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Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

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Published on: December 13, 2024

CREST--classification resources for environmental sequence tags.

Anders Lanzén1, Steffen L Jørgensen, Daniel H Huson

  • 1Department of Biology and Centre for Geobiology, University of Bergen, Bergen, Norway. anders.lanzen@uni.no

Plos One
|November 13, 2012
PubMed
Summary
This summary is machine-generated.

CREST (Classification Resources for Environmental Sequence Tags) offers a robust system for classifying microbial sequences. This alignment-based tool improves accuracy and identifies novel taxa in environmental samples.

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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Environmental microbial community studies increasingly rely on sequencing taxonomic markers like the small-subunit (SSU) ribosomal RNA gene.
  • Large datasets from new sequencing technologies necessitate reliable and flexible taxonomic classification systems.
  • Current methods face challenges in accurately classifying diverse and novel microbial sequences.

Purpose of the Study:

  • To develop and present CREST (Classification Resources for Environmental Sequence Tags), a novel system for generating and utilizing custom taxonomies and reference datasets for environmental sequence classification.
  • To implement CREST using an alignment-based lowest common ancestor algorithm with explicit rank similarity criteria.
  • To provide the SilvaMod SSU rRNA reference database and taxonomy for bacterial, archaeal, and eukaryotic sequences.

Main Methods:

  • Developed CREST, featuring an alignment-based classification method utilizing the lowest common ancestor algorithm and rank similarity criteria.
  • Implemented CREST as a web server, command-line tool, and within the MEGAN graphical user interface.
  • Created the SilvaMod reference database and taxonomy from the SILVA SSURef database.
  • Compared CREST and SilvaMod performance against the RDP Classifier using cross-validation and environmental datasets, with Greengenes as an alternative reference database.

Main Results:

  • CREST demonstrated superior performance compared to alignment-free methods, exhibiting higher recall (sensitivity) and precision.
  • CREST effectively identified sequences from novel taxa.
  • Classification using SilvaMod outperformed Greengenes, especially for environmental sequences.
  • CREST offers a reliable and flexible system for taxonomic classification of environmental sequences.

Conclusions:

  • CREST provides an accurate and efficient alignment-based approach for classifying environmental microbial sequences.
  • The SilvaMod database enhances classification accuracy for bacteria, archaea, and eukaryotes.
  • CREST is a valuable tool for analyzing large microbial datasets and advancing our understanding of microbial ecology.