MetaPro: A scalable and reproducible data processing and analysis pipeline for metatranscriptomic investigation of microbial communities

Abstract

Background Whole microbiome RNASeq (metatranscriptomics) has emerged as a powerful technology to functionally interrogate microbial communities. A key challenge is how best to process, analyze and interpret these complex datasets. In a typical application, a single metatranscriptomic dataset may comprise from tens to hundreds of millions of sequence reads. These reads must first be processed and filtered for low quality and potential contaminants, before being annotated with taxonomic and functional labels and subsequently collated to generate global bacterial gene expression profiles. Results Here we present MetaPro, a flexible, massively scalable metatranscriptomic data analysis pipeline that is cross-platform compatible through its implementation within a Docker framework. MetaPro starts with raw sequence read input (single end or paired end reads) and processes them through a tiered series of filtering, assembly and annotation steps. In addition to yielding a final list of bacterial genes and their relative expression, MetaPro delivers a taxonomic breakdown based on the consensus of complementary prediction algorithms, together with a focused breakdown of enzymes, readily visualized through the Cytoscape network visualization tool. We benchmark the performance of MetaPro against two current state of the art pipelines and demonstrate improved performance and functionality. Conclusion MetaPro represents an effective integrated solution for the processing and analysis of metatranscriptomic datasets. Its modular architecture allows new algorithms to be deployed as they are developed, ensuring its longevity. To aid user uptake of the pipeline, MetaPro, together with an established tutorial that has been developed for educational purposes is made freely available at https://github.com/ParkinsonLab/MetaPro. The software is freely available under the GNU general public license v3.

The commensal protistTritrichomonas musculusexhibits a dynamic life cycle that induces extensive remodeling of the gut microbiota

“…Sequencing libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kits for Illumina (New England Biolabs, Ipswich, MA, USA) and paired-end sequenced (150 bp) on the NovaSeq 6000 platform (Illumina, San Diego, CA, USA) at Genome Quebec (Montréal, Canada). Low quality reads, host and vector contamination, rRNA and tRNA reads were filtered out using the MetaPro pipeline 125 . Remaining reads were mapped to the T. mu genome assembly using STAR v. 2.5.3a 108 , and all unmapped reads were returned to MetaPro for contig assembly, gene model prediction and functional and taxonomic annotation.…”

“…Reads were processed using the MetaPro pipeline. High quality reads were mapped to the T. mu genome assembly using STAR v. 2.5.3a, and all remaining unmapped reads were returned to MetaPro for functional and taxonomic annotation 39,56 . Proteins with iron-related functions were predicted using FeGenie 57 .…”

Elucidating the Dynamics and Impact of the Gut Microbiome on Maternal Nutritional Status During Pregnancy in Rural Pakistan: Study Protocol for a Prospective, Longitudinal Observational Study

“…After total RNA extraction and rRNA depletion (RiboZero Gold Kit, Illumina, San Diego, Ca, or equivalent), libraries will be constructed and Illumina-based sequencing will be performed to generate ∼30 million 2x150bp paired-end reads per sample (our rarefaction analyses have previously shown such sequencing depth is sufficient to identify the vast majority of species and enzymes present in the samples [63]). Reads will be processed for quality and contaminants using the MetaPro pipeline [64]. Reads will be assembled using SPAdes [65] and subsequently annotated with taxonomic and functional assignments.…”

E.coli Nissle increases transcription of flagella assembly and formate hydrogenlyase genes in response to colitis

“… 17 Taxonomic classification of reads was assigned by processing paired end FASTQ reads of each mouse stool sample using the MetaPro pipeline under the default settings. 18 Low-quality sequences were filtered, followed by host and vector removal through BWA alignment, then rRNA removal was completed using Infernal. From our initial analyses, we identified control mouse 4 as being a clear outlier (Figure S1(c,d)), which, with the previously mentioned problems, was therefore removed from subsequent analyses.…”