Clustering metagenomic sequences with interpolated Markov models

By David R. Kelley, Steven L. Salzberg. 2010

120 citations recorded by the source corpus.

BackgroundSequencing of environmental DNA (often called metagenomics) has shown tremendous potential to uncover the vast number of unknown microbes that cannot be cultured and sequenced by traditional methods. Because the output from metagenomic sequencing is a large set of reads of unknown origin, clustering reads together that were sequenced from the same species is a crucial analysis step. Many effective approaches to this task rely on sequenced genomes in public databases, but these genomes are a highly biased sample that is not necessarily representative of environments interesting to many metagenomics projects.ResultsWe present SCIMM (Sequence Clustering with Interpolated Markov Models), an unsupervised sequence clustering method. SCIMM achieves greater clustering accuracy than previous unsupervised approaches. We examine the limitations of unsupervised learning on complex datasets, and suggest a hybrid of SCIMM and supervised learning method Phymm called PHY SCIMM that performs better when evolutionarily close training genomes are available.ConclusionsSCIMM and PHY SCIMM are highly accurate methods to cluster metagenomic sequences. SCIMM operates entirely unsupervised, making it ideal for environments containing mostly novel microbes. PHY SCIMM uses supervised learning to improve clustering in environments containing microbial strains from well-characterized genera. SCIMM and PHY SCIMM are available open source from http://www.cbcb.umd.edu/software/scimm.

Subfields: Ecology, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecology

In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “However, other methods based on interpolated Markov models [248] have performed better than these early genome binners.”
A review of methods and databases for metagenomic classification and assembly (2019, background): “Recent studies have attempted to benchmark the performance of metagenomics classifiers based on both accuracy and speed [62, 63],”
Machine learning for metagenomics: methods and tools (2015, methodology): “The Markov model of LikelyBin has been generalized to varying order Markov chains by (Kelley and Salzberg, 2010) in a tool called SCIMM.”