Note: at the Lab for Data Intensive Biology, we're trying out a new journal club format where we summarize our thoughts on the paper in a blog post. For this blog post, Luiz wrote the majority of the text and the rest of us added questions and comments.
The paper …
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Note: A year ago, I wrote this in response to an editorial request. Ultimately they weren't interested in publishing it, and I got distracted and this languished on my hard disk. So when I remembered it recently, I decided to just push it out to my blog, where I should …
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Note: Last week, I submitted my review of Stephen R. Piccolo, Adam B. Lee, and Michael B. Frampton's paper, Tools and techniques for computational reproducibility. Soon after, Dan Katz wrote a blog post about notebooks, and in a comment I mentioned Piccolo's paper; and, after dropping a note to Dr …
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This is a response to (parts of) Dr. Lior Pachter's post, "The myths of bioinformatics software". (You can also see my post on bioinformatics software licensing for at least some of the background arguments.)
I agree with a lot of what Lior says: most bioinformatics software is not very good …
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If a piece of bioinformatics software is not fully open source, my lab and I will generally seek out alternatives to it for research, teaching and training. This holds whether or not the software is free for academic use.
If a piece of bioinformatics software is only available under the …
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(This is a review of Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees, Solomon and Kingsford, 2015.)
In this paper, Solomon and Kingsford present Sequence Bloom Trees (SBTs). SBT provides an efficient method for indexing multiple sequencing datasets and finding in which datasets a query sequence is present …
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We just submitted our review of the paper Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees., by Brad Solomon and Carl Kingsford.
The paper outlines a fairly simple and straightforward way to query massive amounts of sequence data (5 TB of mRNAseq!) in very small disk (~70 GB …
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