Month: February 2016

Subtypes of Pancreatic Cancer

[PMID:26909576] [Nature]

Genomic analyses identify molecular subtypes of pancreatic cancer

“4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics.”



[PMID:26926108] [Nucleic Acids Research]

Robust classification of protein variation using structural modelling and large-scale data integration

“a computational framework that seamlessly integrates sequence analysis and structural modelling (using the Rosetta protein modelling suite) to identify and interpret deleterious protein variants.”


[PMID:26873929] [Bioinformatics]

Gene expression inference with deep learning

“Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ~1,000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression, limiting its accuracy since it does not capture complex nonlinear relationship between expression of genes.””a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes.”


[PMID:27153700] [Bioinformatics]

dbDSM: a manually curated database for deleterious synonymous mutations

“dbDSM (Database of deleterious synonymous mutation), a continually updated database that collects, curates and manages available human disease related SM data obtained from published literature. In the current release, dbDSM collects 1936 SM-disease association entries, including 1289 SMs and 443 human diseases from ClinVar, GRASP, GWAS Catalog, GWASdb, PolymiRTS database, PubMed database, and Web of Knowledge.”


[PMID:26861821] [Bioinformatics]

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

“an open-source R package that implements the state-of-the-art algorithms for the inference of cancer progression models from (epi)genomic mutational profiles. TRONCO can be used to extract population-level models describing the trends of accumulation of alterations in a cohort of cross-sectional samples, e.g., retrieved from publicly available databases, and individual-level models that reveal the clonal evolutionary history in single cancer patients, when multiple samples, e.g., multiple biopsies or single-cell sequencing data, are available.”