CTCF-Mediated…3D Genome Architecture
http://www.cell.com/cell/abstract/S0092-8674(15)01504-4 SNPs give different #chromatin topologies, including strong #allelic effects
Posts Tagged ‘from’
CTCF-Mediated Human 3D Genome Architecture Reveals Chromatin Topology for Transcription: Cell
March 4, 2016Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins : Nature Genetics : Nature Publishing Group
March 3, 2016Gene-gene & gene-env interactions…by #transcriptome…in twins by @dermitzakis lab
http://www.nature.com/ng/journal/v47/n1/full/ng.3162.html Nice model for ASE HT @cjieming
Gene-gene and gene-environment interactions detected by transcriptome sequence analysis in twins
Alfonso Buil, Andrew Anand Brown, Tuuli Lappalainen, Ana Viñuela, Matthew N Davies, Hou-Feng Zheng, J Brent Richards, Daniel Glass, Kerrin S Small, Richard Durbin, Timothy D Spector & Emmanouil T Dermitzakis
PLOS Genetics: A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures
February 27, 2016Model-Based Approach to Inferring…#Cancer Mutation Signatures http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005657 Assuming independence betw 3 NTs, 11 v 95 parameters
QT:{{”
The first contribution of this paper is to suggest a more parsimonious approach to modelling mutation signatures, with the benefit of producing both more stable estimates and more easily interpretable signatures. In brief, we substantially reduce the number of parameters per signature by breaking each mutation pattern into “features”, and assuming independence across mutation features. For example, consider the case where a mutation pattern is defined by the substitution and its two flanking bases. We break this into three features
(substitution, 3′ base, 5′ base), and characterize each mutation signature by a probability distribution for each feature (which, by our independence assumption, are multiplied together to define a distribution on mutation patterns). Since the number of possible values for each feature is 6, 4, and 4 respectively this requires 5 + 3 + 3 = 11 parameters instead of 96 − 1 = 95 parameters. Furthermore, extending this model to account for ±n neighboring bases requires only 5 + 6nparameters instead of 6 × 42n − 1. For example, considering ±2 positions requires 17 parameters instead of 1,535. Finally,
incorporating transcription strand as an additional feature adds just one parameter, instead of doubling the number of parameters. “}}
Identification of neutral tumor evolution across cancer types : Nature Genetics : Nature Publishing Group
February 27, 2016Neutral tumor #evolution across #cancer types
http://www.nature.com/ng/journal/v48/n3/full/ng.3489.html Initial burst of driver events followed by random mutations
Bacteria?
February 20, 2016http://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?
Similarity 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
linkstream2 microblog 