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I work on the Semantic Scholar academic search engine, and wanted to let people know about a blog post we just put up where we combed through 7 million biomed papers and found the most-mentioned GitHub repos. The top one was Sickle, and 14 out of the top 15 were bioinformatics (the 15th was Keras, a general deep learning library). Enjoy!
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Thanks for sharing! Interesting project. -
Thanks SES! Also do you have any suggestions about the best ways to let people know about the blog? We're mostly just a bunch of computer scientists at Semantic Scholar so we don't know the field well - I literally found this forum by googling around. But if the post is worthwhile for bioinformatics specialists I would really love to spread it around a bit more. Thanks!!Comment
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You could also post this on Biostars.org using "News" category.Comment
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Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
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I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
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