Dataset Information

MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study.

ABSTRACT:

Motivation

Metagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome.

Results

To address this problem, we developed a novel clustering approach called 'metagenomic clustering by reference library' (MCRL), where a reference library containing a set of reference genes is clustered with respect to an assembled metagenome. According to our proposed approach, reference genes homologous to similar sets of metagenomic sequences, termed 'signatures', are iteratively clustered in a greedy fashion, retaining at each step the reference genes yielding the lowest E values, and terminating when signatures of remaining reference genes have a minimal overlap. The outcome of this computation is a non-redundant list of reference genes homologous to minimally overlapping sets of contigs, representing potential candidates for gene families present in the metagenome. Unlike metagenomic clustering methods, there is no need for contigs to overlap to be associated with a cluster, enabling MCRL to draw on more information encoded in the metagenome when computing tentative gene families. We demonstrate how MCRL can be used to extract candidate viral gene families from an oral metagenome and an oral virome that otherwise could not be determined using standard approaches. We evaluate the sensitivity, accuracy and robustness of our proposed method for the viral case study and compare it with existing analysis approaches.

Availability and implementation

https://github.com/a-tadmor/MCRL.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Tadmor AD

PROVIDER: S-EPMC10060711 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study.

Tadmor Arbel D AD Phillips Rob R

Bioinformatics (Oxford, England) 20220101 3

<h4>Motivation</h4>Metagenomes offer a glimpse into the total genomic diversity contained within a sample. Currently, however, there is no straightforward way to obtain a non-redundant list of all putative homologs of a set of reference sequences present in a metagenome.<h4>Results</h4>To address this problem, we developed a novel clustering approach called 'metagenomic clustering by reference library' (MCRL), where a reference library containing a set of reference genes is clustered with respec ...[more]

PMID: 34636854

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Project description:BackgroundIn metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity.ResultsThe proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the >/= 10 reads datasets and comparable in the > or = 8 kb benchmark tests.ConclusionIn the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.

Dataset Information

MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study.

Motivation

Results

Availability and implementation

Supplementary information

Publications

MCRL: using a reference library to compress a metagenome into a non-redundant list of sequences, considering viruses as a case study.

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