linkstream2 microblog

mostly focussed on microRNAs, but it goes into the role of exRNAs on cancer:
http://www.frontiersin.org/Journal/10.3389/fgene.2013.00173/abstract

http://www.nature.com/nsmb/journal/vaop/ncurrent/full/nsmb.2675.html
They say most editing sites identified by modENCODE are technical artifacts !

http://www.sciencedirect.com/science/article/pii/S0002929713003832

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.2645.html

The opposite of clique completion: #Network deconvolution.. to distinguish direct dependencies http://go.nature.com/dVzNwC via @taziovanni

Network deconvolution as a general method to distinguish direct dependencies in networks

Soheil Feizi, Daniel Marbach, Muriel Médard & Manolis Kellis

http://www.nature.com/nbt/journal/vaop/ncurrent/full/nbt.2635.html

My thoughts:

Indirect relationships in a network can confound the inference of true direct relationships in a network. T, so this paper sought to develop a quantitative framework, termed network deconvolution (ND), to infer direct relationships and remove false positives in a network by quantifying and then removing indirect transitive relationship effects. The mathematical framework assumes that (1) an indirect relationship (edge) can be approximated as the product of its component direct edges and that (2) the observed edge weights are the sum of the direct and indirect edge weights – a linear dependency. The main application seems to be in mutual information (MI) and
correlation-based (COR) networks. They applied ND to various scenarios such as local network connectivity prediction (FFL
prediction), gene regulatory network prediction (in E. coli), prediction of interacting amino acids in protein structures (MI network) and coauthorship relationship network and found that (1) it can be used with various networks beyond just MI and COR (2) it can be used alone or more powerfully in combination with existing
methods/algorithms to improve predictions. In a sense it is the opposite of clique and module completion approaches (such as k-core).

http://www.nature.com/ni/journal/v14/n8/full/ni.2647.html

The gist is that it is important for protein folding to choose optimal and non-optimal synonymous codons, at different locations.
http://www.nature.com/nsmb/journal/v20/n2/full/nsmb.2466.html

An interesting paper! Nice to see codon usage revisited again. Another revisiting of codons (my own) is at
http://papers.gersteinlab.org/papers/revisit-cai

Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding
Sebastian Pechmann & Judith Frydman
Nature Structural & Molecular Biology 20, 237–243 (2013) doi:10.1038/nsmb.2466

Appears to use the data set

Cell, Volume 154, Issue 3, 1 August 2013, Pages 518–529
Spatial and Temporal Mapping of De Novo Mutations in Schizophrenia to a Fetal Prefrontal Cortical Network
http://www.sciencedirect.com/science/article/pii/S0092867413008313#

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.2651.html

Network-based stratification of tumor mutations

Matan Hofree,
John P Shen,
Hannah Carter,
Andrew Gross
& Trey Ideker

Nature Methods(2013)doi:10.1038/nmeth.2651

#Variation prefers the golden mean: Moderate selection involves many loci v weak & strong, few http://bit.ly/18MgU6p via @drbachinsky
Moderate selection, many loci; Weak or strong selection, few loci.
http://blogs.discovermagazine.com/gnxp/2013/09/distributed-variation-prefers-the-golden-mean/#.UjJTHmSG1MF