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CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data
Mohammad H Norouzi-Beirami1, Sayed-Amir Marashi2, Ali M Banaei-Moghaddam3
1Laboratory of Complex Biological systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran 1417614335, Iran.
NAR Genomics and Bioinformatics
|February 12, 2021
Summary
CAMAMED is a new pipeline for metagenomic data analysis. It uses a composition-aware mapping approach for accurate taxonomic and functional profiling, aiding biomarker discovery in microbial communities.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- Metagenomics analyzes microbial community DNA.
- Assembly-based and mapping-based methods are common.
- Mapping-based methods are preferred for functional analysis with gene catalogs.
Purpose of the Study:
- Introduce CAMAMED, a composition-aware mapping-based pipeline.
- Enable taxonomic and functional profiling of metagenomic samples.
- Facilitate biomarker discovery in case-control studies.
Main Methods:
- Utilizes mapping-based metagenomic data analysis.
- Applies cumulative sum-scaling for compositional data analysis.
- Integrates KEGG database for functional annotation (KO, EC, reactions).
Main Results:
- Provides gene frequency from metagenome sequences mapped to gene catalogs.
- Enables taxonomic and functional profiling.
- Identifies potential biomarkers by analyzing functional differences.
Conclusions:
- CAMAMED offers a robust approach for metagenomic analysis.
- Compositional data analysis is crucial for accurate profiling.
- The pipeline supports biomarker discovery in microbial communities.
