Title:M-CAMPTM: A Cloud-based Web Platform with a Novel Approach for
Species-level Classification of 16S rRNA Microbiome Sequences
Volume: 18
Issue: 1
Author(s): Andrew E. Schriefer, Brajendra Kumar, Avihai Zolty, Adam Didier, Nirmal M.G., Greeshma G.T., Nofar Nadiv, Michael Perez, Preetam R., Santosh Kumar Mahankuda, Pankaj Kumar, Aaron Tenney, Maureen Bourner, Shira Lezer, Fei Zhong, Michal Daniely*Yang Liu*
Affiliation:
- Department of Bioinformatics, Merck KGaA, Darmstadt, Germany
- Department of Bioinformatics, Merck KGaA, Darmstadt, Germany
Keywords:
Microbiology, 16S-seq, DNA sequencing, bioinformatics, web application, benchmarking.
Abstract:
Background: The M-CAMPTM (Microbiome Computational Analysis for Multi-omic Profiling)
Cloud Platform was designed to provide users with an easy-to-use web interface to access best in
class microbiome analysis tools. This interface allows bench scientists to conduct bioinformatic analysis
on their samples and then download publication-ready graphics and reports.
Objective: In this study, we aim to describe the M-CAMPTM platform and demonstrate that the taxonomic
classification is more accurate than previously described methods on a wide range of microbiome
samples.
Methods: The core pipeline of the platform is the 16S-seq taxonomic classification algorithm which
provides species-level classification of Illumina 16s sequencing. This algorithm uses a novel approach
combining alignment and kmer-based taxonomic classification methodologies to produce a highly accurate
and comprehensive profile. Additionally, a comprehensive proprietary database combining reference
sequences from multiple sources was curated and contained 18056 unique V3-V4 sequences covering
11527 species.
Results and Discussion: The M-CAMPTM 16S taxonomic classification algorithm evaluated 52 sequencing
samples from both public and in-house standard sample mixtures with known fractions. The
same evaluation process was also performed on 5 well-known 16S taxonomic classification algorithms,
including Qiime2, Kraken2, Mapseq, Idtaxa and Spingo, using the same dataset. Results have been discussed
in terms of evaluation metrics and classified taxonomic levels.
Conclusion: Compared to current popular publicly accessible classification algorithms, M-CAMPTM
16S taxonomic classification algorithm provides the most accurate species-level classification of 16S
rRNA sequencing data.
