linkstream2 microblog

Partitioning heritability of regulatory…variants across 11 common diseases http://www.Cell.com/ajhg/abstract/S0002-9297(14)00426-1 Almost 80% #noncoding v 10% coding

The paper below claims to find most of the heritability of 11 common diseases in regulatory regions (79% of heritability found in regulatory regions, <10% in protein coding regions).

Partitioning heritability of regulatory and cell-type-specific variants across 11 common
diseases.

Gusev A, Lee SH, Trynka G, Finucane H, Vilhjálmsson BJ, Xu H, Zang C, Ripke S, Bulik-Sullivan B, Stahl E; Schizophrenia Working Group of the Psychiatric Genomics Consortium; SWE-SCZ Consortium, Kähler AK, Hultman CM, Purcell SM, McCarroll SA, Daly M, Pasaniuc B, Sullivan PF, Neale BM, Wray NR, Raychaudhuri S, Price AL; Schizophrenia Working Group of the Psychiatric Genomics Consortium; SWE-SCZ Consortium.

Am J Hum Genet. 2014 Nov 6;95(5):535-52. doi:
10.1016/j.ajhg.2014.10.004. Epub 2014 Nov 6.

A #circadian gene-expr atlas in mammals by @jbhclock lab
http://www.PNAS.org/content/111/45/16219.abstract 43% of genes have a daily rhythm in at least 1 tissue [1/2]

.@jbhclock Fewest circadian genes in brain; most in liver. Perhaps this more reflects daily feeding cycle than true light-dark cycle? [2/2]

A circadian gene expression atlas in mammals: Implications for biology and medicine

Ray Zhanga,1,
Nicholas F. Lahensa,1,
Heather I. Ballancea,
Michael E. Hughesb,2, and
John B. Hogenescha,2

* Interestingly brain regions have the fewest circ genes(only ~3%), liver has most

* Diseases assoc with circadian genes correlate with NIH funding

* Genes can have up to a 6-hour phase diff. Between diff. organs (eg Vegfa betw. Heart & fat)

* 56 of the top 100 drugs incl. Top 7, targeted the product of a circadian gene. Related to the half-life of drugs.

* Could the liver genes be more reflective of feeding rhythm rather than true circadian clock.

Genome expansion via lineage splitting…in the cicada endosymbiont Hodgkinia http://www.PNAS.org/content/112/33/10192.abstract Host lifecycle partially enables split

Calling genotypes from public RNA-sequencing data enables
identification of genetic variants that affect gene-expression levels

Patrick Deelen†,
Daria V Zhernakova†,
Mark de Haan,
Marijke van der Sijde,
Marc Jan Bonder,
Juha Karjalainen,
K Joeri van der Velde,
Kristin M Abbott,
Jingyuan Fu,
Cisca Wijmenga,
Richard J Sinke,
Morris A Swertz† and
Lude Franke†

Genotypes from…#RNAseq…enables identification of…variants, related to ASE & eQTLs
https://GenomeMedicine.biomedcentral.com/articles/10.1186/s13073-015-0152-4 Validation w/ #Geuvadis

Genotypes from…#RNAseq…enables identification of…variants, related to ASE & eQTLs
https://GenomeMedicine.biomedcentral.com/articles/10.1186/s13073-015-0152-4 Validation w/ #Geuvadis

also from ’14:
https://plus.google.com/+MarkGerstein/posts/D8kYoqiWL1P

Best Practices for Sci Computing
http://journals.PLOS.org/plosbiology/article?id=10.1371/journal.pbio.1001745 Usual stuff (GitHub, profilers, assertions) + some gems (turn bugs into test cases)

Temporal Collateral Sensitivity in Tumor…Evolution
http://www.cell.com/cell/abstract/S0092-8674(16)30059-9 Drug-fitness landscape illuminates transiently vulnerable state

Exploiting Temporal Collateral Sensitivity in Tumor Clonal Evolution

Boyang Zhao
Joseph C. Sedlak
Raja Srinivas
Pau Creixell
Justin R. Pritchard
Bruce Tidor
Douglas A. Lauffenburger
Michael T. Hemann

LongISLND: in silico sequencing of lengthy & noisy datatypes https://www.ncbi.nlm.nih.gov/pubmed/27667791 @PacBio sim. software (NB: was advisor to @binaTechs)

#RNA Struc. Determinants of Optimal Codons…by MAGESeq
http://www.cell.com/cell-systems/fulltext/S2405-4712(16)30368-4 Probing effect of synonymous changes; towards a better dN/dS

RNA Structural Determinants of Optimal Codons Revealed by MAGE-Seq http://www.cell.com/cell-systems/fulltext/S2405-4712(16)30368-4

Genome-wide…analysis implicates miRNA dysregulation in #ASD http://www.nature.com/neuro/journal/v19/n11/full/nn.4373.html 58 diff. expr. miRNAs incl 17 strongly down in cases

http://www.nature.com/neuro/journal/v19/n11/full/nn.4373.html

QT:{{”
The miRNA expression profiles were very similar between the frontal and temporal cortex, but were distinct in the cerebellum
(Supplementary Fig. 2a–f), consistent with previous observations for mRNAs11, 12. We therefore combined 95 covariate-matched samples (47 samples from 28 ASD cases and 48 samples from 28 controls;
Supplementary Fig. 1c and Supplementary Table 1) from the FC and TC for differential gene expression (DGE) analysis, comparing ASD and CTL using a linear mixed-effects regression framework to control for potential confounders (Online Methods). We identified 58 miRNAs showing significant (false discovery rate (FDR) < 0.05) expression changes between ASD and CTL: 17 were downregulated and 41 were upregulated in ASD cortex (Fig. 1b and Supplementary Table 2). The fold changes for the differentially expressed miRNAs were highly concordant between the FC and TC (Pearson correlation coefficient R = 0.96, P < 2.2 × 10−16; Fig. 1c).
“}}