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Captured metagenomics: large-scale targeting of genes based on 'sequence capture' reveals functional diversity in soils.

ABSTRACT: Microbial enzyme diversity is a key to understand many ecosystem processes. Whole metagenome sequencing (WMG) obtains information on functional genes, but it is costly and inefficient due to large amount of sequencing that is required. In this study, we have applied a captured metagenomics technique for functional genes in soil microorganisms, as an alternative to WMG. Large-scale targeting of functional genes, coding for enzymes related to organic matter degradation, was applied to two agricultural soil communities through captured metagenomics. Captured metagenomics uses custom-designed, hybridization-based oligonucleotide probes that enrich functional genes of interest in metagenomic libraries where only probe-bound DNA fragments are sequenced. The captured metagenomes were highly enriched with targeted genes while maintaining their target diversity and their taxonomic distribution correlated well with the traditional ribosomal sequencing. The captured metagenomes were highly enriched with genes related to organic matter degradation; at least five times more than similar, publicly available soil WMG projects. This target enrichment technique also preserves the functional representation of the soils, thereby facilitating comparative metagenomics projects. Here, we present the first study that applies the captured metagenomics approach in large scale, and this novel method allows deep investigations of central ecosystem processes by studying functional gene abundances.

SUBMITTER: Manoharan L

PROVIDER: S-EPMC4675713 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Captured metagenomics: large-scale targeting of genes based on 'sequence capture' reveals functional diversity in soils.

Manoharan Lokeshwaran L Kushwaha Sandeep K SK Hedlund Katarina K Ahrén Dag D

DNA research : an international journal for rapid publication of reports on genes and genomes 20151021 6

Microbial enzyme diversity is a key to understand many ecosystem processes. Whole metagenome sequencing (WMG) obtains information on functional genes, but it is costly and inefficient due to large amount of sequencing that is required. In this study, we have applied a captured metagenomics technique for functional genes in soil microorganisms, as an alternative to WMG. Large-scale targeting of functional genes, coding for enzymes related to organic matter degradation, was applied to two agricult ...[more]

PMID: 26490729

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Project description:MotivationMetagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read is assigned to a taxonomic clade. Because of the large volume of metagenomics datasets, binning methods need fast and accurate algorithms that can operate with reasonable computing requirements. While standard alignment-based methods provide state-of-the-art performance, compositional approaches that assign a taxonomic class to a DNA read based on the k-mers it contains have the potential to provide faster solutions.ResultsWe propose a new rank-flexible machine learning-based compositional approach for taxonomic assignment of metagenomics reads and show that it benefits from increasing the number of fragments sampled from reference genome to tune its parameters, up to a coverage of about 10, and from increasing the k-mer size to about 12. Tuning the method involves training machine learning models on about 10(8) samples in 10(7) dimensions, which is out of reach of standard softwares but can be done efficiently with modern implementations for large-scale machine learning. The resulting method is competitive in terms of accuracy with well-established alignment and composition-based tools for problems involving a small to moderate number of candidate species and for reasonable amounts of sequencing errors. We show, however, that machine learning-based compositional approaches are still limited in their ability to deal with problems involving a greater number of species and more sensitive to sequencing errors. We finally show that the new method outperforms the state-of-the-art in its ability to classify reads from species of lineage absent from the reference database and confirm that compositional approaches achieve faster prediction times, with a gain of 2-17 times with respect to the BWA-MEM short read mapper, depending on the number of candidate species and the level of sequencing noise.Availability and implementationData and codes are available at http://cbio.ensmp.fr/largescalemetagenomicsContactpierre.mahe@biomerieux.comSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:A common strategy for genotyping large samples begins with the characterization of human single nucleotide polymorphisms (SNPs) by sequencing candidate regions in a small sample for SNP discovery. This is usually followed by typing in a large sample those sites observed to vary in a smaller sample. We present results from a systematic investigation of variation at the human apolipoprotein E locus (APOE), as well as the evaluation of the two-tiered sampling strategy based on these data. We sequenced 5.5 kb spanning the entire APOE genomic region in a core sample of 72 individuals, including 24 each of African-Americans from Jackson, Mississippi; European-Americans from Rochester, Minnesota; and Europeans from North Karelia, Finland. This sequence survey detected 21 SNPs and 1 multiallelic indel, 14 of which had not been previously reported. Alleles varied in relative frequency among the populations, and 10 sites were polymorphic in only a single population sample. Oligonucleotide ligation assays (OLA) were developed for 20 of these sites (omitting the indel and a closely-linked SNP). These were then scored in 2179 individuals sampled from the same three populations (n = 843, 884, and 452, respectively). Relative allele frequencies were generally consistent with estimates from the core sample, although variation was found in some populations in the larger sample at SNPs that were monomorphic in the corresponding smaller core sample. Site variation in the larger samples showed no systematic deviation from Hardy-Weinberg expectation. The large OLA sample clearly showed that variation in many, but not all, of OLA-typed SNPs is significantly correlated with the classical protein-coding variants, implying that there may be important substructure within the classical epsilon 2, epsilon 3, and epsilon 4 alleles. Comparison of the levels and patterns of polymorphism in the core samples with those estimated for the OLA-typed samples shows how nucleotide diversity is underestimated when only a subset of sites are typed and underscores the importance of adequate population sampling at the polymorphism discovery stage. [The sequence data described in this paper have been submitted to the GenBank data library under accession no. AF261279.]

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Captured metagenomics: large-scale targeting of genes based on 'sequence capture' reveals functional diversity in soils.

Publications

Captured metagenomics: large-scale targeting of genes based on 'sequence capture' reveals functional diversity in soils.

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