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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

M-CAMPTM: A Cloud-based Web Platform with a Novel Approach for Species-level Classification of 16S rRNA Microbiome Sequences

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* and Yang Liu*

Volume 18, Issue 1, 2023

Published on: 29 November, 2022

Page: [21 - 39] Pages: 19

DOI: 10.2174/1574893617666220520100535

Price: $65

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.

Keywords: Microbiology, 16S-seq, DNA sequencing, Bioinformatics, Web Application, Benchmarking

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