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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.
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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.
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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. -
You mean 'cruising' the script files?Comment
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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 PostComment
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The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist on Modified Bases...Yesterday, 07:01 AM -
Proteins are often described as the workhorses of the cell, and identifying their sequences is key to understanding their role in biological processes and disease. Currently, the most common technique used to determine protein sequences is mass spectrometry. While still a valuable tool, mass spectrometry faces several limitations and requires a highly experienced scientist familiar with the equipment to operate it. Additionally, other proteomic methods, like affinity assays, are constrained...04-04-2024, 04:25 PM
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