title: CATCh, an ensemble classifier for chimera detection in 16S rRNA sequencing studies.
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.
authors.affiliation: Unit of Microbiology, Belgian Nuclear Research Centre (SCK•CEN), Mol, Belgium Department of Bioscience Engineering, Vrije Universiteit Brussel, Brussels, Belgium VIB Center for the Biology of Disease, Leuven, Belgium.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Mysara
authors.affiliation: Data Mining and Modeling Group, VIB Inflammation Research Center, Ghent, Belgium Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
authors.forename: Yvan
authors.initials: Y
authors.lastname: Saeys
authors.affiliation: Unit of Microbiology, Belgian Nuclear Research Centre (SCK•CEN), Mol, Belgium.
authors.forename: Natalie
authors.initials: N
authors.lastname: Leys
authors.affiliation: Department of Bioscience Engineering, Vrije Universiteit Brussel, Brussels, Belgium VIB Center for the Biology of Disease, Leuven, Belgium Department of Microbiology and Immunology, REGA Institute, KU Leuven, Leuven, Belgium.
authors.forename: Jeroen
authors.initials: J
authors.lastname: Raes
authors.affiliation: Unit of Microbiology, Belgian Nuclear Research Centre (SCK•CEN), Mol, Belgium pmonsieu@sckcen.be.
authors.forename: Pieter
authors.initials: P
authors.lastname: Monsieurs
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004275:DNA, Ribosomal / Q000737:chemistry / Q000235:genetics*; D008969:Molecular Sequence Data; D010802:Phylogeny; D012336:RNA, Ribosomal, 16S / Q000235:genetics*; D011995:Recombination, Genetic; D017422:Sequence Analysis, DNA
chemical_list: D004275:DNA, Ribosomal; D012336:RNA, Ribosomal, 16S