Comparative transcriptomics analysis pipeline for the meta-analysis of phylogenetically divergent datasets (CoRMAP).

BACKGROUND: Transcriptional regulation is a fundamental mechanism underlying biological functions. In recent years, a broad array of RNA-Seq tools have been used to measure transcription levels in biological experiments, in whole organisms, tissues, and at the single cell level. Collectively, this is a vast comparative dataset on transcriptional processes across organisms. Yet, due to technical differences between the studies (sequencing, experimental design, and analysis) extracting usable comparative information and conducting meta-analyses remains challenging. RESULTS: We introduce Comparative RNA-Seq Metadata Analysis Pipeline (CoRMAP), a meta-analysis tool to retrieve comparative gene expression data from any RNA-Seq dataset using de novo assembly, standardized gene expression tools and the implementation of OrthoMCL, a gene orthology search algorithm. It employs the use of orthogroup assignments to ensure the accurate comparison of gene expression levels between experiments and species. Here we demonstrate the use of CoRMAP on two mouse brain transcriptomes with similar scope, that were collected several years from each other using different sequencing technologies and analysis methods. We also compare the performance of CoRMAP with a functional mapping tool, previously published. CONCLUSION: CoRMAP provides a framework for the meta-analysis of RNA-Seq data from divergent taxonomic groups. This method facilitates the retrieval and comparison of gene expression levels from published data sets using standardized assembly and analysis. CoRMAP does not rely on reference genomes and consequently facilitates direct comparison between diverse studies on a range of organisms.

Are Next-Generation Pathogenicity Predictors Applicable to Cancer?

Next-generation pathogenicity predictors are designed to identify pathogenic mutations in genetic disorders but are increasingly used to detect driver mutations in cancer. Despite this, their suitability for cancer is not fully established. Here we have assessed the effectiveness of next-generation pathogenicity predictors when applied to cancer by using a comprehensive experimental benchmark of cancer driver and neutral mutations. Our findings indicate that state-of-the-art methods AlphaMissense and VARITY demonstrate commendable performance despite generally underperforming compared to cancer-specific methods. This is notable considering that these methods do not explicitly incorporate cancer-related data in their training and have made concerted efforts to prevent data leakage from the human-curated training and test sets. Nevertheless, it should be mentioned that a significant limitation of using pathogenicity predictors for cancer arises from their inability to detect cancer potential driver mutations specific for a particular cancer type.