Month: October 2015

TransVar

[PMID:26513549] [Nature Methods]

TransVar: a multilevel variant annotator for precision genomics

It looks like a pretty promising variant annotator. I would like to give it a try someday.  It has both web interface and stand-alone versions. “to perform three main functions supporting diverse reference genomes and transcript databases: (i) forward annotation, which annotates all potential effects of a genomic variant on mRNAs and proteins; (ii) reverse annotation, which traces an mRNA or protein variant to all potential genomic origins; and (iii) equivalence annotation, which, for a given protein variant, searches for alternative protein variants that have an identical genomic origin but are represented on the basis of different isoforms.”

Exosome-driven Metastasis

[PMID:26524530] [Nature]

Tumour exosome integrins determine organotropic metastasis

“Ever since Stephen Paget’s 1889 hypothesis, metastatic organotropism has remained one of cancer’s greatest mysteries. Here we demonstrate that exosomes from mouse and human lung-, liver- and brain-tropic tumour cells fuse preferentially with resident cells at their predicted destination, namely lung fibroblasts and epithelial cells, liver Kupffer cells and brain endothelial cells. We show that tumour-derived exosomes uptaken by organ-specific cells prepare the pre-metastatic niche.”

VariantMetaCaller

[PMID:26510841] [BMC Bioinformatics]

VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering

“the automated fusion of measurement related information allows better performance than the recommended hard-filtering settings or recalibration and the fusion of the individual call sets without using annotations.” “This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, ranging from a few hundred kilobases to whole exomes.”

RNA-Seq Normalization

[PMID:26176014] [BioMed Research International]

The Impact of Normalization Methods on RNA-Seq Data Analysis

“we suggest the application of the following workflow to determine which normalization method is optimal for a specific data set: (i) normalize the data using considered methods, (ii) calculate the “bias” and “variance” and rank the methods based on these values, (iii) after each normalization perform differential analysis and determine DEG lists found by each normalization method, (iv) select a subset of genes that can serve as positive and negative controls to investigate the sensitivity and specificity of normalization methods and rank the methods based on these criteria, (v) calculate the percentage of the mean of the prediction errors obtained using chosen classifiers for DEGs found by each normalization method and rank them, (vi) draw Venn diagrams or balloon plots based on the number of differentially expressed genes and rank the methods based on the number of common DEG values, and (vii) based on the summary of ranks choose the most appropriate normalization method of the investigated data set.”

See also the article today @BMC Bioinformatics [], Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data, which “compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish).” “Spearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.”