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  • adaptivegenome
    Super Moderator
    • Nov 2009
    • 436

    #1

    Reproducibility & Accessibility for NGS

    Great paper for those that have not seen it yet: "Next-generation sequencing data interpretation: enhancing reproducibility and accessibility". The paper does a great job of highlighting barriers to open and transparent genomics research.

  • Richard Finney
    Senior Member
    • Feb 2009
    • 701

    #2
    tl;dr for those who dont have a subscription to Nature:
    [ please forgive any tounge-in-cheek-isms ... ]

    This is an editorial in Nature, clearly labeled "opinion".

    Although Hapmap and 1K genomes has crafted a method, not everyone follows it. This methodology uses Picard and samtools and "the standard" modification of the hg19 snapshot. "This is necessary and unavoidable". Some other methodologies lack details. Most results reported in today’s publications using NGS data cannot be accurately verified. BioExtract18, Galaxy19, GenePattern20, GeneProf21, Mobyle22 totally rock! Cloudman rocks! Amazon cloud and other API and virtual machine based solutions are bad. Github is bad . Appstore type stuff is better. Cool integrated stuff is coming. Guidelines for reproducibility are presented.

    Key quotes:
    One would expect that these
    approaches will be widely used in studies
    that feature a similar design. As we demonstrate
    below, this is not the case and is thus a
    cause for grave concern because not following
    tested practices undermines the quality
    of biomedical research, limiting its potential.


    At the same time, our computational colleagues
    must ask themselves if it is really
    possible for biologists to use their software.
    The emergence of integrative frameworks
    for accessible and reproducible analysis is
    a good indicator that things are starting to
    change, as the next big change in life sciences
    will come not from the new ways to
    generate data but from the innovative ways
    to analyze them.


    ____
    Interesting and thought provoking. I am not convinced that there is one "best practices" methodology. I welcome alternatives to any "standard" that produce good results. The day of clicking on a button on your iphone to upload your USB connected sequencer data and having p-values pop up on your HTML5 based IGV with links to power point slides ... in real time ... may be coming. Til then ... I'll keep typing "./configure; make" ... and cursing at the script files.

    Comment

    • xied75
      Senior Member
      • Feb 2012
      • 129

      #3
      You mean 'cruising' the script files?

      Comment

      • adaptivegenome
        Super Moderator
        • Nov 2009
        • 436

        #4
        I actually agree there is not one best practice. This is the problem. If there one way to do it, we could all just cite the Broad/GATK. I think the solution might lie in establishing standards for reporting workflows. Surely galaxy is one option. You could encode a workflow there and cite it in the paper.

        But not everyone uses galaxy so maybe we need a way to share and modify workflows (like with github) that we can reference in our papers. I think to be transparent and enable others to evaluate our work, this is just as critical as uploading our sequences to genbank.

        Anyways, thats what I got out of the commentary.





        Originally posted by Richard Finney View Post
        tl;dr for those who dont have a subscription to Nature:
        [ please forgive any tounge-in-cheek-isms ... ]

        This is an editorial in Nature, clearly labeled "opinion".

        Although Hapmap and 1K genomes has crafted a method, not everyone follows it. This methodology uses Picard and samtools and "the standard" modification of the hg19 snapshot. "This is necessary and unavoidable". Some other methodologies lack details. Most results reported in today’s publications using NGS data cannot be accurately verified. BioExtract18, Galaxy19, GenePattern20, GeneProf21, Mobyle22 totally rock! Cloudman rocks! Amazon cloud and other API and virtual machine based solutions are bad. Github is bad . Appstore type stuff is better. Cool integrated stuff is coming. Guidelines for reproducibility are presented.

        Key quotes:
        One would expect that these
        approaches will be widely used in studies
        that feature a similar design. As we demonstrate
        below, this is not the case and is thus a
        cause for grave concern because not following
        tested practices undermines the quality
        of biomedical research, limiting its potential.


        At the same time, our computational colleagues
        must ask themselves if it is really
        possible for biologists to use their software.
        The emergence of integrative frameworks
        for accessible and reproducible analysis is
        a good indicator that things are starting to
        change, as the next big change in life sciences
        will come not from the new ways to
        generate data but from the innovative ways
        to analyze them.


        ____
        Interesting and thought provoking. I am not convinced that there is one "best practices" methodology. I welcome alternatives to any "standard" that produce good results. The day of clicking on a button on your iphone to upload your USB connected sequencer data and having p-values pop up on your HTML5 based IGV with links to power point slides ... in real time ... may be coming. Til then ... I'll keep typing "./configure; make" ... and cursing at the script files.

        Comment

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