linkstream2 microblog

structures for team science
http://www.pnas.org/content/111/11/3984.full

GWAS identifies 74 loci associated w. educational attainment http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17671.html Described in 3 pgs of main text & 146 pgs of supplement

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17671.html

http://www.cell.com/cell/abstract/S0092-8674(15)01127-7?_returnURL=http%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS0092867415011277%3Fshowall%3Dtrue

Genomic & microenvironmental heterogeneity as integrated predictors for prostate #cancer recurrence
http://www.ncbi.nlm.nih.gov/pubmed/25456371 CNVs & hypoxia

* Lalonde E*, Ishkanian AS*, ….P’ng C, Collins CC, Squire JA, Jurisica I, Cooper C, Eeles R, Pintilie M, Dal Pra A, Davicioni E, Lam WL, Milosevic M, Neal DE, van der Kwast T, Boutros PC, Bristow RG (2014) “Tumour genomic and microenvironmental heterogeneity as integrated predictors for prostate cancer recurrence: a retrospective study” Lancet Oncology 15(13):1521-1532 (PMID: 25456371)

The novelty of the paper is that it is the first study integrating DNA-based signatures and microenviroment-based signature for cancer prognosis. The authors found four prognostic indices, i.e. cancer genomic subtype (generated from clusters of CNV profiles), genomic instability (represented by the percentage of genome alteration), DNA signature (276 genes identified from random forests), and tumor hypoxia (the microenvironment signature), to be effective in predicting patient survival in different groups. Standard clinical univariate and multivariate analyses were performed.

Timing, rates & spectra of human germline mutation
http://www.nature.com/ng/journal/v48/n2/full/ng.3469.html Metaanalysis of >6500 events gives a de novo mutational signature

http://www.ncbi.nlm.nih.gov/pubmed/25837513

http://bioinformatics.oxfordjournals.org/content/30/21/3004.long

Cell-lineage analysis in human #brain using endogenous retroelements http://www.cell.com/neuron/abstract/S0896-6273(14)01137-4 Tracing L1 insertions w/ #singlecell sequencing

Using single cell WGS of 16 neuronal cells the authors investigated two somatic insertions of L1Hs elements in an adult human brain. Using these results the authors infer that L1 somatic insertions are infrequent and ALUs and SVAs somatic retrotransposition are extremely rare. Assessing two L1Hs insertions in 32 samples across different regions of this same adult brain, they found that while one insertion was spatially restricted (2x1cm region), the other was found across all samples of the adult brain (but not found in other tissues such as Heart, Lung, etc.). The more restricted one (L1Hs#1) is inferred to have happened during the Fetal stage (first trimester) while the broader one happened earlier, approximately 2 weeks
post-fertilization. Overall the paper is clear, concise, and simple. It answers an interesting biological question: Can retrotransposition be used as a marker of cell clonal expansion? It does, although the retrotransposition frequency is very small and SNVs might support better results for the same analysis due to their higher frequency..

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

Learning the…Determinants of Alternative #Splicing [in a largely linear model] from Millions of Random Sequences
http://www.cell.com/cell/abstract/S0092-8674(15)01271-4

** Rosenberg et al Cell. 2015

Builds a model of splicing using a library of randomized sequence Also, builds a generalized model for predicting effect of a SNP in the Geuvadis RNAseq
7mer model does well with lots of data