Meta4: a web application for sharing and annotating metagenomic gene predictions using web services

Richardson, Emily J.; Escalettes, Franck; Fotheringham, Ian; Wallace, Robert John; Watson, Michael

doi:10.3389/fgene.2013.00168

Cited by 8 publications

(7 citation statements)

References 31 publications

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“…Predicted proteins, and their domains, are available as a Meta4 database [ 37 ] at http://www.ark-genomics.org/tools/meta4…”

Section: Resultsmentioning

confidence: 99%

“…From the resulting scaffolds, microbial genes were predicted using Prokka [ 71 ], and compared to Pfam [ 35 ] using HMMER [ 72 ]. All protein predictions and annotations were uploaded to a Meta4 database [ 37 ]. We applied a P -value cut-off of 0.01 to the resulting domain predictions and counted the number of protein predictions which were assigned domains.…”

Section: Methodsmentioning

confidence: 99%

“…Predicted proteins, and their domains, are available as a Meta4 database [ 37 ] at http://www.ark-genomics.org/tools/meta4 . Raw data are available in the European Nucleotide Archive under accession PRJEB10338.…”

Section: Availability Of Supporting Datamentioning

confidence: 99%

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The rumen microbial metagenome associated with high methane production in cattle

et al. 2015

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BackgroundMethane represents 16 % of total anthropogenic greenhouse gas emissions. It has been estimated that ruminant livestock produce ca. 29 % of this methane. As individual animals produce consistently different quantities of methane, understanding the basis for these differences may lead to new opportunities for mitigating ruminal methane emissions. Metagenomics is a powerful new tool for understanding the composition and function of complex microbial communities. Here we have applied metagenomics to the rumen microbial community to identify differences in the microbiota and metagenome that lead to high- and low-methane-emitting cattle phenotypes.MethodsFour pairs of beef cattle were selected for extreme high and low methane emissions from 72 animals, matched for breed (Aberdeen-Angus or Limousin cross) and diet (high or medium concentrate). Community analysis was carried out by qPCR of 16S and 18S rRNA genes and by alignment of Illumina HiSeq reads to the GREENGENES database. Total genomic reads were aligned to the KEGG genes databasefor functional analysis.ResultsDeep sequencing produced on average 11.3 Gb per sample. 16S rRNA gene abundances indicated that archaea, predominantly Methanobrevibacter, were 2.5× more numerous (P = 0.026) in high emitters, whereas among bacteria Proteobacteria, predominantly Succinivibrionaceae, were 4-fold less abundant (2.7 vs. 11.2 %; P = 0.002). KEGG analysis revealed that archaeal genes leading directly or indirectly to methane production were 2.7-fold more abundant in high emitters. Genes less abundant in high emitters included acetate kinase, electron transport complex proteins RnfC and RnfD and glucose-6-phosphate isomerase. Sequence data were assembled de novo and over 1.5 million proteins were annotated on the subsequent metagenome scaffolds. Less than half of the predicted genes matched matched a domain within Pfam. Amongst 2774 identified proteins of the 20 KEGG orthologues that correlated with methane emissions, only 16 showed 100 % identity with a publicly available protein sequence.ConclusionsThe abundance of archaeal genes in ruminal digesta correlated strongly with differing methane emissions from individual animals, a finding useful for genetic screening purposes. Lower emissions were accompanied by higher Succinovibrionaceae abundance and changes in acetate and hydrogen production leading to less methanogenesis, as similarly postulated for Australian macropods. Large numbers of predicted protein sequences differed between high- and low-methane-emitting cattle. Ninety-nine percent were unknown, indicating a fertile area for future exploitation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2032-0) contains supplementary material, which is available to authorized users.

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“…Predicted proteins, and their domains, are available as a Meta4 database [ 37 ] at http://www.ark-genomics.org/tools/meta4…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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The rumen microbial metagenome associated with high methane production in cattle

et al. 2015

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“…Our MAG annotation pipeline is available as a reproducible workflow 76 . Genomes and gene predictions were loaded into a Meta4 database 77 …”

Section: Methodsmentioning

confidence: 99%

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

et al. 2018

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The cow rumen is adapted for the breakdown of plant material into energy and nutrients, a task largely performed by enzymes encoded by the rumen microbiome. Here we present 913 draft bacterial and archaeal genomes assembled from over 800 Gb of rumen metagenomic sequence data derived from 43 Scottish cattle, using both metagenomic binning and Hi-C-based proximity-guided assembly. Most of these genomes represent previously unsequenced strains and species. The draft genomes contain over 69,000 proteins predicted to be involved in carbohydrate metabolism, over 90% of which do not have a good match in public databases. Inclusion of the 913 genomes presented here improves metagenomic read classification by sevenfold against our own data, and by fivefold against other publicly available rumen datasets. Thus, our dataset substantially improves the coverage of rumen microbial genomes in the public databases and represents a valuable resource for biomass-degrading enzyme discovery and studies of the rumen microbiome.

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“…Meta4 (Richardson et al, 2013) is a simple web application that allows users to query, search and browse the millions of gene and protein predictions that often result from metagenomic assembly and annotation. An underlying database can be built from common formats such as FASTA and GFF.…”

Section: Data Sharing and Online Portalsmentioning

confidence: 99%

A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data

et al. 2017

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The microbiome can be defined as the community of microorganisms that live in a particular environment. Metagenomics is the practice of sequencing DNA from the genomes of all organisms present in a particular sample, and has become a common method for the study of microbiome population structure and function. Increasingly, researchers are finding novel genes encoded within metagenomes, many of which may be of interest to the biotechnology and pharmaceutical industries. However, such “bioprospecting” requires a suite of sophisticated bioinformatics tools to make sense of the data. This review summarizes the most commonly used bioinformatics tools for the assembly and annotation of metagenomic sequence data with the aim of discovering novel genes.

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Meta4: a web application for sharing and annotating metagenomic gene predictions using web services

Cited by 8 publications

References 31 publications

The rumen microbial metagenome associated with high methane production in cattle

The rumen microbial metagenome associated with high methane production in cattle

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

A Review of Bioinformatics Tools for Bio-Prospecting from Metagenomic Sequence Data

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