PubMed:33858350 JSONTXT

{"project":"JournalClub","denotations":[{"id":"T1","span":{"begin":98,"end":115},"obj":"Method"},{"id":"T2","span":{"begin":117,"end":120},"obj":"Method"},{"id":"T3","span":{"begin":282,"end":285},"obj":"Method"},{"id":"T4","span":{"begin":794,"end":797},"obj":"Method"},{"id":"T5","span":{"begin":854,"end":942},"obj":"Database"},{"id":"T6","span":{"begin":945,"end":961},"obj":"Database"},{"id":"T7","span":{"begin":1088,"end":1091},"obj":"Method"},{"id":"T8","span":{"begin":1492,"end":1495},"obj":"Method"},{"id":"T9","span":{"begin":703,"end":711},"obj":"Database"},{"id":"T10","span":{"begin":790,"end":793},"obj":"Number"}],"attributes":[{"id":"A3","pred":"tao:has_database_id","subj":"T5","obj":"https://gsa-central.github.io/benchmarKING.html"},{"id":"A2","pred":"tao:has_database_id","subj":"T6","obj":"https://gsa-central.github.io/benchmarKING.html"},{"id":"A1","pred":"tao:has_database_id","subj":"T9","obj":"https://gsa-central.github.io/gsarefdb.html"}],"text":"Popularity and performance of bioinformatics software: the case of gene set analysis.\nBACKGROUND: Gene Set Analysis (GSA) is arguably the method of choice for the functional interpretation of omics results. The following paper explores the popularity and the performance of all the GSA methodologies and software published during the 20 years since its inception. \"Popularity\" is estimated according to each paper's citation counts, while \"performance\" is based on a comprehensive evaluation of the validation strategies used by papers in the field, as well as the consolidated results from the existing benchmark studies.\nRESULTS: Regarding popularity, data is collected into an online open database (\"GSARefDB\") which allows browsing bibliographic and method-descriptive information from 503 GSA paper references; regarding performance, we introduce a repository of jupyter workflows and shiny apps for automated benchmarking of GSA methods (\"GSA-BenchmarKING\"). After comparing popularity versus performance, results show discrepancies between the most popular and the best performing GSA methods.\nCONCLUSIONS: The above-mentioned results call our attention towards the nature of the tool selection procedures followed by researchers and raise doubts regarding the quality of the functional interpretation of biological datasets in current biomedical studies. Suggestions for the future of the functional interpretation field are made, including strategies for education and discussion of GSA tools, better validation and benchmarking practices, reproducibility, and functional re-analysis of previously reported data."}