http://eol.org/pages/1078536/names
Bacteria appear to be placed under Insecta in EOL’s taxonomy http://eol.org/pages/40008429/overview Rather puzzling! Any ideas why?
Posts Tagged ‘from’
Bacteria?
February 20, 2016Similarity network fusion for aggregating data types on a genomic scale : Nature Methods : Nature Publishing Group
February 9, 2016Similarity #network fusion for aggregating data types
http://www.nature.com/nmeth/journal/v11/n3/full/nmeth.2810.html Combines mRNA, miRNA & gene fusions to classify cancer subtypes http://compbio.cs.toronto.edu/SNF/SNF
Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution : Nature : Nature Publishing Group
January 23, 2016Dynamics of genomic clones in breast #cancer PDX at #singlecell resolution http://www.nature.com/nature/journal/v518/n7539/full/nature13952.html Extensive trees of samples & some WGS
Peter Eirew,
Adi Steif,
Jaswinder Khattra,
Gavin Ha,
…
Jazmine Brimhall,
Arusha Oloumi,
Tomo Osako
et al.
Nature 518, 422–426 (19 February 2015) doi:10.1038/nature13952
Research Parasites
January 23, 2016Dara sharing http://www.nejm.org/doi/full/10.1056/NEJMe1516564 Deems #datascientists as “research parasites,” using another’s data for their own ends via @dspakowicz
QT:{{”
“A second concern held by some is that a new class of research person will emerge — people who had nothing to do with the design and execution of the study but use another group’s data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited. There is concern among some front-line researchers that the system will be taken over by what some researchers have characterized as “research parasites.””
“}}
Retina Macbook 2015 Teardown
January 14, 2016Microphones appear near audio jack on RHS
as described in step 23
https://www.ifixit.com/Teardown/Retina+Macbook+2015+Teardown/39841
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells | Science
January 13, 2016#SingleCell #RNASeq Reveals Dynamic, Random Monoallelic Gene Expression, occurring in ~20% of genes in mice cells
http://science.sciencemag.org/content/343/6167/193.abstract
MarsEdit 3 – Desktop blog editing for the Mac.
January 7, 2016Mind Mapping Software: Mind Maps | MindMeister
December 31, 2015perhaps useful for workflow viz
https://www.mindmeister.com/
Ewing AD*, Houlahan KE…..Stuart JM, Boutros PC (2015) “Combining accurate tumour genome simulation with crow d-sourcing to benchmark somatic single nucleotide variant detection” Nature Methods 12(7):623-630 (PMID: 25984700)
December 28, 2015Tumor genome simulation w/ #crowdsourcing to benchmark…SNV detection http://www.nature.com/nmeth/journal/v12/n7/full/nmeth.3407.html Addresses lack of gold standards & privacy
Ewing, Houlahan…..Stuart, Boutros (2015) “Combining accurate
tumour genome simulation with crowd-sourcing to benchmark somatic
single nucleotide variant detection” Nature Methods 12(7):623-630
(PMID: 25984700)
A crowdsourced benchmark of somatic mutation detection algorithms was
introduced for the ICGC-TCGA DREAM challenge. This has the advantage
of dealing with the lack of gold standard data and the issue of
sharing private genomic data. All groups worked on three different
simulated tumor-normal pairs generated with BAMSurgeon, by directly
adding synthetic mutations to existing reads. An ensemble of
pipelines outperforms the best individual pipeline in all cases,
assessed on the basis of recall, precision and F-score.
Parameterization and genomic localization both have an effect on
pipeline performance, while characteristics of prediction errors
differed for most pipelines.
Bias from removing read duplication in ultra-deep sequencing experiments
December 25, 2015Bias from removing read duplication [eg from PCR amplification] in ultra-deep #sequencing
http://bioinformatics.oxfordjournals.org/content/early/2014/01/02/bioinformatics.btt771 pot. overcorrection issues
Zhou et al.
Bias from removing read duplication in ultra-deep sequencing experiments
Estimating variant allele frequency and copy number variations can be approached by counting reads. In practice, read counting is
complicated by bias from PCR amplification and from sampling coincidence. This paper assessed the overcorrection introduced while removing read duplicates. The overcorrection is a particular concern when the sequencing is ultra-deep and the insert size is short and non-variant.
