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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis.

Sebastian Hupfauf¹, Mohammad Etemadi², Marina Fernández-Delgado Juárez¹

¹Department of Microbiology, University of Innsbruck, Innsbruck, Austria.

|December 2, 2020

Summary

CoMA is a free, intuitive pipeline for analyzing gene amplicon sequencing data. It simplifies complex bioinformatics, offering pre-processing to publication-ready graphics for diverse operating systems.

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Area of Science:

Microbiology and Bioinformatics
Computational Biology
Genomics and Molecular Biology

Background:

Next-generation sequencing (NGS) generates vast amounts of gene amplicon data in biological and medical research.
Existing analysis tools often demand significant bioinformatics expertise and specific operating systems (e.g., Linux).
There is a need for accessible, user-friendly tools for analyzing amplicon sequencing data.

Purpose of the Study:

To introduce "CoMA-Comparative Microbiome Analysis," a free and intuitive pipeline for amplicon sequencing data analysis.
To provide a tool compatible with common operating systems, lowering the barrier to entry for researchers.
To offer a comprehensive workflow from data pre-processing to publication-ready visualizations and standardized outputs.

Main Methods:

Development of the CoMA (Comparative Microbiome Analysis) pipeline.
Implementation of services including data pre-processing, quality checking, OTU clustering, taxonomic assignment, and visualization.
Validation through a benchmark test using mock communities and comparison with established tools (Mothur, QIIME, QIIME2-DADA2).
Demonstration of functionality on real-world soil microbial community data.

Main Results:

CoMA provides a user-friendly interface and supports various operating systems.
The pipeline successfully generates publication-ready graphics and standardized output files (OTU-table, BIOM, NEWICK).
Benchmark tests showed CoMA performed comparably to established pipelines, accurately identifying key microbial genera in mock and soil samples.

Conclusions:

CoMA is an effective and accessible tool for analyzing gene amplicon sequencing data.
It democratizes microbiome analysis by simplifying complex bioinformatics tasks.
The tool's comprehensive features and validated performance make it suitable for diverse research applications.