strprofiler 0.3.0

A python package, CLI tool, and Shiny application to compare short tandem repeat (STR) profiles.

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:

$$ score = \frac{2 * no.shared.alleles}{no.query.alleles + no.reference.alleles} $$

Masters (vs. query):

$$ score = \frac{no.shared.alleles}{no.query.alleles} $$

Masters (vs. reference):

$$ score = \frac{no.shared.alleles}{no.reference.alleles} $$

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    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.

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 clastr --help.

 Usage: clastr [OPTIONS] INPUT_FILES...   

**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.                              │
╰──────────────────────────────────────────────────────────────────────────────────────────╯

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.

clastr

Output for clastr is provided in XLSX format. Results follow the CLASTR format, documented here: https://www.cellosaurus.org/str-search/help.html#4

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).

Database Format

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.

The STRprofiler App

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.

Deploying an strprofiler App

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>=0.3.0

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.

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 the authors retain no liability for its use. Read the full license for additional details.

References

If you use STRprofiler in your research, please cite the DOI:

Jared Andrews, Mike Lloyd, & Sam Culley. (2024). j-andrews7/strprofiler: v0.3.0 (v0.3.0). Zenodo. https://doi.org/10.5281/zenodo.7348386

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