IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses

Abstract

Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1116-8) contains supplementary material, which is available to authorized users.

YAMP: a containerised workflow enabling reproducibility in metagenomics research

“…On the other hand, ensuring reproducibility does not come for free: anecdotic evidence suggests that the time spent on a project may increase by 30-50% [1], and that to reproduce the analysis of single computational biology paper can require up to 280 hours [57]. YAMP, along with other containarised workflows, such as the Integrated Meta-omic Pipeline (IMP) [58] and Bio-Docklets [59], represents a proof-of-concept showing a simple way to enable reproducible and collaborative research. We also advocate the sharing of such containerised workflows, which will benefit a wide group of researchers, regardless of their computational experience [11].…”

System for Quality‐Assured Data Analysis: Flexible, reproducible scientific workflows

“…Whether the sole use of CLI is a strength or a weakness is a matter of taste, but adding a web interface would raise the height of the software dependency stack greatly, adding to system complexity, not only increasing the chance of bugs but also introducing security concerns that are inherent in web applications. 1 SyQADA has made no direct attempt to address "cloud" computing in contrast to Galaxy, IMP, and Omics Pipe (Fisch et al, 2015;Goecks et al, 2010;Narayanasamy et al, 2016), among others. Those with institutional access to large-scale computing resources may not yet have encountered a need for this.…”

“…There are several related systems for managing bioinformatics workflows; from an analyst's perspective, they fall into the following four categories based on human interface: Command‐line interface (CLI) Bpipe (Sadedin, Pope & Oshlack, ), NGSANE (Buske, French, Smith, Clark & Bauer, ), Omics Pipe (Fisch et al, ); Standalone application with graphic interface (app) NEAT (Schorderet, ), Chipster (Kallio et al, ), GenePattern (Kuehn, Liberzon, Reich & Mesirov, ); Web application (webapp) Galaxy (Goecks et al, ), IMP (Narayanasamy et al, ); Application program interface (API) GATK (McKenna et al, ), Queue (Shakir, ), Omics Pipe (Fisch et al, ), Ruffus (Goodstadt, ). SyQADA is a CLI system whose only dependencies are a Unix operating system and a standard installation of Python 3.5 (or higher). This simplicity by design is shared by Bpipe, NGSANE and to some extent by Omics Pipe.…”

“…Command-line interface (CLI) Bpipe (Sadedin, Pope & Oshlack, 2012), NGSANE (Buske, French, Smith, Clark & Bauer, 2014), Omics Pipe (Fisch et al, 2015); Standalone application with graphic interface (app) NEAT (Schorderet, 2016), Chipster (Kallio et al, 2011), GenePattern (Kuehn, Liberzon, Reich & Mesirov, 2008); Web application (webapp) Galaxy (Goecks et al, 2010), IMP (Narayanasamy et al, 2016); Application program interface (API) GATK (McKenna et al, 2010), Queue (Shakir, 2011), Omics Pipe (Fisch et al, 2015), Ruffus (Goodstadt, 2010).…”

ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data

“…A number of assembly-based metagenome pipelines have been developed, each providing a subset of the required tools needed to carry out a complete analysis process from raw data to annotated genomes (14)(15)(16)(17). For example, MOCAT (16) relies on gene catalogs to evaluate the functional potential of the metagenome as a whole, but without directly relating functions to individual microbes.…”