Popularity and performance of bioinformatics software: the case of gene set analysis.
Popularity and performance of bioinformatics software: the case of gene set analysis.
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DOI：
10.1186/s12859-021-04124-5
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发表时间：
2021-04-15
影响因子：
3
通讯作者：
Mora A
中科院分区：
文献类型：
--
作者：
Xie C;Jauhari S;Mora A
基因集分析（GSA）可以说是选择的方法的功能解释的组学结果。下面的文件探讨了流行和性能的所有GSA方法和软件发布在20年以来，它的成立。“受欢迎程度”是根据每篇论文的引用次数来估计的，而“绩效”则是基于对该领域论文所使用的验证策略的综合评估，以及现有基准研究的综合结果。关于流行度，数据被收集到一个在线开放数据库（“GSARefDB”）中，该数据库允许从503篇GSA论文参考文献中浏览书目和方法描述信息;关于性能，我们引入了一个jupyter工作流程和闪亮应用程序的存储库，用于GSA方法的自动基准测试（“GSA-BenchmarKING”）。在比较流行与性能之后，结果显示最流行和性能最好的GSA方法之间的差异。上述结果引起了我们对研究人员所遵循的工具选择程序的性质的关注，并对当前生物医学研究中生物数据集的功能解释的质量提出了质疑。未来的功能解释领域的建议，包括战略的教育和讨论GSA工具，更好的验证和基准测试的做法，再现性，和功能重新分析以前报告的数据。在线版本包含补充材料，可通过10.1186/s12859-021-04124-5获得。
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. 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. 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. The online version contains supplementary material available at 10.1186/s12859-021-04124-5.
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影响因子：
14.9
作者：
Kuleshov MV;Jones MR;Rouillard AD;Fernandez NF;Duan Q;Wang Z;Koplev S;Jenkins SL;Jagodnik KM;Lachmann A;McDermott MG;Monteiro CD;Gundersen GW;Ma'ayan A
通讯作者：
Ma'ayan A
影响因子：
3
作者：
Ma, Jing;Shojaie, Ali;Michailidis, George
通讯作者：
Michailidis, George
影响因子：
--
作者：
Alhamdoosh M;Law CW;Tian L;Sheridan JM;Ng M;Ritchie ME
通讯作者：
Ritchie ME
影响因子：
9.5
作者：
Osz, Agnes;Pongor, Lorinc Sandor;Gyorffy, Balazs
通讯作者：
Gyorffy, Balazs
影响因子：
9.5
作者：
Jaakkola MK;Elo LL
通讯作者：
Elo LL