STRprofiler is a python package, CLI tool, and Shiny application to compare short tandem repeat (STR) profiles. In particular, it is designed to aid research labs in comparing models (e.g. cell lines & xenografts) generated from primary tissue samples to ensure contamination has not occurred. It includes basic checks for sample mixing and contamination and provides a simple interface to conveniently query the Cellosaurus database via the CLASTR API.
STRprofiler is intended only for research purposes.
For each STR profile provided, STRprofiler will generate a sample-specific report that includes the following similarity scores as compared to every other profile:
Tanabe, AKA the Sørenson-Dice coefficient:
Amelogenin is not included in the score computation by default but can be included by passing the --score_amel flag.
STRprofiler is available on PyPI and can be installed with pip:
pip install strprofilerSTRprofiler can be run directly from the command line.
strprofiler compare -sm "SampleMap_exp.csv" -scol "Sample Name" -o ./strprofiler_output STR1.xlsx STR2.csv STR3.txt
Full usage information can be found by running strprofiler --help.
Usage: strprofiler compare [OPTIONS] INPUT_FILES...
STRprofiler compares STR profiles to each other.
╭─ Options ────────────────────────────────────────────────────────────────────────────────╮
│ --tan_threshold -tanth FLOAT Minimum Tanabe score to report as potential matches |
| in summary table. [default: 80] │
│ --mas_q_threshold -masqth FLOAT Minimum Masters (vs. query) score to report as |
| potential matches in summary table. [default: 80] │
│ --mas_r_threshold -masrth FLOAT Minimum Masters (vs. reference) score to report as |
| potential matches in summary table. [default: 80] │
│ --mix_threshold -mix INTEGER Number of markers with >= 2 alleles allowed before |
| a sample is flagged for potential mixing. |
| [default: 3] │
│ --sample_map -sm PATH Path to sample map in csv format for renaming. |
| First column should be sample names as given in |
| STR file(s), second should be new names to assign. |
| No header. │
│ --database -db PATH Path to an STR database file in csv, xlsx, tsv, |
| or txt format. │
│ --amel_col -acol STR Name of Amelogenin column in STR file(s). |
| [default: 'AMEL'] │
│ --sample_col -scol STR Name of sample column in STR file(s). |
| [default: 'Sample'] │
│ --marker_col -mcol STR Name of marker column in STR file(s). |
| Only used if format is 'wide'. [default: 'Marker'] │
│ --penta_fix -pfix FLAG Whether to try to harmonize PentaE/D allele |
| spelling. [default: True] │
│ --score_amel -amel FLAG Use Amelogenin for similarity scoring. |
| [default: False] │
│ --output_dir -o PATH Path to the output directory. |
| [default: ./STRprofiler] │
│ --version Show the version and exit. │
│ --help Show this message and exit │
╰──────────────────────────────────────────────────────────────────────────────────────────╯CLASTR
Additionally, the Cellosaurus (Bairoch, 2018) cell line database can be queried via the CLASTR (Robin, Capes-Davis, and Bairoch, 2019) REST API.
strprofiler clastr -sm "SampleMap_exp.csv" -scol "Sample Name" -o ./strprofiler_output STR1.xlsx STR2.csv STR3.txt
Full usage information can be found by running strprofiler clastr --help.
Usage: strprofiler clastr [OPTIONS] INPUT_FILES...
**strprofiler clastr** compares STR profiles to the human Cellosaurus knowledge base using the CLASTR REST API.
╭─ Options ────────────────────────────────────────────────────────────────────────────────╮
│ --search_algorithm -sa INT Search algorithm to use in the CLASTR query. |
| 1 - Tanabe, 2 - Masters (vs. query); |
| 3 - Masters (vs. reference) [default: 1] │
│ --scoring_mode -sm INT Search mode to account for missing alleles in query or |
| reference. 1 - Non-empty markers, 2 - Query markers, |
| 3 - Reference markers. [default: 1] │
│ --score_filter -sf INT Minimum score to report as potential matches in |
| summary table. [default: 80] │
│ --max_results -mr INT Filter defining the maximum number of results to be |
| returned. [default: 200] │
│ --min_markers -mm INT Filter defining the minimum number of markers for |
| matches to be reported. [default: 8] │
│ --sample_col -scol STR Name of sample column in STR file(s). |
| [default: 'Sample'] │
│ --marker_col -mcol STR Name of marker column in STR file(s). |
| Only used if format is 'wide'. [default: 'Marker'] │
│ --penta_fix -pfix FLAG Whether to try to harmonize PentaE/D allele spelling. |
| [default: True] │
│ --score_amel -amel FLAG Use Amelogenin for similarity scoring. [default: False] │
│ --output_dir -o PATH Path to the output directory. [default: ./STRprofiler] │
│ --version Show the version and exit. │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────╯
STRprofiler can take either a single STR file or multiple STR files as input. These files can be csv, tsv, tab-separated text, or xlsx (first sheet used) files. The STR file(s) should be in either 'wide' or 'long' format. The long format expects all columns to map to the markers except for the designated sample name column with each row reflecting a different profile, e.g.:
| Sample | D1S1656 | DYS391 | D3S1358 | D2S441 | D16S539 | D5S818 | D8S1179 | D22S1045 | D18S51 |
|---|---|---|---|---|---|---|---|---|---|
| Line1 | 12,14 | 12 | 13 | 12,14 | 17.3 | 16,17 | 18.3 | 17,11 | |
| Line2 | 12,14 | 11.3,12 | 13,15 | 12,14 | 17.3 | 16,17 | 18.3 | 17,11 | |
| ... |
The wide format expects a line for each marker for each sample, e.g.:
| Sample Name | Marker | Allele 1 | Size 1 | Height 1 | Allele 2 | Size 2 | Height 2 | Allele 3 |
|---|---|---|---|---|---|---|---|---|
| Sample1 | DYS391 | |||||||
| Sample1 | D3S1358 | 16 | 128.29 | 8268 | 18 | 136.84 | 5467 | 16 |
| Sample1 | D16S539 | 12 | 110.7 | 9660 | 13 | 115.17 | 5215 | |
| Sample1 | Penta D | 9 | 415.04 | 5099 | 13 | 435.88 | 9426 | |
| Sample1 | D22S1045 | 15 | 455.95 | 13504 | 17 | 462.06 | 6186 | |
| Sample1 | Penta E | 11 | 397.7 | 7420 | 14 | 412.02 | 5986 | |
| Sample1 | D18S51 | 12 | 153.72 | 9134 | 16 | 170.48 | 10501 | |
| Sample1 | D2S1338 | 20 | 263.91 | 3209 | 21 | 267.97 | 3834 | |
| Sample1 | TH01 | 7 | 85.33 | 8305 | 9.3 | 97.43 | 7853 | |
| Sample1 | D7S820 | 10 | 292.51 | 12340 | 14 | 308.71 | 11784 | |
| Sample1 | D12S391 | 15 | 141.53 | 12870 | 18.3 | 157.12 | 13731 | |
| Sample1 | AMEL | X | 81.97 | 16696 | ||||
| Sample1 | D10S1248 | 16 | 283.82 | 8469 | ||||
| Sample1 | D13S317 | 12 | 328.21 | 7079 | ||||
| Sample1 | D21S11 | 32.2 | 239.67 | 19231 | ||||
| Sample1 | TPOX | 11 | 424.02 | 12239 | ||||
| Sample1 | D19S433 | 14 | 228.37 | 14273 | ||||
| Sample1 | FGA | 23 | 302.23 | 14599 | ||||
| Sample2 | D16S539 | 9 | 97.59 | 9286 | 11 | 106.43 | 8592 | |
| Sample2 | TH01 | 9.3 | 97.45 | 5920 | ||||
| Sample2 | D8S1179 | 13 | 101.1 | 26414 | ||||
| Sample2 | AMEL | X | 82.1 | 7476 | Y | 88.34 | 8029 | |
| Sample2 | D3S1358 | 14 | 119.87 | 10146 | 15 | 124.14 | 10160 | 19 |
| Sample2 | D18S51 | 12 | 153.8 | 9316 | 18 | 178.79 | 9182 | 19 |
| Sample2 | Penta D | 10 | 420.13 | 7693 | 11 | 425.25 | 7945 | 12 |
| Sample2 | vWA | 17 | 156.9 | 7953 | 18 | 160.86 | 8230 | |
| Sample2 | TPOX | 9 | 416 | 6596 | 11 | 424.02 | 5304 | |
| Sample2 | D12S391 | 21 | 166.75 | 13481 | 22 | 170.9 | 14232 | |
| Sample2 | D22S1045 | 15 | 455.95 | 14310 | 17 | 462.06 | 10898 | |
| Sample2 | D2S441 | 14 | 236.24 | 18628 | ||||
| Sample2 | DYS391 | 10 | 468.83 | 6722 | ||||
| Sample2 | FGA | 21 | 294.67 | 11941 |
In this format, the marker_col must be specified. Only columns beginning with "Allele" will be used to parse the alleles for each sample/marker. Any other size or height columns will be ignored.
STRprofiler generates two types of output files. The first is a summary file, which contains the top hits for each sample above the specified scoring thresholds. This file provides a useful overview in addition to a flag to identify samples with potential mixing for closer inspection. In the output directory, this file will be named full_summary.strprofiler.YYYYMMDD.HH_MM_SS.csv where the date and time are the time the program was run.
In addition to the marker columns, the summary file contains the following columns:
| Column Name | Description |
|---|---|
| mixed | Flag to indicate sample mixing. |
| top_hit | Name and Tanabe score of top match to sample. |
| next_best | Name and Tanabe score of next best match to sample. |
| tanabe_matches | Name and Tanabe score of matches above scoring threshold to sample. |
| masters_query_matches | Name and Masters (vs. query) score of matches above scoring threshold to sample. |
| masters_ref_matches | Name and Masters (vs. reference) score of matches above scoring threshold to sample. |
The second is a sample-specific comparison file, which contains the results of the comparison between the query sample and all other provided samples. These files are generated for each STR profile provided in the input file(s) and named after the query sample in question. For example, if the input file contains a sample named Sample1, the output file will be named Sample1.strprofiler.YYYYMMDD.HH_MM_SS.csv.
In addition to the marker columns, this output contains the following columns:
| Column Name | Description |
|---|---|
| mixed | Flag to indicate sample mixing. |
| query_sample | Flag to indicate query sample. |
| n_shared_markers | Number of shared markers between query and reference sample. |
| n_shared_alleles | Number of shared alleles between query and reference sample. |
| n_query_alleles | Total number of alleles in query sample. |
| n_reference_alleles | Total number of alleles in reference sample. |
| tanabe_score | Tanabe similarity score. |
| masters_query_score | Masters (vs query) similarity score. |
| masters_ref_score | Masters (vs reference) similarity score. |
clastr
Output for strprofiler clastr is provided in XLSX format.
Results follow the CLASTR format, documented here: https://www.cellosaurus.org/str-search/help.html#4
STRprofiler can be also used to compare batches of samples against a larger database of samples.
strprofiler compare -db ExampleSTR_database.csv -o ./strprofiler_output STR1.xlsx
In this mode, inputs are compared against the database samples only, and not among themselves. Outputs will be as described above for sample input(s).
The database should be formatted as a samples by markers matrix and saved as a csv file, e.g:
| Sample | Amelogenin | CSF1PO | D13S317 | D16S539 | D18S51 | D19S433 | D21S11 | D2S1338 | D3S1358 | D5S818 | D7S820 | D8S1179 | FGA | TH01 | TPOX | vWA | PentaE | PentaD |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| sample1 | X,Y | 12 | 8 | 13 | 14 | 14 | 31,31.2 | 17,19 | 15 | 11,12 | 11,12 | 12,15 | 23 | 7,9.3 | 8 | 18 | ||
| sample2 | X | 10 | 9 | 13 | 16 | 12,14 | 29 | 20,23 | 15,16 | 12,13 | 9,12 | 14,15 | 18 | 7 | 8,9 | 15 |
Optionally, one may provide two metadata columns - "Center" and "Passage", which will be recognized as non-marker columns.
New in v0.2.0 is strprofiler app, a command that launches a Shiny application that allows for user queries against an uploaded or pre-defined database (provided with the -db parameter) of STR profiles.
This application can provide a convenient portal to a group's STR database and can be hosted on standard Shiny servers, Posit Connect instances, or ShinyApps.io.
An example of the application can be seen here.
Building an app for deployment to any of the above options is simple.
First, make your app.py file:
from strprofiler.shiny_app.shiny_app import create_app
database = "./tester_db.csv"
app = create_app(db=database)If no database is provided, an example database included with the package will be used.
The database file uses same format as for the standard strprofiler command.
Then create a requirements.txt file in the same directory with strprofiler listed:
strprofiler>=0.4.1
This app can then be deployed to any of the above endpoints as one would with any other Shiny app, e.g.:
rsconnect deploy shiny -n your_server -t STRprofiler .
Alternatively, one could export it as a shinylive app and host it on Github pages or similar.
You can contribute by creating issues to highlight bugs and make suggestions for additional features. Pull requests are also very welcome.
STRprofiler is released on the MIT license. You are free to use, modify, or redistribute it in almost any way, provided you state changes to the code, disclose the source, and use the same license. It is released with zero warranty for any purpose and the authors retain no liability for its use. Read the full license for additional details.
If you use STRprofiler in your research, please cite the following:
Jared M Andrews, Michael W Lloyd, Steven B Neuhauser, Margaret Bundy, Emily L Jocoy, Susan D Airhart, Carol J Bult, Yvonne A Evrard, Jeffrey H Chuang, Suzanne Baker, STRprofiler: efficient comparisons of short tandem repeat profiles for biomedical model authentication, Bioinformatics, 2024;, btae713, https://doi.org/10.1093/bioinformatics/btae713
If you use the clastr command or functionality from the Shiny application, please cite the Cellosaurus and CLASTR publications:
Bairoch A. (2018) The Cellosaurus, a cell line knowledge resource. Journal of Biomolecular Techniques. 29:25-38. DOI: 10.7171/jbt.18-2902-002; PMID: 29805321
Robin, T., Capes-Davis, A. & Bairoch, A. (2019) CLASTR: the Cellosaurus STR Similarity Search Tool - A Precious Help for Cell Line Authentication. International Journal of Cancer. PubMed: 31444973 DOI: 10.1002/IJC.32639