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
{"title":"STRprofiler：用于生物医学模型认证的短串联重复谱的高效比较。","authors":"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","doi":"10.1093/bioinformatics/btae713","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>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.</p><p><strong>Availability and implementation: </strong>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/.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"STRprofiler: efficient comparisons of short tandem repeat profiles for biomedical model authentication.\",\"authors\":\"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\",\"doi\":\"10.1093/bioinformatics/btae713\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Summary: </strong>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.</p><p><strong>Availability and implementation: </strong>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/.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>\",\"PeriodicalId\":93899,\"journal\":{\"name\":\"Bioinformatics (Oxford, England)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics (Oxford, England)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btae713\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btae713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
STRprofiler: efficient comparisons of short tandem repeat profiles for biomedical model authentication.
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/.
Supplementary information: Supplementary data are available at Bioinformatics online.
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