Month: March 2016

RNA-seq Biological Replicates

[PMID:27022035] [RNA]

How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use

“more than six biological replicates should be used, rising to more than 12 when it is important to identify SDE genes for all fold changes. If less than 12 replicates are used, a superior combination of true positive and false positive performances makes edgeR the leading tool. For higher replicate numbers, minimizing false positives is more important and DESeq marginally outperforms the other tools.”

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Somatic Variant Callers

[PMID:27002637] [PLoS ONE]

Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data

“a comprehensive evaluation using exome sequencing and targeted deep sequencing data of paired tumor-normal samples from five breast cancer patients to evaluate the performance of nine publicly available somatic variant callers: EBCall, Mutect, Seurat, Shimmer, Indelocator, Somatic Sniper, Strelka, VarScan 2 and Virmid for the detection of single nucleotide mutations and small deletions and insertions.” “Sequencing depth had markedly diverse impact on individual callers, as for some callers, increased sequencing depth highly improved sensitivity. For SNV calling, we report EBCall, Mutect, Virmid and Strelka to be the most reliable somatic variant callers for both exome sequencing and targeted deep sequencing. For indel calling, EBCall is superior due to high sensitivity and robustness to changes in sequencing depths.”

dbWGFP

[PMID:26989155] [Database]

dbWGFP: a database and web server of human whole-genome single nucleotide variants and their functional predictions

“dbWGFP (a database and web server of human whole-genome single nucleotide variants and their functional predictions) that contains functional predictions and annotations of nearly 8.58 billion possible human whole-genome single nucleotide variants. Specifically, this database integrates 48 functional predictions calculated by 17 popular computational methods and 44 valuable annotations obtained from various data sources.”

RNA-seq Fusion Meta-caller

[PMID:26582927] [Nucleic Acids Research]

Comprehensive evaluation of fusion transcript detection algorithms and a meta-caller to combine top performing methods in paired-end RNA-seq data

“Fifteen fusion transcript detection tools were compared using three synthetic data sets” “No single method dominantly performed the best but SOAPfuse generally performed well, followed by FusionCatcher and JAFFA.” “a meta-caller algorithm by combining top performing methods to re-prioritize candidate fusion transcripts with high confidence that can be followed by experimental validation.”