Document Type
Article
Publication Date
11-28-2024
Original Citation
Andrews J,
Lloyd M,
Neuhauser S,
Bundy M,
Jocoy E,
Airhart S,
Bult C,
Evrard Y,
Chuang J,
Baker S.
STRprofiler: efficient comparisons of short tandem repeat profiles for biomedical model authentication. Bioinformatics. 2024;40(12).
Keywords
JMG, JGM, JCA, Microsatellite Repeats, Software, Humans, Cell Line Authentication, Databases, Genetic
JAX Source
Bioinformatics. 2024;40(12).
ISSN
1367-4811
PMID
39589865
DOI
https://doi.org/10.1093/bioinformatics/btae713
Grant
This work was supported by the National Cancer Institute (NCI) of the National Institutes of Health under award num- bers P01CA096832 and U24CA224067. This project was also supported in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E.
Abstract
SUMMARY: Short tandem repeat (STR) profiling is commonly performed for authentication of biomedical models of human origin, yet no tools exist to easily compare sets of STR profiles to each other or an existing database in a high-throughput manner. Here, we present STRprofiler, a Python package, command line tool, and Shiny application providing methods for STR profile comparison and cross-contamination detection. STRprofiler can be run with custom databases or used to query against the Cellosaurus cell line database.
AVAILABILITY AND IMPLEMENTATION: STRprofiler is freely available as a Python package with a rich CLI from PyPI https://pypi.org/project/strprofiler/ with source code available under the MIT license on GitHub https://github.com/j-andrews7/strprofiler and at https://zenodo.org/records/10989034. A web server hosting an example STRprofiler Shiny application backed by a database with data from the National Cancer Institute-funded PDXNet consortium and The Jackson Laboratory PDX program is available at https://sj-bakerlab.shinyapps.io/strprofiler/. Full documentation is available at https://strprofiler.readthedocs.io/en/latest/.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.