bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: O16
Oral presentation
10th Benelux Bioinformatics Conference bbc 2015
O16. BIOINFORMATICS TOOLS FOR ACCURATE ANALYSIS OF AMPLICON
SEQUENCING DATA FOR BIODIVERSITY ANALYSIS
Mohamed Mysara 1-3 , Yvan Saeys 4,5 , Natalie Leys 1 , Jeroen Raes 2,6 & Pieter Monsieurs 1* .
Unit of Microbiology, Belgian Nuclear Research Centre SCK•CEN, Mol; Belgium 1; Department of Bioscience
Engineering, Vrije Universiteit Brussel VUB, Brussels, Belgium 2 ; Department of Structural Biology, Vlaams Instituut
voor Biotechnologie VIB, Brussels, Belgium 3 ; Data Mining and Modeling Group, VIB Inflammation Research Center,
Ghent, Belgium 4 , Department of RespiratoryMedicine, Ghent University Hospital, Ghent, Belgium 5 , Department of
Microbiology and Immunology, REGA institute, KU Leuven, Belgium 6 . * pmonsieu@sckcen.be
High-throughput sequencing technologies have created a wide range of new applications, also in the field of microbial
ecology. Yet when used in 16S rRNA biodiversity studies, it suffers from two important problems: the presence of PCR
artefacts (called chimera) and sequencing errors resulting from the sequencing sequencing technologies. In this work
three artificial intelligence-based algorithms are proposed, CATCh, NoDe and IPED, to handle these two problems. A
benchmarking study was performed comparing CATCh/NoDe (for 454 pyrosequencing) or CATCh/IPED (for Illumina
MiSeq sequencing) with other state-of-the art tools, showing a clear improvement in chimera detection and reduction of
sequencing errors respectively, and in general leading to more accurate clustering of the sequencing reads in Operational
Taxonomic Units (OTUs). All algorithms are available via http://science.sckcen.be/en/Institutes/EHS/MCB/MIC
/Bioinformatics/.
INTRODUCTION
The revolution in new sequencing technologies has led to
an explosion of possible applications, including new
opportunities for microbial ecological studies via the
usage of 16S rDNA amplicon sequencing. However,
within such studies, all sequencing technologies suffer
from the presence of erroneous sequences, i.e. (i) chimera,
introduced by wrong target amplification in PCR, and (ii)
sequencing errors originating from different factors during
the sequencing process. As such, there is a need for
effective algorithms to remove those erroneous sequences
to be able to accurately assess the microbial diversity.
METHODS
First, a new algorithm called CATCh (Combining
Algorithms to Track Chimeras) was developed by
integrating the output of existing chimera detection tools
into a new more powerful method. Second, NoDe (Noise
Detector) was introduced, an algorithm that identifies and
corrects erroneous positions in 454-pyrosequencing reads.
Third, IPED (Illumina Paired End Denoiser) algorithm
was developed to handle error correction in Illumina
MiSeq sequencing data as the first tool in the field. After
identifying those positions likely to contain an error, those
sequencing reads are subsequently clustered with correct
reads resulting in error-free consensus reads. The three
algorithms were benchmarked with state-of-the-art tools.
RESULTS & DISCUSSION
Via a comparative study with other chimera detection
tools, CATCh was shown to outperform all other tools,
thereby increasing the sensitivity with up to 14% (see
Figure 1).
FIGURE 1. Plot indicating the effect of applying 5% indels (shown on the
left) and 5% mismatches (shown on the right), on the performance of
different chimera detection tools. CATCh was found to outperform other
existing tools.
Similarly, NoDe and IPED were benchmarked against
other denoising algorithms, thereby showing a significant
improvement in reduction of the error rate up to 55% and
75% respectively (see Figure 2). The combined effect of
our algorithms for chimera removal and error correction
also had a positive effect on the clustering of reads in
operational taxonomic units (OTUs), with an almost
perfect correlation between the number of OTUs and the
number of species present in the mock communities.
Indeed, when applying our improved pipeline containing
CATCh and NoDe on a 454 pyrosequencing mock dataset,
our pipeline could reduce the number of OTUs to 28 (i.e.
close 18, the correct number of species). In contrast,
running the straightforward pipeline without our
algorithms included would inflate the number of OTUs to
98. Similarly, when tested on Illumina MiSeq sequencing
data obtained for a mock community, using a pipeline
integrating CATCh and IPED, the number of OTUs
returned was 33 (i.e. close to the real number of 21
species), while 86 OTUs was obtained using the default
mothur pipeline.
REFERENCES
Mysara M., Leys N., Raes J., Monsieurs P.- NoDe: a fast error-correction
algorithm for pyrosequencing amplicon reads.- In: BMC
Bioinformatics, 16:88(2015), p. 1-15.- ISSN 1471-2105
Mysara M., Saeys Y., Leys N., Raes J., Monsieurs P.- CATCh, an
Ensemble Classifier for Chimera Detection in 16S rRNA Sequencing
Studies.- In: Applied and Environmental Microbiology, 81:5(2015),
p. 1573-1584.- ISSN 0099-2240
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