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
Gene-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
Model-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. “}}
Neutral 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
TIGRA: Targeted Iterative Graph Routing Assembler for breakpoint[s ]http://GENOME.CSHLP.org/content/24/2/310.long key steps: read extraction & de Bruijn #assembly
This presents a breakpoint assembler used for many projects including 1000 Genomes. It uses a targeted iterative graph routing approach. The program consists of two steps: read extraction and then assembly. The assembly step uses a de Bruin graph-based approach to create contigs from the selected reads. A shortcoming of TIGRA is it depends on the success of the first step of the program, selection of reads that span breakpoints. Thus TIGRA is sensitive to the breakpoint annotation accuracy input. Breakpoints determined from discordant paired-end or split-end alignments and by predictors like breakdancer, delly, genomestrip are excellent for TIGRA, but those determined only by read-depth such as CNVnator and RDX are poor performers.
As input TIGRA requires putative breakpoints annotation/prediction (preferably at nucleotide level or at least within 100bp resolution) and BAM files (sequence reads aligned to reference genome).
In the read extraction TIGRA tries to select all the reads that are likely associated with the breakpoint as long ass they have at least one ned or subsegment that is confidently mapped. For known SV types, TIGRA extract reads selectively to reduce the over representation of the reference allele. The assembly step uses the a de Bruin graph-based approach to create contigs from the selected reads. For this TIGRA first uses an iterative procedure to explore multiple k-mers and thus increases the chance of assembling of low coverage reads. Next it records alternative path in the contain graph
Massively parallel quantification of…regulatory effects of #noncoding…variation in a…cohort
http://genome.cshlp.org/content/25/8/1206.long new popstarr assay
http://www.ncbi.nlm.nih.gov/pubmed/26084464
Genome Res. 2015 Aug;25(8):1206-14. doi: 10.1101/gr.190090.115. Epub 2015 Jun 17.
Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.
Vockley CM1, Guo C2, Majoros WH3, Nodzenski M4, Scholtens DM4, Hayes MG5, Lowe WL Jr5, Reddy TE6.
Life history of retrocopies [incl. ~5k #pseudogenes]
illuminates…evolution
http://genome.cshlp.org/content/early/2016/01/04/gr.198473.115 Suggests modes of promotor acquisition
[tag
from,fn,from_fn,jclub,pseudogenes,u41pg,evolution,gencode,cmppgene]
Reprogramming…#epigenome & transcriptome in…muscle after training http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4622000/ Delta(Me) from #exercise in 1 leg v. other
QT:{{”
Exercise has long been known to have an effect on health, lowering the incidence of cardiovascular disease, obesity, and type 2 diabetes. Recently, it has been found that exercise can have a direct effect on DNA methylation and gene expression. A group of 23 subjects exercised one leg but not the other for 45 minutes four times a week for three months. At the end of this period, the methylation and gene expression patterns were different in the exercised leg relative to the one that did not exercise, indicating a direct effect of exercise on
epigenetics.
“}}
Quote from:
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2987618/
Aging Pathways: insulin-foxo, mTOR, AMPK, Sirtuin