Posts Tagged ‘from_jclub’

Jclub Paper

January 28, 2017

#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, integrative analysis implicates microRNA dysregulation in autism spectrum disorder : Nature Neuroscience : Nature Research

January 28, 2017

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).
“}}

Jclub paper

January 16, 2017

The impact of #SVs on…gene expression
http://biorxiv.org/content/early/2016/06/09/055962 24k in 147 people in GTEx pilot act as causal variants in 3-7% of ~25k eQTLs

The impact of structural variation on human gene expression

Colby Chiang, Alexandra J Scott, Joe R Davis, Emily
K Tsang, Xin Li, Yungil Kim, Farhan N Damani, Liron Ganel, GTEx Consortium, Stephen B Montgomery, Alexis Battle, Donald F Conrad, Ira M Hall
doi: https://doi.org/10.1101/055962

Uncovering Earth’s virome : Nature : Nature Research

October 21, 2016

Uncovering Earth’s virome
http://www.nature.com/nature/journal/v536/n7617/full/nature19094.html Meta analysis of 5Tb of extant #metagenomic sequences finds >125k partial viral genomes

David Paez-Espino, Emiley A. Eloe-Fadrosh, Georgios A. Pavlopoulos, Alex D. Thomas, Marcel Huntemann, Natalia Mikhailova, Edward Rubin, Natalia N. Ivanova & Nikos C. Kyrpides

Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease: Cell

October 1, 2016

Zhang, Bin, Chris Gaiteri, Liviu-Gabriel Bodea, Zhi Wang, Joshua McElwee, Alexei A. Podtelezhnikov, Chunsheng Zhang et al. "Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease." Cell 153, no. 3 (2013): 707-720.

Integrated systems approach identifies genetic…#networks in…Alzheimer’s http://www.Cell.com/cell/abstract/S0092-8674(13)00387-5 Determining causality from co-expression

Journal Club

July 23, 2016

Basset: #DeepLearning the regulatory code w/…NNs by @noncodarnia lab http://genome.cshlp.org/content/early/2016/05/03/gr.200535.115 Has score for all possible SNVs in the genome

“Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks”

The role of regulatory variation in complex traits and disease : Nature Reviews Genetics : Nature Publishing Group

June 12, 2016

Reg. variation in cplx traits by @LeonidKruglyak
http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html nice teaching figure for #eQTLs, showing how mostly cis + hotspots http://www.nature.com/nrg/journal/v16/n4/full/nrg3891.html

Identification of significantly mutated regions across cancer types highlights a rich landscape of functional molecular alterations : Nature Genetics : Nature Publishing Group

May 2, 2016

Identification of [872] sig. mutated regions across #cancer types http://www.nature.com/ng/journal/v48/n2/full/ng.3471.html ranges from noncoding annotations to 3D structure

Epigenomic analysis detects aberrant super-enhancer DNA methylation in human cancer | Genome Biology | Full Text

April 17, 2016

two papers for journal club:

1. What are super-enhancers? Pott et al., Nature Genetics (2015) http://www.nature.com/ng/journal/v47/n1/full/ng.3167.html

2. Epigenomic analysis detects aberrant super-enhancer DNA methylation in human cancer, Heyn et al., Genome Biology (2016)
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0879-2

#Epigenomic analysis detects aberrant super-enhancer DNA methylation in human #cancer
https://GenomeBiology.biomedcentral.com/articles/10.1186/s13059-016-0879-2 hypo-Me of many large blocks

Cell type- and brain region-resolved mouse brain proteome : Nature Neuroscience : Nature Publishing Group

December 13, 2015

Celltype & region–resolved mouse brain proteome
http://www.nature.com/neuro/journal/v18/n12/full/nn.4160.html proteins enriched there v liver & in specific regions (eg NCX v STR)

http://www.nature.com/neuro/journal/v18/n12/full/nn.4160.html