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
Archive for the 'SciLit' Category
Variation in cancer risk among tissues can be explained by the number of stem cell divisions
January 12, 2015NEw paper using BrainSpan data
January 12, 2015The 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
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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.
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Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types : Nature Genetics : Nature Publishing Group
January 8, 2015Analysis 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
December 31, 2014Programming 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
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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.
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Neuroscience, Ethics, and National Security: The State of the Art
December 30, 2014#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!
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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].”
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PLOS Genetics: Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network
December 28, 2014Correlated 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 functions
December 25, 2014VIRGO: 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.
BMC Bioinformatics | Abstract | Sensitive detection of pathway perturbations in cancers
December 23, 2014Sensitive detection of pathway perturbations in #cancers
http://www.biomedcentral.com/1471-2105/13/S3/S9/abstract Differential expression of #pathways (in toto or a sub-part)
** Sensitive detection of pathway perturbations in cancers. Rivera et al. BMC Bioinfo (2014)
In this paper, the authors introduce a new computational method that identifies subsets of pathways that exhibit differential gene expression between cancer and normal tissue. Some previous methods only considered differential expression in sets of genes without considering the structure of interactions between the genes or their protein products. Other previous methods looked at pathway
perturbations, but required all members of a pathway to exhibit differential gene expression in order for the pathway to appear significant. The authors demonstrate the general superiority of their method to these previous methods, as well as the robustness of their method to missing data. The authors also consider future enhancements, such as taking into account the direction of differential expression, using more information on the nature of each gene interaction involved, and using universal protein interaction networks to incorporate data beyond what is found in curated pathway databases.
Human Genetics Shape the Gut Microbiome: Cell
December 19, 2014Human Genetics Shape the Gut #Microbiome
http://www.cell.com/abstract/S0092-8674%2814%2901241-0 A particular family of firmicutes is highly heritable & correlated w/ BMI
Goodrich, J.K., Waters, J.L., Poole, A.C., Sutter, J.L., Koren, O., Blekhman, R., Beaumont, M., Van Treuren, W., Knight, R., Bell, J.T., et al. (2014). Human Genetics Shape the Gut Microbiome. Cell 159, 789–799.
hi-C for 9 cell types
December 16, 2014A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of #Chromatin Looping
http://www.cell.com/cell/abstract/S0092-8674(14)01497-4 HiC data for 9 celltypes
A 3D Map of the Human Genome at Kilobase Resolution Reveals Principles of Chromatin Looping
Suhas S.P. Rao
,Miriam H. Huntley
Neva C. Durand
Elena K. Stamenova
Ivan D. Bochkov
James T. Robinson
Adrian L. Sanborn
Ido Machol
Arina D. Omer
Eric S. Lander
Erez Lieberman Aiden
DOI: http://dx.doi.org/10.1016/j.cell.2014.11.021
http://www.cell.com/cell/abstract/S0092-8674(14)01497-4
linkstream2 microblog 