MetaTrans: an open-source pipeline for metatranscriptomics

Abstract

To date, meta-omic approaches use high-throughput sequencing technologies, which produce a huge amount of data, thus challenging modern computers. Here we present MetaTrans, an efficient open-source pipeline to analyze the structure and functions of active microbial communities using the power of multi-threading computers. The pipeline is designed to perform two types of RNA-Seq analyses: taxonomic and gene expression. It performs quality-control assessment, rRNA removal, maps reads against functional databases and also handles differential gene expression analysis. Its efficacy was validated by analyzing data from synthetic mock communities, data from a previous study and data generated from twelve human fecal samples. Compared to an existing web application server, MetaTrans shows more efficiency in terms of runtime (around 2 hours per million of transcripts) and presents adapted tools to compare gene expression levels. It has been tested with a human gut microbiome database but also proposes an option to use a general database in order to analyze other ecosystems. For the installation and use of the pipeline, we provide a detailed guide at the following website (www.metatrans.org).

Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length Markov Chains

“…Using a similar simulation strategy as developed in Martinez et al 11, we simulated three groups of synthetic mock communities with different expression levels using Polyester12, an RNA-Seq simulation tool. Five most abundant microbial genomes in human gut were selected based on Qin et al 29: Bacteroides vulgatus ATCC 8482 , Ruminococcus torques L2−14 , Faecalibacterium prausnitzii SL3/3 , Bacteroides thetaiotaomicron VPI-5482 and Parabacteroides distasonis ATCC 8503 .…”

“…Among the limited metatranscriptomic analytic tools, some were designed for Illumina paired-end10 or single/paired-end data11, or only used to evaluate the gene expression level12. A previous study11 compared four taxonomical classification tools based on a common metatranscriptomic data and obvious differences among the taxonomical analytic results were observed, which was the second figure in original paper11. (3) Sequence assembly is time-consuming and challenging especially for metagenome/metatranscriptome when organisms share a high volume of homologous sequences.…”

Host-Microbiome Interaction and Cancer: Potential Application in Precision Medicine

“…These analyses can be performed using locally installed software; alternatively, for users with no bioinformatic training, there are different suites for analysis, such as MG-RAST (Wilke et al, 2016), IMG/M (Markowitz et al, 2012; Wilke et al, 2016), JCVI and Metagenomics Reports (METAREP) (Goll et al, 2010; Markowitz et al, 2012; Wilke et al, 2016) or MEGAN (Huson and Weber, 2013), MetAMOS (Treangen et al, 2013), MOCAT2 (Kultima et al, 2016), and MetaTrans (Martinez et al, 2016) which are software designed to simplify all metagenomics or metatranscriptomics pipeline; preprocessing, assembly, annotation and analysis.…”

To assemble or not to resemble – A validated Comparative Metatranscriptomics Workflow (CoMW)

“…Jung et al [8], aligning short reads to reference genomes of lactic acid bacterial strains associated with the kimchi microbial community. Similarly, an open source pipeline developed by Martinez et al [9] to analyse metatranscriptomics data-sets also aligns short reads directly to a protein database before annotation. The choice of either of these two alternatives for metatranscriptomics analyses may depend on lack of thorough comparisons.…”