Literature DB >> 33422152

Re-purposing software for functional characterization of the microbiome.

Laura-Jayne Gardiner1, Niina Haiminen2, Filippo Utro2, Laxmi Parida2, Ed Seabolt3, Ritesh Krishna4, James H Kaufman3.   

Abstract

BACKGROUND: Widespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit.
RESULTS: To facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights.
CONCLUSIONS: We demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development. Video abstract.

Entities:  

Keywords:  Functional analysis; Microbiome; Sequencing read classification; Taxonomy

Mesh:

Year:  2021        PMID: 33422152      PMCID: PMC7797099          DOI: 10.1186/s40168-020-00971-1

Source DB:  PubMed          Journal:  Microbiome        ISSN: 2049-2618            Impact factor:   14.650


  33 in total

1.  Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

Authors:  M Ashburner; C A Ball; J A Blake; D Botstein; H Butler; J M Cherry; A P Davis; K Dolinski; S S Dwight; J T Eppig; M A Harris; D P Hill; L Issel-Tarver; A Kasarskis; S Lewis; J C Matese; J E Richardson; M Ringwald; G M Rubin; G Sherlock
Journal:  Nat Genet       Date:  2000-05       Impact factor: 38.330

2.  Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences.

Authors:  Ashok K Sharma; Ankit Gupta; Sanjiv Kumar; Darshan B Dhakan; Vineet K Sharma
Journal:  Genomics       Date:  2015-04-08       Impact factor: 5.736

Review 3.  A clinician's guide to microbiome analysis.

Authors:  Marcus J Claesson; Adam G Clooney; Paul W O'Toole
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2017-08-09       Impact factor: 46.802

4.  MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data.

Authors:  Daniel H Huson; Sina Beier; Isabell Flade; Anna Górska; Mohamed El-Hadidi; Suparna Mitra; Hans-Joachim Ruscheweyh; Rewati Tappu
Journal:  PLoS Comput Biol       Date:  2016-06-21       Impact factor: 4.475

5.  KrakenUniq: confident and fast metagenomics classification using unique k-mer counts.

Authors:  F P Breitwieser; D N Baker; S L Salzberg
Journal:  Genome Biol       Date:  2018-11-16       Impact factor: 13.583

6.  BLAST: a more efficient report with usability improvements.

Authors:  Grzegorz M Boratyn; Christiam Camacho; Peter S Cooper; George Coulouris; Amelia Fong; Ning Ma; Thomas L Madden; Wayne T Matten; Scott D McGinnis; Yuri Merezhuk; Yan Raytselis; Eric W Sayers; Tao Tao; Jian Ye; Irena Zaretskaya
Journal:  Nucleic Acids Res       Date:  2013-04-22       Impact factor: 16.971

7.  Kraken: ultrafast metagenomic sequence classification using exact alignments.

Authors:  Derrick E Wood; Steven L Salzberg
Journal:  Genome Biol       Date:  2014-03-03       Impact factor: 13.583

8.  Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics.

Authors:  Juan Jovel; Jordan Patterson; Weiwei Wang; Naomi Hotte; Sandra O'Keefe; Troy Mitchel; Troy Perry; Dina Kao; Andrew L Mason; Karen L Madsen; Gane K-S Wong
Journal:  Front Microbiol       Date:  2016-04-20       Impact factor: 5.640

9.  SUPER-FOCUS: a tool for agile functional analysis of shotgun metagenomic data.

Authors:  Genivaldo Gueiros Z Silva; Kevin T Green; Bas E Dutilh; Robert A Edwards
Journal:  Bioinformatics       Date:  2015-10-09       Impact factor: 6.937

10.  MetAnnotate: function-specific taxonomic profiling and comparison of metagenomes.

Authors:  Pavel Petrenko; Briallen Lobb; Daniel A Kurtz; Josh D Neufeld; Andrew C Doxey
Journal:  BMC Biol       Date:  2015-11-05       Impact factor: 7.431

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  1 in total

1.  Functional profiling of COVID-19 respiratory tract microbiomes.

Authors:  Niina Haiminen; Filippo Utro; Ed Seabolt; Laxmi Parida
Journal:  Sci Rep       Date:  2021-03-19       Impact factor: 4.379

  1 in total

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