Abstract
Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub “stars,” “watchers,” and “forks” (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.
Highlights
It is currently undeniable that bioinformatics tools and databases represent a highly impactful part of modern research (Wren, 2016)
Some of the most famous examples include application notes published in Bioinformatics, database, and web-server issues published by Nucleic Acids Research, software articles published in Frontiers Bioinformatics and Computational Biology, PLOS Computational Biology, BMC Bioinformatics
A journal’s impact factor, calculated as the average number of citations received in a calendar year by the total number of articles and reviews published in that journal in the preceding 2 years (JIF) is not a good predictor of software popularity (Seglen, 1997; Wren, 2016), making it hard to predict whether a bioinformatics tool or a database published in a high-impact journal will be useful in real-life applications
Summary
It is currently undeniable that bioinformatics tools and databases represent a highly impactful part of modern research (Wren, 2016). DNA methylation analysis notes from Ming Tang https://github.com/hussius/deeplearning- biology https://github.com/greenelab/deep- review https://github.com/danielecook/AwesomeBioinformatics https://github.com/shenwei356/awesome https://github.com/jtleek/genomicspapers https://github.com/jdidion/biotools https://github.com/crazyhottommy/getting- started- withgenomics- tools- and- resources https://github.com/seandavi/awesome- single- cell https://github.com/crazyhottommy/RNA- seq- analysis https://github.com/crazyhottommy/ChIP- seq- analysis https://github.com/seandavi/awesome- cancer- variantdatabases https://github.com/johandahlberg/awesome- 10xgenomics https://github.com/crazyhottommy/DNA- seq- analysis https://github.com/stevetsa/awesome- microbes https://github.com/crazyhottommy/DNA- methylationanalysis
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
