Dataset Information

Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics.

ABSTRACT: Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing real and simulated targeted metagenomics datasets, and adequate metrics allowing us to study the impact of different variables on the biological interpretation of results. This protocol was used to compare six different bioinformatics pipelines in the basic user context: Three common ones (mothur, QIIME and BMP) based on a clustering-first approach and three emerging ones (Kraken, CLARK and One Codex) using an assignment-first approach. This study surprisingly reveals that the effect of sequencing errors has a bigger impact on the results that choosing different amplified regions. Moreover, increasing sequencing throughput increases richness overestimation, even more so for microbiota of high complexity. Finally, the choice of the reference database has a bigger impact on richness estimation for clustering-first pipelines, and on correct taxa identification for assignment-first pipelines. Using emerging assignment-first pipelines is a valid approach for targeted metagenomics analyses, with a quality of results comparable to popular clustering-first pipelines, even with an error-prone sequencing technology like Ion Torrent. However, those pipelines are highly sensitive to the quality of databases and their annotations, which makes clustering-first pipelines still the only reliable approach for studying microbiomes that are not well described.

SUBMITTER: Siegwald L

PROVIDER: S-EPMC5215245 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics.

Siegwald Léa L Touzet Hélène H Lemoine Yves Y Hot David D Audebert Christophe C Caboche Ségolène S

PloS one 20170104 1

Targeted metagenomics, also known as metagenetics, is a high-throughput sequencing application focusing on a nucleotide target in a microbiome to describe its taxonomic content. A wide range of bioinformatics pipelines are available to analyze sequencing outputs, and the choice of an appropriate tool is crucial and not trivial. No standard evaluation method exists for estimating the accuracy of a pipeline for targeted metagenomics analyses. This article proposes an evaluation protocol containing ...[more]

PMID: 28052134

Similar Datasets

Project description:BACKGROUND: Bioinformatic analyses typically proceed as chains of data-processing tasks. A pipeline, or 'workflow', is a well-defined protocol, with a specific structure defined by the topology of data-flow interdependencies, and a particular functionality arising from the data transformations applied at each step. In computer science, the dataflow programming (DFP) paradigm defines software systems constructed in this manner, as networks of message-passing components. Thus, bioinformatic workflows can be naturally mapped onto DFP concepts. RESULTS: To enable the flexible creation and execution of bioinformatics dataflows, we have written a modular framework for parallel pipelines in Python ('PaPy'). A PaPy workflow is created from re-usable components connected by data-pipes into a directed acyclic graph, which together define nested higher-order map functions. The successive functional transformations of input data are evaluated on flexibly pooled compute resources, either local or remote. Input items are processed in batches of adjustable size, all flowing one to tune the trade-off between parallelism and lazy-evaluation (memory consumption). An add-on module ('NuBio') facilitates the creation of bioinformatics workflows by providing domain specific data-containers (e.g., for biomolecular sequences, alignments, structures) and functionality (e.g., to parse/write standard file formats). CONCLUSIONS: PaPy offers a modular framework for the creation and deployment of parallel and distributed data-processing workflows. Pipelines derive their functionality from user-written, data-coupled components, so PaPy also can be viewed as a lightweight toolkit for extensible, flow-based bioinformatics data-processing. The simplicity and flexibility of distributed PaPy pipelines may help users bridge the gap between traditional desktop/workstation and grid computing. PaPy is freely distributed as open-source Python code at http://muralab.org/PaPy, and includes extensive documentation and annotated usage examples.

Project description:BACKGROUND:Automated bioinformatics workflows are more robust, easier to maintain, and results more reproducible when built with command-line utilities than with custom-coded scripts. Command-line utilities further benefit by relieving bioinformatics developers to learn the use of, or to interact directly with, biological software libraries. There is however a lack of command-line utilities that leverage popular Open Source biological software toolkits such as BioPerl ( http://bioperl.org ) to make many of the well-designed, robust, and routinely used biological classes available for a wider base of end users. RESULTS:Designed as standard utilities for UNIX-family operating systems, BpWrapper makes functionality of some of the most popular BioPerl modules readily accessible on the command line to novice as well as to experienced bioinformatics practitioners. The initial release of BpWrapper includes four utilities with concise command-line user interfaces, bioseq, bioaln, biotree, and biopop, specialized for manipulation of molecular sequences, sequence alignments, phylogenetic trees, and DNA polymorphisms, respectively. Over a hundred methods are currently available as command-line options and new methods are easily incorporated. Performance of BpWrapper utilities lags that of precompiled utilities while equivalent to that of other utilities based on BioPerl. BpWrapper has been tested on BioPerl Release 1.6, Perl versions 5.10.1 to 5.25.10, and operating systems including Apple macOS, Microsoft Windows, and GNU/Linux. Release code is available from the Comprehensive Perl Archive Network (CPAN) at https://metacpan.org/pod/Bio::BPWrapper . Source code is available on GitHub at https://github.com/bioperl/p5-bpwrapper . CONCLUSIONS:BpWrapper improves on existing sequence utilities by following the design principles of Unix text utilities such including a concise user interface, extensive command-line options, and standard input/output for serialized operations. Further, dozens of novel methods for manipulation of sequences, alignments, and phylogenetic trees, unavailable in existing utilities (e.g., EMBOSS, Newick Utilities, and FAST), are provided. Bioinformaticians should find BpWrapper useful for rapid prototyping of workflows on the command-line without creating custom scripts for comparative genomics and other bioinformatics applications.

Project description:NASA planetary protection (PP) requires an assessment of the biological contamination of the potential microbial burden on spacecraft destined to explore planetary bodies that may harbor signs of life, like Mars and Europa. To help meet these goals, the performance of multiple metagenomic pipelines were compared and assessed for their ability to detect microbial diversity of a low-biomass clean room environment used to build spacecraft destined to these planetary bodies. Four vendors were chosen to implement their own metagenomic analysis pipeline on the shotgun sequences retrieved from environmental surfaces in the relevant environments at NASA's Jet Propulsion Laboratory. None of the vendors showed the same microbial profile patterns when analyzing same raw dataset since each vendor used different pipelines, which begs the question of the validity of a single pipeline to be recommended for future NASA missions. All four vendors detected species of interest, including spore-forming and extremotolerant bacteria, that have the potential to hitch-hike on spacecraft and contaminate the planetary bodies explored. Some vendors demonstrated through functional analysis of the metagenomes that the molecular mechanisms for spore-formation and extremotolerance were represented in the data. However, relative abundances of these microorganisms varied drastically between vendor analyses, questioning the ability of these pipelines to quantify the number of PP-relevant microorganisms on a spacecraft surface. Metagenomics offers tantalizing access to the genetic and functional potential of a microbial community that may offer NASA a viable method for microbial burden assays for planetary protection purposes. However, future development of technologies such as streamlining the processing of shotgun metagenome sequence data, long read sequencing, and all-inclusive larger curated and annotated microbial genome databases will be required to validate and translate relative abundances into an actionable assessment of PP-related microbes of interest. Additionally, the future development of machine learning and artificial intelligence techniques could help enhance the quality of these metagenomic analyses by providing more accurate identification of the genetic and functional potential of a microbial community.

Dataset Information

Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics.

Publications

Assessment of Common and Emerging Bioinformatics Pipelines for Targeted Metagenomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure