A simple python utility to compare short tandem repeat (STR) profiles.
Project description
strprofiler
strprofiler is a simple python utility 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.
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:
$$ score = \frac{2 * no.shared.alleles}{no.query.alleles + no.reference.alleles} $$
$$ score = \frac{no.shared.alleles}{no.query.alleles} $$
$$ score = \frac{no.shared.alleles}{no.reference.alleles} $$
Amelogenin is not included in the score computation by default but can be included by passing the --score_amel
flag.
Installation
strprofiler is available on PyPI and can be installed with pip:
pip install strprofiler
Usage
strprofiler can be run directly from the command line.
strprofiler -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 [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 TEXT Name of Amelogenin column in STR file(s). [default: AMEL] │
│ --sample_col -scol TEXT Name of sample column in STR file(s). [default: Sample] │
│ --marker_col -mcol TEXT Name of marker column in STR file(s). Only used if format is 'wide'. [default: Marker] │
│ --penta_fix -pfix Whether to try to harmonize PentaE/D allele spelling. [default: True] │
│ --score_amel -amel 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. │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Input Files(s)
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.
Output Files
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. |
Database Comparison
strprofiler can be also used to compare batches of samples against a larger database of samples.
strprofiler -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).
Contributing
You can contribute by creating issues to highlight bugs and make suggestions for additional features. Pull requests are also very welcome.
License
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 I retain no liability for its use. Read the full license for additional details.
Reference
If you use strprofiler in your research, please cite the DOI:
Jared Andrews, & Sam Culley. (2022). j-andrews7/strprofiler: v0.1.1 (v0.1.1). Zenodo. https://doi.org/10.5281/zenodo.7348387
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