1. A mildly crazy idea: crowdsourced -omic analysis with data privacy sunset?

    Or, "can we crowdsource BGI?" ;)

    With all of the crazy need surrounding genomic analysis -- most of it on a shoestring budget -- I am thinking about a mildly crazy idea.

    What if I offered to computationally analyze people's non-model transcriptomic and metagenomic data for them, in exchange for (a) non-exclusive access …

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  2. A simple idea: standard but optional review criteria for bioinformatics papers

    Brad Chapman (@chapmanb on twitter) wrote and signed a nice review of my submission to the Bioinformatics Open Source Conference. In his review, he said

    My only small suggestion is to include some discussion about your
    reproducibility work during the talk: the Amazon AMI, documentation
    and reproducible ipython workflows. This …
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  3. Paper draft: Scaling metagenome sequence assembly with probabilistic de Bruijn graphs

    (updated to point to http://arxiv.org/).

    Authors: Jason Pell, Arend Hintze, Rosangela Canino-Koning, Adina Howe, James M. Tiedje, C. Titus Brown


    The memory requirements for de novo assembly of short-read shotgun sequencing data from complex microbial populations are an increasingly large practical barrier to environmental studies. Here we …
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  4. Course: Analyzing Next-Generation Sequencing Data (2011 version)

    The second iteration of our bioinformatics summer course, Analyzing Next-Generation Sequencing Data, just finished. It was a great success, at least judging from the comments that people made to us personally; the evaluations aren't yet complete.

    The what: a two week course on analyzing next-gen sequencing data, using the Amazon …

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  5. CSE 891, Computational Science for Evolutionary Biologists

    Course page at: http://ged.msu.edu/courses/2010-fall-cse-891/:

    This course will introduce biologists to computational thinking, practical computational techniques, and research topics in computational evolution. The course will consist of three intensive hands-on 5-week modules: computational competence in UNIX; data mining and hypothesis generation using the Avida digital life …
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  6. A memory efficient way to remove low-abundance k-mers from large (metagenomic?) DNA data sets

    I've spent the last few weeks working on a simple solution to a challenging problem in DNA sequence assembly, and I think we've got a nice simple theoretical solution with an actual implementation. I'd be interested in comments!


    Briefly, the algorithmic challenge is this:

    We have a bunch of …

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  7. Course announcement: Analyzing Next-Generation Sequencing Data

    Analyzing Next-Generation Sequencing Data

    May 31 - June 11th, 2010

    Kellogg Biological Station, Michigan State University

    CSE 891 s431 / MMG 890 s433, 2 cr

    Applications are due by midnight EST, April 9th, 2010.

    Course sponsor: Gene Expression in Disease and Development Focus Group at Michigan State University.

    Instructors: Dr. C. Titus …

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  8. Easily Accessible Web-Based Tools For Analyzing Next-Generation Sequencing Data From Agricultural Animals

    Just submitted this on Thursday:

    Next generation sequencers are beginning to impact agricultural biology. Over the next few years, next generation sequencing will produce incredibly large datasets that will address structural (e.g., SNPs, CNVs, indels, methylation, translocations) and functional (e.g., RNA expression, transcription factor binding sites) variation in …
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  9. corebio proceedeth

    corebio, the joint effort by a junta of California bioinformaticians to replace BioPython with something we like better, is proceeding interestingly. So far we have discussed the following issues:

    • what license? (BSD)
    • what focus? (sequence manipulation & parsing)
    • what about binary extensions? (focus on API, provide fast implementations where appropriate, but …
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