Archive for the ‘SciLit’ Category

Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution : Nature : Nature Publishing Group

Saturday, January 23rd, 2016

Dynamics 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

Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells | Science

Wednesday, January 13th, 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

Health ROI as a measure of misalignment of biomedical needs and resources : Nature Biotechnology : Nature Publishing Group

Sunday, January 10th, 2016

Health ROI as a measure of misalignment of…needs & resources by @arzhetsky http://www.nature.com/nbt/journal/v33/n8/full/nbt.3276.html See funding decisions like stock trades

QT:{{"In a recently published letter to Nature Biotechnology, Lixia Yao,
IGSB core faculty Andrey Rzhetsky and colleagues dissect the decisions
made in funding choices. His team compares these choices by funding
agencies to trades in a financial market. In this communication, they
expand on the idea that there exists an imbalance between health needs
and biomedical research investment.

In order to fairly examine the relationship between biomedical need
and biomedical research, they validated a new, insurance based measure
of health burden that enables automatic evaluation of burden and
research investment for many more diseases than have been previously
assessed. "
"}}

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)

Monday, December 28th, 2015

Tumor 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

Friday, December 25th, 2015

Bias 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.

PLOS Computational Biology: Catalysis of Protein Folding by Chaperones Accelerates Evolutionary Dynamics in Adapting Cell Populations

Friday, December 18th, 2015

Folding by Chaperones Accelerates Evolutionary Dynamics
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003269 Multiscale models link NT mutations, PPIs & cell populations

Cell type- and brain region-resolved mouse brain proteome : Nature Neuroscience : Nature Publishing Group

Sunday, December 13th, 2015

Celltype & region–resolved mouse brain proteome
http://www.nature.com/neuro/journal/v18/n12/full/nn.4160.html proteins enriched there v liver & in specific regions (eg NCX v STR)

http://www.nature.com/neuro/journal/v18/n12/full/nn.4160.html

Understanding the Cellular and Molecular Mechanisms of Physical Activity-Induced Health Benefits: Cell Metabolism

Saturday, December 5th, 2015

Cellular…Mechanisms of Physical Activity-Induced Health Benefits http://www.cell.com/cell-metabolism/abstract/S1550-4131(15)00223-5 Mitochondria important to “#exercise responsome”

QT:{{”
… augmenting overall mitochondrial density and oxidative
phosphorylation capacity by as much as 2-fold (Hood et al., 2011). Moreover, PA affects mito-chondrial quality as well as quantity, and recent studies suggest that the functional properties of these organelles are much more heterogeneous and dynamic in nature than previously appreci-ated (Jacobs and Lundby, 2013). Interestingly, PA-induced mito-chondrial biogenesis also occurs in tissues other than skeletal muscle, including brain (E et al., 2013; Steiner et al., 2011), liver (Boveris and Navarro, 2008; E et al., 2013; Navarro et al., 2004), adipose tissue (Laye et al., 2009; Sutherland et al., 2009), and kidney (Navarro et al., 2004), providing evidence that exercise also increases metabolic demand in these tissues and/or stimu-lates inter-organ crosstalk.
….
The rate-limiting impediment to discovery of molecular trans-ducers and their function is not the ‘‘omic” core technology, but the bioinformatics to extract the most useful signals and generate the most appropriate biological interpretation, including those associated with exercise adaptation. Robust computational and bioinformatics analytical tools allowing inte-gration of large datasets from a multiplicity of ‘‘omics” platforms with in vivo exercise physiology assays and measurements would contribute greatly to our understanding of the response to acute bouts of exercise and long-term adaptation to regular exercise exposure.
….
this regard, the development of detailed molecular profiles in cells and tissues in response to acute and chronic exposures to exercise (‘‘the exercise responsomes”) would provide the benchmark against which all other exercise-related conditions, including aging, sex differences, disease states, etc., could be compared for commonality and specificity.

Resources are needed not only to fund new trainees, but also to restructure current programs in a manner that combines studies in integrative physiology and bioenergetics with training in basic biochemistry, cellular and molecular biology, and bioinformatics. Additional resources are needed to establish mechanisms for assembling and supporting interdisciplinary teams that are able to catalyze and sustain ex-ercise research. The field would likewise benefit from a program to support a multi-site consortium of exercise scientists with complimentary expertise and resources that together are well positioned to tackle the large, challenging problems relevant to the overarching mission.
“}}

http://www.cell.com/cell-metabolism/abstract/S1550-4131(15)00223-5

Understanding multicellular function and disease with human tissue-specific networks : Nature Genetics : Nature Publishing Group

Saturday, November 28th, 2015

Human tissue-specific #networks by @TroyanskayaLab
http://www.nature.com/ng/journal/v47/n6/full/ng.3259.html
Brain-specific ones & NetWAS approach for combining #GWAS genes

access all tissue networks including the brain-specific
networks at giant.princeton.edu

Enhancer Evolution across 20 Mammalian Species: Cell

Saturday, November 28th, 2015

#Enhancer Evolution across 20 Mammal[s is faster than for promoters] by @PaulFlicek lab http://www.cell.com/cell/abstract/S0092-8674(15)00007-0 H3K27ac & H3K4me3 #chipseq

http://www.cell.com/cell/abstract/S0092-8674(15)00007-0