Clustering Metagenome Short Reads Using Weighted Proteins
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Evidence-Based Clustering of Reads and Taxonomic Analysis of Metagenomic Data
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Virus DNA-fragment classification using taxonomic hidden Markov model profiles
Proceedings of the 2010 ACM Symposium on Applied Computing
SIMCOMP: a hybrid soft clustering of metagenome reads
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
A two-way Bayesian mixture model for clustering in metagenomics
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A two-way multi-dimensional mixture model for clustering metagenomic sequences
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Motivation: A typical metagenome dataset generated using a 454 pyrosequencing platform consists of short reads sampled from the collective genome of a microbial community. The amount of sequence in such datasets is usually insufficient for assembly, and traditional gene prediction cannot be applied to unassembled short reads. As a result, analysis of such datasets usually involves comparisons in terms of relative abundances of various protein families. The latter requires assignment of individual reads to protein families, which is hindered by the fact that short reads contain only a fragment, usually small, of a protein. Results: We have considered the assignment of pyrosequencing reads to protein families directly using RPS-BLAST against COG and Pfam databases and indirectly via proxygenes that are identified using BLASTx searches against protein sequence databases. Using simulated metagenome datasets as benchmarks, we show that the proxygene method is more accurate than the direct assignment. We introduce a clustering method which significantly reduces the size of a metagenome dataset while maintaining a faithful representation of its functional and taxonomic content. Contact: vmmarkowitz@lbl.gov