WASP: allele-specific software for robust discovery of molecular quantitative trait loci
Bryce van de Geijn, Graham McVicker, Yoav Gilad, Jonathan Pritchard
doi: http://dx.doi.org/10.1101/011221
http://biorxiv.org/content/early/2014/11/07/011221
QT:{{”
Mapping of reads to a reference genome is biased by sequence polymorphisms6. Reads which contain the non-reference allele may fail to map uniquely or map to a different (incorrect) location in the genome6.
“}}
Removing deleterious mutations in Europeans [v] Africans
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3186.html Comparing nonsynonymous freq. betw. populations HT @obahcall
Landscape of oncogenic signatures across human #cancers
http://www.nature.com/ng/journal/v45/n10/full/ng.2762.html Disjoint types dominated by copy number changes or mutations
pp1127 – 1133
Giovanni Ciriello, Martin L Miller, Bülent Arman Aksoy, Yasin Senbabaoglu, Nikolaus Schultz & Chris Sander
doi:10.1038/ng.2762
Chris Sander and colleagues have extracted significant functional events from 12 tumor types. Tumors can be classified as being driven largely by either mutation or copy number changes, and, within this division, subclasses of cross-tissue patterns of events are discerned that suggest sets of combinatorial therapies.
Tomasetti & Volgenstein
Science 2 January 2015:
Vol. 347 no. 6217 pp. 78-81
DOI: 10.1126/science.1260825
It’s a correlation between aggressiveness, mutations and cell division http://www.sciencemag.org/content/347/6217/78
The discovery of integrated gene networks for autism and related disorders
Fereydoun Hormozdiari
Osnat Penn
Elhanan Borenstein
Evan E. Eichler
Published in Advance November 5, 2014, doi:10.1101/gr.178855.114 Genome Res. 2015. 25: 142-154
QT:{{”
Motivated by this observation, we have developed a novel method that simultaneously integrates information from both PPI and coexpression networks to identify highly connected modules in both types of networks that are also enriched in mutations in cases and not in controls. We call this method MAGI, short for merging affected genes into integrated networks. MAGI is based on a combinatorial
optimization algorithm that aims to maximize the number of mutations in the modules while accounting for gene length and distribution of putative LoF and missense mutations in cases and controls. MAGI is generic and can be applied to any disease, given a list of de novo mutations in cases and relevant coexpression information. Using neurodevelopmental RNA-seq data from the BrainSpan Atlas
(http://www.brainspan.org/), we have applied it to exome sequence data generated from ASD, ID, epilepsy, and schizophrenia, providing a comprehensive comparison of common and specific gene modules for these diseases.
“}}
Analysis of noncoding somatic mutations &…expression alterations http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html 505 WGS variants w. RNAseq, #TCGA as of Mar ’14
all of what’s in TCGA as of spring ’14
505 TCGA WGS Somatic mutations, Expression Calls, CNA
via
https://www.synapse.org/#!Synapse:syn2882200
Orthogonal to PCAWG-607 (Alexandrov et al + 100 "public" stomach cancers)
Programming tools: Adventures with #R http://www.nature.com/news/programming-tools-adventures-with-r-1.16609 Overview of available science packages & their increase in popularity over time
QT:{{"
Not every scientist is enthusiastic about learning the necessary programming — even though, says Ram, R is less intimidating than languages such as Python (let alone Perl or C). “There are going to be far more scientists that will be comfortable with click-and-drop interfaces than will ever learn to program at any time,” Muenchen says. Geneticist Rabih Murr, for example, took the same R course as Royo when he was a postdoc, but he did not invest as much time in practising. To get started and develop research-specific skills in R definitely requires a commitment: “It’s a matter of priorities,” he says. But after becoming a lab head at the University of Geneva in Switzerland this year, he is planning to hire someone with R experience.
”}}
#Neuroscience, Ethics & National Security http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001289
Interrogations w/ oxytocin truth serum, No-lie fMRI & p300 waves. Scary!
QT:{{"
National security agencies are also mining neuroscience for ways to advance interrogation methods and the detection of deception. The increasing sophistication of brain-reading neurotechnologies has led many to investigate their potential applications for lie detection. Deception has long been associated with empirically measurable correlates, arguably originating nearly a century ago with research into blood pressure [24]. Yet blood pressure, among other modern bases for polygraphy like heart and breathing rates, indicates the presence of a proxy for deception: stress. Although the polygraph performs better than chance, it does not reliably and accurately indicate the presence of deception, and it is susceptible to counter measures. ….
“Brain fingerprinting” utilizes EEG to detect the P300 wave, an event-related potential (ERP) associated with the perception of a recognized, meaningful stimulus, and it is thought to hold potential for confirming the presence of “concealed information” [25]. The technology is marketed for a number of uses: “national security, medical diagnostics, advertising, insurance fraud and in the criminal justice system” [26]. Similarly, fMRI-based lie detection services are currently offered by several companies, including No Lie MRI [27] and Cephos [28]. DARPA funded the pioneering research that showed how deception involves a more complex array of neurological processes than truth-telling, and that fMRI arguably can detect the difference between the two [29]. No Lie MRI also has ties to national security: they market their services to the DoD, Department of Homeland Security, and the intelligence community, among other potential customers [30].
…
In addition to questions of scientific validity, these technologies raise legal and ethical issues. Legally required brain scans arguably violate “the guarantee against self-incrimination” because they differ from acceptable forms of bodily evidence, such as fingerprints or blood samples, in an important way: they are not simply physical, hard evidence, but evidence that is intimately linked to the defendant’s mind [32]. Under US law, brain-scanning technologies might also raise implications for the Fourth Amendment, calling into question whether they constitute an unreasonable search and seizure [33].”
"}}
Correlated Genome Associations to Quantitative Trait #Network (QTN) http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000587
Uses fused #lasso for estimation of relationships
Kim & Xing (’09) provide a new method for calculating how genetic
markers associate with phenotypes by incorporating phenotype
connectivity features into the correlation structure between markers
and phenotypes. Their model attempts to quantify pleiotropic
relationships between different phenotypes and assumes a common
genotypic origin for the existence of clusters of correlated
phenotypes, which their algorithm uses to reduce the number of
significant genetic markers. In particular, Kim and Xing present a
method for performing quantitative trait analysis that implements two
novel approaches to inferring the contribution of a
[marker/allele/SNP/gene/locus] to a quantitative trait. The first is
organization of traits into a quantitative trait network (QTN). The
second is the utilization of fused lasso, a variation of multivariate
regression that seeks to minimize the number of non-zero coefficients
and least squared error. These two approaches are combined in an
attempt to minimize noise (in the form of small coefficients for SNP’s
that don’t really make a contribution) and focus on truly relevant
SNP’s while dealing with the correlated nature of quantitative
traits. Based on two datasets – simulated HapMap data and
data from the Severe Asthma Research Program – the authors show marked
improvement in accuracy and reduction of false positives over simpler
multivariate regression methods.
VIRGO: computational prediction of gene function http://nar.oxfordjournals.org/content/34/suppl_2/W340.full Webserver propagates GO terms over PPI & gene-expression #networks
This work was said to be the first web server for gene function
annotation (not the first algorithm).
The idea is to predict gene functions from known molecular interaction
networks (such as PPI), which includes both annotated and unannotated
genes. The potential function of an unannotated gene is predicted
using a propagation diagram, which takes into account the neighbors’
functions. The weight of edge in the network is determined by user uploaded expression data. Weight = |Pearson correlation| of expression
profiles of the gene pair. Weight reflects the confidence of the edge.
linkstream2 microblog 