Gene and translation initiation site prediction in metagenomic sequences

Hyatt, Doreene R.; LoCascio, Philip F.; Hauser, Loren; Uberbacher, Edward C.

doi:10.1093/bioinformatics/bts429

Cited by 483 publications

(388 citation statements)

References 18 publications

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“…We observed a clear switch in the optimal translation and annotation strategies at about 250 bp: metagenomes shorter than this benefitted from 6FT and per-read annotation, while metagenomes longer than this benefitted from Prodigal and per-ORF annotation (Fig 3A). We speculate these results are likely explained by (i) metagenomic gene-finders are less accurate for short-reads than for long-reads (26) and (ii) short reads usually only contain a single true ORF while long-reads are more likely to span multiple ORFs.…”

Section: Annotating Multiple Orfs Per Read Is Necessary With Longer Rmentioning

confidence: 94%

“…While 6FT captures all true open reading frames (ORFs), it also increases the volume of query sequences and can lead to classification of spurious ORFs. Alternatively, ab initio metagenomic gene prediction [26], [27], [28] decreases the volume of queries, but can result in false-positive and false-negative reading frame predictions Metagenomic simulation framework. 1) Taxonomic profiles from real metagenomes are used to construct mock microbial communities.…”

Section: Ab Initio Gene Prediction Reduces Data Volume At a Small Cosmentioning

confidence: 99%

“…ORFs were searched and classified into SFams using RAPsearch2 while optimizing all other parameters (Methods). We ran 6FT in addition to three different metagenomic gene predictors-FragGeneScan [27], MetaGeneMark [28], and Prodigal [26]-and classified reads into protein families according to their top-hit across predicted ORFs (per-read annotation). Additionally, we evaluated a novel heuristic to rapidly filter short spurious ORFs (S2 Fig). We found that the metagenomic gene finders reduced sequence volume by~85% relative to ORFs that had been naively translated in 6-frames (Fig 2A), consistent with prior observations [29].…”

Section: Ab Initio Gene Prediction Reduces Data Volume At a Small Cosmentioning

confidence: 99%

“…In the first, we performed naïve translation of reads into all 6 possible reading frames (6FT) using the Transeq application from EMBOSS (v6.3.1) [82]. In the second approach, we took ORFs predicted from 6FT, split the sequences on stop-codons, and subsequently filtered these sequences to remove short spurious ORFs and reduce data volume ( S2 Fig). In the third approach, we used three popular tools-FragGeneScan [27], MetaGeneMark [28], and Prodigal [26]-to perform ab initio gene prediction. FragGeneScan (v1.18) was run using the options: -complete = 0 -train = illumina_10; MetaGeneMark (v3.25) was run using the options: -m MetaGeneMark_v1.mod; Prodigal (v2.60) was run using the options: -p meta.…”

Section: Statistical Assessment Of Metagenome Annotationmentioning

confidence: 99%

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Automated and accurate estimation of gene family abundance from shotgun metagenomes

Nayfach

Bradley

Wyman

et al. 2015

Preprint

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Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn's disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease. Author SummaryMicrobial communities perform a wide variety of functions, from marine photosynthesis to aiding digestion in the human gut. Shotgun "metagenomic" sequencing can be used to sample millions of short DNA sequences from such communities directly, without needing to first culture its constituents in the laboratory. Using these data, researchers can survey which functions are encoded by mapping these short sequences to known protein families and pathways. Several tools for this annotation already exist. But, annotation is a multi-step process that includes identification of genes in a metagenome and determination of the type of protein each gene encodes. We currently know little about how different choices of parameters during annotation influences the final results. In this work, we systematically test how several key decisions affect the accuracy and speed of annotation, and based on these results, develop new software for annotation, which we named ShotMAP. We then use ShotMAP to functionally characterize marine communities and gut communities in a clinical cohort of inflammatory bowel disease. We find several functions are differentially represented in the gut microbiome of Crohn's disease patients, which could be candidates for biomarkers and could also offer insight into the pathophysiology of Crohn's. ShotMAP is freely available (https://github.com/sharpton/shotmap).

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Section: Annotating Multiple Orfs Per Read Is Necessary With Longer Rmentioning

confidence: 94%

Section: Ab Initio Gene Prediction Reduces Data Volume At a Small Cosmentioning

confidence: 99%

Section: Ab Initio Gene Prediction Reduces Data Volume At a Small Cosmentioning

confidence: 99%

Section: Statistical Assessment Of Metagenome Annotationmentioning

confidence: 99%

See 2 more Smart Citations

Automated and accurate estimation of gene family abundance from shotgun metagenomes

Nayfach

Bradley

Wyman

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…All scaffolds ≥5 kb (≥1 kb for subassemblies, Methanohalophilus-1, and the GB enrichment culture) were included when binning genomes from the metagenomic assembly. Scaffolds were annotated as described previously 36,37 by predicting open reading frames using MetaProdigal 39 . Sequences were compared using USEARCH 40 to KEGG, UniRef90 and InterproScan 41 with single and reverse best hit (RBH) matches greater than 60 bits reported.…”

Section: Methodsmentioning

confidence: 99%

Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales

et al. 2016

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Hydraulic fracturing is the industry standard for extracting hydrocarbons from shale formations. Attention has been paid to the economic benefits and environmental impacts of this process, yet the biogeochemical changes induced in the deep subsurface are poorly understood. Recent single-gene investigations revealed that halotolerant microbial communities were enriched after hydraulic fracturing. Here, the reconstruction of 31 unique genomes coupled to metabolite data from the Marcellus and Utica shales revealed that many of the persisting organisms play roles in methylamine cycling, ultimately supporting methanogenesis in the deep biosphere. Fermentation of injected chemical additives also sustains long-term microbial persistence, while thiosulfate reduction could produce sulfide, contributing to reservoir souring and infrastructure corrosion. Extensive links between viruses and microbial hosts demonstrate active viral predation, which may contribute to the release of labile cellular constituents into the extracellular environment. Our analyses show that hydraulic fracturing provides the organismal and chemical inputs for colonization and persistence in the deep terrestrial subsurface. S hale gas accounts for one-third of natural gas energy resources worldwide. It has been estimated that shale gas will provide half of the natural gas in the USA, annually, by 2040, with the Marcellus shale in the Appalachian Basin projected to produce three times more than any other formation 1 . Recovery of these hydrocarbons is dependent on hydraulic fracturing technologies, where the high-pressure injection of water and chemical additives generates extensive fractures in the shale matrix. Hydrocarbons trapped in tiny pore spaces are subsequently released and collected at the wellpad surface, together with a portion of the injected fluids that have reacted with the shale formation. The mixture of injected fluids and hydrocarbons collected is referred to as 'produced fluids'.Microbial metabolism and growth in hydrocarbon reservoirs has both positive and negative impacts on energy recovery. Whereas stimulation of methanogens in coal beds enhances energy recovery 2 , bacterial hydrogen sulfide production ('reservoir souring') decreases profits and contributes to corrosion and the risk of environmental contamination 3 . Additionally, biomass accumulation within newly generated fractures may reduce their permeability, decreasing natural gas recovery. Despite these potential microbial impacts, little is known about the function and activity of microorganisms in hydraulically fractured shale.Initial work by our group and others 4-9 used single marker gene analyses to identify microorganisms from several geographically distinct shale formations. These analyses showed similar halotolerant taxa in produced fluids several months after hydraulic fracturing. To assign functional roles to these organisms, we conducted metagenomic and metabolite analyses on input and produced fluids up to a year after hydraulic fracturing (HF) from two Appala...

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AnnotationGene Calling, Taxonomy, and Function

2018

Genomic Approaches in Earth and Environmental Sciences

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Gene and translation initiation site prediction in metagenomic sequences

Abstract: The Prodigal software is freely available under the General Public License from http://code.google.com/p/prodigal/.

Cited by 483 publications

References 18 publications

Automated and accurate estimation of gene family abundance from shotgun metagenomes

Automated and accurate estimation of gene family abundance from shotgun metagenomes

Microbial metabolisms in a 2.5-km-deep ecosystem created by hydraulic fracturing in shales

AnnotationGene Calling, Taxonomy, and Function

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