Archive for the 'SciLit' Category

FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data | BMC Bioinformatics | Full Text

March 2, 2017

FastProject: A Tool for Low-Dimensional Analysis of #ScRNASeq https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1176-5 Software for many reductions to 2D scatterplots

* FastProject: A Tool for Low-Dimensional Analysis of Single-Cell RNA-Seq Data/ D. DeTomaso, N. Yosef. BMC Bioinformatics
2016.17(1):315. doi: 10.1186/s12859-016-1176-5

FastProject, developed by DeTomaso and Yosef, is a software tool for analyzing and interpreting single-cell RNA-Seq(scRNA-Seq) data. This pipeline utilizes a plethora of dimensionality reduction methods to project the high-dimensional scRNA-Seq data (i.e. the gene expression matrix) to dozens of two-dimensional scatter-plots. By incorporating the signature-based analysis, the biological significance of these two-dimensional representations can be systematically investigated. FastProject was designed using a modular architecture with the aim of serving as a general platform for the development and evaluation of new scRNA-Seq analysis methods.

Inferring chromatin-bound protein complexes from genome-wide binding assays – Genome Research

February 26, 2017

Inferring [w. NMF] chromatin-bound protein complexes [of TFs] from [ENCODE ChIP-seq] binding assays, by @ElementoLab
http://genome.cshlp.org/content/23/8/1295.full

Giannopoulou E, Elemento O. 2013. Inferring chromatin-bound
protein complexes from genome-wide binding assays. Genome Research, Published in Advance April 3, 2013, doi: 10.1101/gr.149419.112.

This study uses nonnegative matrix factorization (NMF) of ENCODE CHIP-seq data (transcription
factors and histone modifications) to predict complexes of
transcription factors that bind DNA
together; it then assesses how these predicted complexes regulate gene expression. It goes beyond
previous studies in that it attempts to treat the TFs as complexes rather than individuals. A handful of
the predicted complexes correspond to known regulatory complexes, e.g. PRC2, and overall, the
complexes were enriched for known protein-protein interactions. Linear regression and random forest
models were then used to predict the effects of the complexes on the expression of adjacent genes. In
both models, the complexes performed better than those predicted from a scrambled TF read count
matrix. Overall, this study provides a large set of hypotheses for combinations of TFs that may
function together, as well as potential new components of known complexes.

Detecting overlapping protein complexes in protein-protein interaction networks : Nature Methods : Nature Research

February 24, 2017

http://www.nature.com/nmeth/journal/v9/n5/abs/nmeth.1938.html

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human: Cell

February 24, 2017

FissionNet: Proteome-wide [pombe] Interactome Reveals #Network Evolution Principles
http://www.Cell.com/cell/abstract/S0092-8674(15)01556-1 Involving ~1300 soluble proteins

Computational tools for cancer immunology : Computational genomics tools for dissecting tumour-immune cell interactions : Nature Reviews Genetics : Nature Research

February 24, 2017

http://www.nature.com/nrg/journal/v17/n8/fig_tab/nrg.2016.67_T1.html

Table 1: Computational tools for cancer immunology
FromComputational genomics tools for dissecting tumour–immune cell interactions
Hubert Hackl,
Pornpimol Charoentong,
Francesca Finotello
& Zlatko Trajanoski
Nature Reviews Genetics 17, 441–458 (2016) doi:10.1038/nrg.2016.67

interesting paper

February 22, 2017

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.

JClub papers

February 16, 2017

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 and genome reduction in the cicada endosymbiont Hodgkinia

February 13, 2017

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

paper on geuvadis rna-seq variant calling

February 11, 2017

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

Best Practices for Scientific Computing

February 5, 2017

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)