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‎README.md

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# xPore
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![alt text](https://github.com/GoekeLab/xpore/blob/master/figures/xpore_textlogo.png "xPore")
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See [our documentation](https://xpore.readthedocs.io)!
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### Installation
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xpore requires [Python3](https://www.python.org) to run.
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To install our xpore package and its dependencies, run
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```sh
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$ pip install xpore
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$ pyensembl install --release 91 --species homo_sapiens
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```
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### Detection of differential modification
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Download the demo dataset from our [S3 bucket](http://s3.ap-southeast-1.amazonaws.com/all-public-data.store.genome.sg/xpore/demo.tar.gz).
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```sh
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$ wget http://s3.ap-southeast-1.amazonaws.com/all-public-data.store.genome.sg/xpore/demo.tar.gz
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```
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After extraction, you will find
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```
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demo
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|-- diffmod.ini
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|-- data
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|-- HEK293T-METTL3-KO-rep1 # dataset dir
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|-- HEK293T-WT-rep1 # dataset dir
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|-- db
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|-- model_kmer.csv
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|-- Homo_sapiens.GRCh38.cdna.mmi
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```
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Each dataset under the `data` directory contains the following directories
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* `fast5`: Raw signal FAST5 files
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* `fastq`: Basecalled reads
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* `bamtx`: Transcriptome-aligned sequence
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* `nanopolish`: Eventalign files obtained from [nanopolish eventalign](https://nanopolish.readthedocs.io/en/latest/quickstart_eventalign.html)
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```
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1. Preprocess the data for each data set using `xpore-dataprep`.
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```sh
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# Within each dataset directory i.e. demo/data/HEK293T-METTL3-KO-rep1 and demo/data/HEK293T-WT-rep1, run
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$xpore-dataprep --eventalign nanopolish/eventalign.txt \
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--summary nanopolish/summary.txt \
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--read_count_min 10 \
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--read_count_max 500 \
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--out_dir dataprep \
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--genome \
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--n_processes 2
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```
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The output files are stored under `dataprep` in each dataset directory:
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* `eventalign.hdf5`: Merged segments from `nanopolish eventalign`, stored with the hierarchical keys `<TRANSCRIPT_ID>/<READ_ID>/events`.
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* `eventalign.log`: Log file
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* `data.json`: Preprocessed data for xpore-diffmod
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* `data.index`: File index of data.json for random access per gene
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* `data.readcount`: Summary of readcounts per gene
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* `data.log`: Log file
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Run `xpore-dataprep -h` to explore the full usage.
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2. Now that we have the data ready for estimating differential modification using `xpore-diffmod`. To call the differential modification between HEK293T-METTL3-KO and HEK293T-WT, we can use the example confgiuration file `diffmod.yaml`.
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```sh
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# At the demo directory where the configuration file is, run
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$ xpore-diffmod --config_filepath ./tests/config/diffmod.yaml --n_processes 2 --save_table
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```
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The output files are generated within the `demo/out/diffmod.yaml` directory:
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* `out/diffmod.yaml/diffmod.table` : Result table of differential RNA modification across all tested positions
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* `out/diffmod.ini/diffmod.log` : Log file
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Run `xpore-diffmod -h` to explore the full usage.
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We can rank the significanly differentially modified sites based on `p_w_mod_HEK293T-KO_vs_HEK293T-WT`. The results are shown below.
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```
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idx position kmer coverage_HEK293T-METTL3-KO-rep1 coverage_HEK293T-WT-rep1 mu_unmod mu_mod sigma2_unmod sigma2_mod conf_mu_unmod conf_mu_mod mod_assignment w_mod_HEK293T-METTL3-KO-rep1 w_mod_HEK293T-WT-rep1 p_w_mod_HEK293T-KO_vs_HEK293T-WT w_mod_mean_diff_HEK293T-KO_vs_HEK293T-WT z_score_HEK293T-KO_vs_HEK293T-WT
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ENSG00000114125 141745412 GGACT 167.00000000000009 77.99999999999997 123.64198305264463 117.62845573389104 5.925237677872507 18.048686652338954 0.9686894976263544 0.19542869203353666 lower 0.122081280515318 0.9453989811254184 4.241373321581284e-115 -0.8233177006101003 -22.803411286539568
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ENSG00000159111 47824212 GGACT 115.0 56.99999999999999 126.04060818513784 120.32286061375729 2.6865489759165357 13.820088773078876 0.6444364495129247 0.4640590683780786 lower 0.12675220252612124 0.9547753654686716 1.1037896604310229e-88 -0.8280231629425505 -19.965292828395782
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ENSG00000159111 47824138 GGGAC 105.0 55.0 118.8431338231134 115.38819698904041 3.965195468986447 9.877299131873366 0.8614802593826912 0.35998415978405274 lower 0.2420911154423771 0.9999818188429512 1.8981606007746968e-73 -0.7578907034005742 -18.128515052229204
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ENSG00000159111 47824213 CGGAC 111.0 58.0 120.0023181524575 125.19517965940052 16.09490223670403 2.517386156153043 0.7770385571640749 0.1754346779458279 higher 0.6714153939678753 1.7240784800524122e-05 3.0229603394241693e-51 0.6713981531830748 15.058784020930725
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ENSG00000114125 141745411 GGGAC 166.00000000000003 85.00000000000003 117.03987287411272 120.2784827935068 8.177643930183974 2.8216439842252683 0.6933138912876065 0.5304746373270921 higher 0.7056088802507199 0.12806065000998446 4.010247723322406e-30 0.5775482302407354 11.403633554535956
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```
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### License
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MIT
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‎_README.md

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# xPore
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See [our documentation](https://xpore.readthedocs.io)!
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### Installation
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xpore requires [Python3](https://www.python.org) to run.
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To install our xpore package and its dependencies, run
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```sh
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$ pip install xpore
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$ pyensembl install --release 91 --species homo_sapiens
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```
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### Detection of differential modification
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Download the demo dataset from our [S3 bucket](http://s3.ap-southeast-1.amazonaws.com/all-public-data.store.genome.sg/xpore/demo.tar.gz).
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```sh
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$ wget http://s3.ap-southeast-1.amazonaws.com/all-public-data.store.genome.sg/xpore/demo.tar.gz
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```
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After extraction, you will find
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```
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demo
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|-- diffmod.ini
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|-- data
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|-- HEK293T-METTL3-KO-rep1 # dataset dir
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|-- HEK293T-WT-rep1 # dataset dir
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|-- db
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|-- model_kmer.csv
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|-- Homo_sapiens.GRCh38.cdna.mmi
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```
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Each dataset under the `data` directory contains the following directories
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* `fast5`: Raw signal FAST5 files
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* `fastq`: Basecalled reads
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* `bamtx`: Transcriptome-aligned sequence
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* `nanopolish`: Eventalign files obtained from [nanopolish eventalign](https://nanopolish.readthedocs.io/en/latest/quickstart_eventalign.html)
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```
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1. Preprocess the data for each data set using `xpore-dataprep`.
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```sh
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# Within each dataset directory i.e. demo/data/HEK293T-METTL3-KO-rep1 and demo/data/HEK293T-WT-rep1, run
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$xpore-dataprep --eventalign nanopolish/eventalign.txt \
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--summary nanopolish/summary.txt \
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--read_count_min 10 \
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--read_count_max 500 \
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--out_dir dataprep \
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--genome \
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--n_processes 2
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```
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The output files are stored under `dataprep` in each dataset directory:
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* `eventalign.hdf5`: Merged segments from `nanopolish eventalign`, stored with the hierarchical keys `<TRANSCRIPT_ID>/<READ_ID>/events`.
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* `eventalign.log`: Log file
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* `data.json`: Preprocessed data for xpore-diffmod
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* `data.index`: File index of data.json for random access per gene
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* `data.readcount`: Summary of readcounts per gene
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* `data.log`: Log file
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Run `xpore-dataprep -h` to explore the full usage.
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2. Now that we have the data ready for estimating differential modification using `xpore-diffmod`. To call the differential modification between HEK293T-METTL3-KO and HEK293T-WT, we can use the example confgiuration file `diffmod.yaml`.
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```sh
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# At the demo directory where the configuration file is, run
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$ xpore-diffmod --config_filepath ./tests/config/diffmod.yaml --n_processes 2 --save_table
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```
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The output files are generated within the `demo/out/diffmod.yaml` directory:
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* `out/diffmod.yaml/diffmod.table` : Result table of differential RNA modification across all tested positions
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* `out/diffmod.ini/diffmod.log` : Log file
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Run `xpore-diffmod -h` to explore the full usage.
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We can rank the significanly differentially modified sites based on `p_w_mod_HEK293T-KO_vs_HEK293T-WT`. The results are shown below.
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```
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idx position kmer coverage_HEK293T-METTL3-KO-rep1 coverage_HEK293T-WT-rep1 mu_unmod mu_mod sigma2_unmod sigma2_mod conf_mu_unmod conf_mu_mod mod_assignment w_mod_HEK293T-METTL3-KO-rep1 w_mod_HEK293T-WT-rep1 p_w_mod_HEK293T-KO_vs_HEK293T-WT w_mod_mean_diff_HEK293T-KO_vs_HEK293T-WT z_score_HEK293T-KO_vs_HEK293T-WT
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ENSG00000114125 141745412 GGACT 167.00000000000009 77.99999999999997 123.64198305264463 117.62845573389104 5.925237677872507 18.048686652338954 0.9686894976263544 0.19542869203353666 lower 0.122081280515318 0.9453989811254184 4.241373321581284e-115 -0.8233177006101003 -22.803411286539568
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ENSG00000159111 47824212 GGACT 115.0 56.99999999999999 126.04060818513784 120.32286061375729 2.6865489759165357 13.820088773078876 0.6444364495129247 0.4640590683780786 lower 0.12675220252612124 0.9547753654686716 1.1037896604310229e-88 -0.8280231629425505 -19.965292828395782
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ENSG00000159111 47824138 GGGAC 105.0 55.0 118.8431338231134 115.38819698904041 3.965195468986447 9.877299131873366 0.8614802593826912 0.35998415978405274 lower 0.2420911154423771 0.9999818188429512 1.8981606007746968e-73 -0.7578907034005742 -18.128515052229204
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ENSG00000159111 47824213 CGGAC 111.0 58.0 120.0023181524575 125.19517965940052 16.09490223670403 2.517386156153043 0.7770385571640749 0.1754346779458279 higher 0.6714153939678753 1.7240784800524122e-05 3.0229603394241693e-51 0.6713981531830748 15.058784020930725
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ENSG00000114125 141745411 GGGAC 166.00000000000003 85.00000000000003 117.03987287411272 120.2784827935068 8.177643930183974 2.8216439842252683 0.6933138912876065 0.5304746373270921 higher 0.7056088802507199 0.12806065000998446 4.010247723322406e-30 0.5775482302407354 11.403633554535956
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```
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### License
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MIT
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‎figures/xpore_logo.png

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‎figures/xpore_textlogo.png

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