So far, in this week of khmer blog posts (1, 2, 3), we've been focusing on the read-to-graph aligner ("graphalign"), which enables sequence alignments to a De Bruijn graph. One persistent challenge with this functionality as introduced is that our De Bruijn graphs nodes are anonymous, so we have no …
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De Bruijn graph alignment should also be useful for exploring concepts in transcriptomics/mRNAseq expression. As with variant calling graphalign can also be used to avoid the mapping step in quantification; and, again, as with the variant calling approach, we can do so by aligning our reference sequences to the …
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There's an interesting and intuitive connection between error correction and variant calling - if you can do one well, it lets you do (parts of) the other well. In the previous blog post on some new features in khmer, we introduced our new "graphalign" functionality, that lets us align short sequences …
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One of the newer features in khmer that we're pretty excited about is the read-to-graph aligner, which gives us a way to align sequences to a De Bruijn graph; our nickname for it is "graphalign."
Briefly, graphalign uses a pair-HMM to align a sequence to a k-mer graph (aka De …
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About a month ago, I took some time to try out Docker, a container technology that lets you bundle together, distribute, and execute applications in a lightweight Linux container. It seemed neat but I didn't apply it to any real problems. (Heng Li also tried it out, and came to …
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After a fair amount of time thinking about software's place in science (see blog posts 1, 2, 3, and 4), and thinking about khmer's short- and long-term future, we're making some changes to our development process.
Semantic versioning: The first change, and most visible one, is that we are going …
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I finally got a chance to more thoroughly read Mark Stalzer and Chris Mentzel's arxiv preprint, "A Preliminary Review of Influential Works in Data-Driven Discovery". This is a short review paper that discusses concepts highlighted by the 1,000+ "influential works" lists submitted to the Moore Foundation's Data Driven Discovery …
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