CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies

Mohamed Mysara, Yvan Saeys, Natalie Leys, Jeroen Raes, Pieter Monsieurs

Research output: Contribution to journalA1: Web of Science-article

Abstract

In ecological studies, microbial diversity is nowadays mostly assessed via the detection of phylogenetic marker genes, such as 16S rRNA. However, PCR amplification of these marker genes produces a significant amount of artificial sequences, often referred to as chimeras. Different algorithms have been developed to remove these chimeras, but efforts to combine different methodologies are limited. Therefore, two machine learning classifiers (reference-based and de novo CATCh) were developed by integrating the output of existing chimera detection tools into a new, more powerful method. When comparing our classifiers with existing tools in either the reference-based or de novo mode, a higher performance of our ensemble method was observed on a wide range of sequencing data, including simulated, 454 pyrosequencing, and Illumina MiSeq data sets. Since our algorithm combines the advantages of different individual chimera detection tools, our approach produces more robust results when challenged with chimeric sequences having a low parent divergence, short length of the chimeric range, and various numbers of parents. Additionally, it could be shown that integrating CATCh in the preprocessing pipeline has a beneficial effect on the quality of the clustering in operational taxonomic units.

Original languageEnglish
JournalApplied and Environmental Microbiology
Volume81
Issue number5
Pages (from-to)1573-84
Number of pages12
ISSN0099-2240
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • Cluster Analysis
  • Computational Biology/methods
  • DNA, Ribosomal/chemistry
  • Molecular Sequence Data
  • Phylogeny
  • RNA, Ribosomal, 16S/genetics
  • Recombination, Genetic
  • Sequence Analysis, DNA

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