MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data

Area of Science:

• Bioinformatics
• Microbiology
• Genomics

Background:

• Big data and high-performance computing facilitate large-scale re-analysis of public datasets.
• The microbiome's role in human physiology and disease is increasingly recognized.
• RNA sequencing (RNA-seq) can detect eukaryotic gene expression and microbial/viral transcripts.

Purpose of the Study:

• To develop and validate a bioinformatic pipeline for detecting microbial and viral reads in human RNA-seq datasets.
• To create a comprehensive resource (MetaMap) of microbial and viral signatures across numerous human disease studies.

Main Methods:

• Developed a pipeline to analyze the non-human-mapping fraction of RNA-seq reads.
• Validated the pipeline using controlled infection experiments and compared it to metatranscriptomic strategies.
• Applied the pipeline to ~150 TB of public RNA-seq data from >17,000 samples across >400 studies.

Main Results:

• Successfully recapitulated infection experiment outcomes, demonstrating pipeline efficacy.
• Identified microbial and viral signatures in a large cohort of human RNA-seq data.
• Compiled findings into the MetaMap database, a novel resource for disease-associated microbial/viral detection.

Conclusions:

• Human RNA-seq data contains valuable information for correlating microbial/viral detection with diseases.
• The MetaMap database serves as a rich resource for generating hypotheses on the microbiome's role in human disease.
• Associated code for data processing and statistical analysis is publicly available.