Dataset Information

Quantitative assessment of protein function prediction from metagenomics shotgun sequences.

ABSTRACT: To assess the potential of protein function prediction in environmental genomics data, we analyzed shotgun sequences from four diverse and complex habitats. Using homology searches as well as customized gene neighborhood methods that incorporate intergenic and evolutionary distances, we inferred specific functions for 76% of the 1.4 million predicted ORFs in these samples (83% when nonspecific functions are considered). Surprisingly, these fractions are only slightly smaller than the corresponding ones in completely sequenced genomes (83% and 86%, respectively, by using the same methodology) and considerably higher than previously thought. For as many as 75,448 ORFs (5% of the total), only neighborhood methods can assign functions, illustrated here by a previously undescribed gene associated with the well characterized heme biosynthesis operon and a potential transcription factor that might regulate a coupling between fatty acid biosynthesis and degradation. Our results further suggest that, although functions can be inferred for most proteins on earth, many functions remain to be discovered in numerous small, rare protein families.

SUBMITTER: Harrington ED

PROVIDER: S-EPMC1955820 | biostudies-other | 2007 Aug

REPOSITORIES: biostudies-other

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Quantitative assessment of protein function prediction from metagenomics shotgun sequences.

Harrington E D ED Singh A H AH Doerks T T Letunic I I von Mering C C Jensen L J LJ Raes J J Bork P P

Proceedings of the National Academy of Sciences of the United States of America 20070823 35

To assess the potential of protein function prediction in environmental genomics data, we analyzed shotgun sequences from four diverse and complex habitats. Using homology searches as well as customized gene neighborhood methods that incorporate intergenic and evolutionary distances, we inferred specific functions for 76% of the 1.4 million predicted ORFs in these samples (83% when nonspecific functions are considered). Surprisingly, these fractions are only slightly smaller than the correspondi ...[more]

PMID: 17717083

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Project description:Although microbial communities are associated with human, environmental, plant, and animal health, there exists no cost-effective method for precisely characterizing species and genes in such communities. While deep whole-metagenome shotgun (WMS) sequencing provides high taxonomic and functional resolution, it is often prohibitively expensive for large-scale studies. The prevailing alternative, 16S rRNA gene amplicon (16S) sequencing, often does not resolve taxonomy past the genus level and provides only moderately accurate predictions of the functional profile; thus, there is currently no widely accepted approach to affordable, high-resolution, taxonomic, and functional microbiome analysis. To address this technology gap, we evaluated the information content of shallow shotgun sequencing with as low as 0.5 million sequences per sample as an alternative to 16S sequencing for large human microbiome studies. We describe a library preparation protocol enabling shallow shotgun sequencing at approximately the same per-sample cost as 16S sequencing. We analyzed multiple real and simulated biological data sets, including two novel human stool samples with ultradeep sequencing of 2.5 billion sequences per sample, and found that shallow shotgun sequencing recovers more-accurate species-level taxonomic and functional profiles of the human microbiome than 16S sequencing. We discuss the inherent limitations of shallow shotgun sequencing and note that 16S sequencing remains a valuable and important method for taxonomic profiling of novel environments. Although deep WMS sequencing remains the gold standard for high-resolution microbiome analysis, we recommend that researchers consider shallow shotgun sequencing as a useful alternative to 16S sequencing for large-scale human microbiome research studies where WMS sequencing may be cost-prohibitive. IMPORTANCE A common refrain in recent microbiome-related academic meetings is that the field needs to move away from broad taxonomic surveys using 16S sequencing and toward more powerful longitudinal studies using shotgun sequencing. However, performing deep shotgun sequencing in large longitudinal studies remains prohibitively expensive for all but the most well-funded research labs and consortia, which leads many researchers to choose 16S sequencing for large studies, followed by deep shotgun sequencing on a subset of targeted samples. Here, we show that shallow- or moderate-depth shotgun sequencing may be used by researchers to obtain species-level taxonomic and functional data at approximately the same cost as amplicon sequencing. While shallow shotgun sequencing is not intended to replace deep shotgun sequencing for strain-level characterization, we recommend that microbiome scientists consider using shallow shotgun sequencing instead of 16S sequencing for large-scale human microbiome studies.

Dataset Information

Quantitative assessment of protein function prediction from metagenomics shotgun sequences.

Publications

Quantitative assessment of protein function prediction from metagenomics shotgun sequences.

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