MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data

While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis. Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available fromhttps://github.com/ablab/metaGT.

Automated summary

While metagenome sequencing provides insights on microbial communities, metatranscriptome analysis is useful for studying functional activity. However, the complexity, dynamic range, and lack of computational methods for assembling metatranscriptomes pose challenges. This study presents MetaGT, a pipeline that combines metatranscriptomic and metagenomic data to improve assembly coverage and completeness. Results show significant improvement compared to existing methods. The pipeline, implemented in NextFlow, is available athttps://github.com/ablab/metaGT.