Binning sequences using very sparse labels within a metagenome

Chan, Chon-Kit Kenneth, Hsu, Arthur L., Halgamuge, Saman K. and Tang, Sen-Lin (2008) Binning sequences using very sparse labels within a metagenome. BMC Bioinformatics, 9 215-1-215-17. doi:10.1186/1471-2105-9-215

Author Chan, Chon-Kit Kenneth
Hsu, Arthur L.
Halgamuge, Saman K.
Tang, Sen-Lin
Title Binning sequences using very sparse labels within a metagenome
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2008-04-04
Sub-type Article (original research)
DOI 10.1186/1471-2105-9-215
Open Access Status DOI
Volume 9
Start page 215-1
End page 215-17
Total pages 17
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted abstract
Background: In 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.

Results: The 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 ≥ 8 kb benchmark tests.

Conclusion: In 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.

Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article number 215

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Agriculture and Food Sciences
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Citation counts: TR Web of Science Citation Count  Cited 41 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 22 Mar 2011, 22:52:32 EST by Dr Chon-Kit Kenneth Chan on behalf of School of Agriculture and Food Sciences