</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>BM48-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(variants.variant_count_df.head().to_html(index=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confirm that we have counts for all of our library / sample combinations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.testing.assert_frame_equal(\n",
    "    barcode_runs[['library', 'sample']]\n",
    "                .sort_values(['library', 'sample'])\n",
    "                .reset_index(drop=True),\n",
    "    variants.variant_count_df\n",
    "        [['library', 'sample']]\n",
    "        .drop_duplicates()\n",
    "        .sort_values(['library', 'sample'])\n",
    "        .reset_index(drop=True),\n",
    "    check_dtype=False,\n",
    "    check_categorical=False,\n",
    "    check_like=True,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>lib1</td>\n",
       "      <td>SortSeq_bin4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lib1</td>\n",
       "      <td>TiteSeq_01_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_15_bin4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>lib2</td>\n",
       "      <td>TiteSeq_16_bin4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    library           sample\n",
       "0      lib1     SortSeq_bin1\n",
       "1      lib1     SortSeq_bin2\n",
       "2      lib1     SortSeq_bin3\n",
       "3      lib1     SortSeq_bin4\n",
       "8      lib1  TiteSeq_01_bin1\n",
       "..      ...              ...\n",
       "131    lib2  TiteSeq_15_bin4\n",
       "132    lib2  TiteSeq_16_bin1\n",
       "133    lib2  TiteSeq_16_bin2\n",
       "134    lib2  TiteSeq_16_bin3\n",
       "135    lib2  TiteSeq_16_bin4\n",
       "\n",
       "[136 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barcode_runs[['library', 'sample']].sort_values(['library', 'sample'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>library</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [library, sample]\n",
       "Index: []"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((variants.variant_count_df\n",
    "        [['library', 'sample']]\n",
    "        .drop_duplicates()\n",
    "        .sort_values(['library', 'sample'])\n",
    "        .reset_index(drop=True)) == barcode_runs[['library', 'sample']].sort_values(['library', 'sample']).reset_index(drop=True)).query('sample == False')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the variant counts data frame to a CSV file.\n",
    "It can then be used to re-initialize a [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) via its [from_variant_count_df](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable.from_variant_count_df) method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing variant counts to results/counts/variant_counts.csv\n"
     ]
    }
   ],
   "source": [
    "print(f\"Writing variant counts to {config['variant_counts_file']}\")\n",
    "variants.variant_count_df.to_csv(config['variant_counts_file'], index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [CodonVariantTable](https://jbloomlab.github.io/dms_variants/dms_variants.codonvarianttable.html#dms_variants.codonvarianttable.CodonVariantTable) has lots of nice functions that can be used to analyze the counts it contains.\n",
    "However, we do that in the next notebook so we don't have to re-run this entire (rather computationally intensive) notebook every time we want to analyze a new aspect of the counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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