Month: May 2016

PredictSNP2

[PMID:27224906] [PLoS Computational Biology]

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

“Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score.”

For Whom the Bell Tolls

[PMID:27212078] [Scientific Reports]

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

“all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30–40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention.”

HiBS

[PMID:26809676] [Nucleic Acids Research]

Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation

“We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification.” “The three major advantages of HiBS are: first, the read species based method helps in decisions regarding the tumor/normal source of the sample using only the structure of the data. Second, the algorithm is an extremely sensitive method of identifying specific amplification/deletion loci, making it suitable for loci targeting approaches. Third, the algorithm can be easily integrated within a tumor staging system to assist in diagnosis. “