Month: October 2016


[PMID:27789693] [Nucleic Acids Research]

3DSNP: a database for linking human noncoding SNPs to their three-dimensional interacting genes

“an integrated database for annotating human noncoding variants by exploring their roles in the distal interactions between genes and regulatory elements. 3DSNP integrates 3D chromatin interactions, local chromatin signatures in different cell types and linkage disequilibrium (LD) information from the 1000 Genomes Project. 3DSNP provides informative visualization tools to display the integrated local and 3D chromatin signatures and the genetic associations among variants.”


MPEG HTS Compression Working Group

[PMID:27776113] [Nature Methods]

Comparison of high-throughput sequencing data compression tools

“to evaluate all available approaches used for HTS data compression. These approaches include both industry-scale tools as well as research-oriented prototypes. Compression performance, running times, memory usage and parallelization capabilities were measured for each tool.”


[] [Nature Communications]

Pan-cancer transcriptomic analysis associates long non-coding RNAs with key mutational driver events

“Here we make use of transcriptome and exome sequencing data from thousands of tumours across 19 cancer types, to identify lncRNAs that are induced or repressed in relation to somatic mutations in key oncogenic driver genes.” Complete lncRNA expression profiles across 7,295 tumours can be downloaded from the Supplementary Data 7.


[PMID:27776117] [Nature Genetics]

M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity

“The Mendelian Clinically Applicable Pathogenicity (M-CAP) score is a pathogenicity likelihood score that aims to misclassify no more than 5% of pathogenic variants while aggressively reducing the list of variants of uncertain significance. Much like allele frequency, M-CAP is readily interpreted; if it classifies a variant as benign, then that variant can be trusted to be benign with high confidence. M-CAP uses gradient boosting trees, a supervised learning classifier that excels at analyzing nonlinear interactions between features, and has state-of-the-art performance in a variety of classification tasks. The features M-CAP uses for classification are based on both existing pathogenicity likelihood scores and direct measures of evolutionary conservation, the cross-species analog to frequency within the human population.””We developed M-CAP, a clinical pathogenicity classifier that outperforms existing methods at all thresholds and correctly dismisses 60% of rare, missense variants of uncertain significance in a typical genome at 95% sensitivity.”


[PMID:27732578] [Nature]

A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns

Chromothripsis and polyploidizationin Pancreatic Cancer.  “To evaluate polyploidization, we developed and validated a new informatic tool, termed CELLULOID, which estimates tumour ploidy and copy number from whole-genome data.” “We developed a sensitive algorithm, termed ChromAL, to differentiate chromothripsis from localized gradual events that accumulate over time.” BOth CELLULOID and ChromAL are implemented in R. “Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory.”