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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Modern Molecular Taxonomy

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Multi-species Conserved Sequences

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Related Experiment Video

Updated: Jun 7, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

Clustering metagenomic sequences with interpolated Markov models.

David R Kelley1, Steven L Salzberg

  • 1Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, College Park, MD 20742, USA. dakelley@umiacs.umd.edu

BMC Bioinformatics
|November 4, 2010
PubMed
Summary
This summary is machine-generated.

We developed SCIMM, an unsupervised method for clustering metagenomic sequences, achieving high accuracy. A hybrid method, PHYSCIMM, improves clustering when related genomes are available, aiding microbial discovery.

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Last Updated: Jun 7, 2026

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Environmental DNA sequencing (metagenomics) reveals uncultured microbes.
  • Clustering metagenomic reads by species is essential but challenging.
  • Public genome databases are biased and may not represent target environments.

Purpose of the Study:

  • To develop accurate unsupervised and hybrid methods for metagenomic sequence clustering.
  • To address limitations of existing clustering approaches.
  • To facilitate the analysis of novel microbial communities.

Main Methods:

  • SCIMM (Sequence Clustering with Interpolated Markov Models): an unsupervised method.
  • PHYSCIMM: a hybrid method combining SCIMM with supervised learning (Phymm).

Main Results:

  • SCIMM demonstrates superior clustering accuracy compared to previous unsupervised methods.
  • PHYSCIMM enhances clustering performance when evolutionarily close training genomes are accessible.
  • Both methods offer robust solutions for metagenomic data analysis.

Conclusions:

  • SCIMM and PHYSCIMM provide highly accurate metagenomic sequence clustering.
  • SCIMM is ideal for novel microbial environments due to its unsupervised nature.
  • PHYSCIMM improves clustering for well-characterized genera using supervised learning.