ArticleNAR genomics and bioinformatics2021
CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From functional annotation to functional meaning in microbiome research.Environmental microbiome · 2026Review
- Microbiome-microRNA interactions in inflammatory bowel disease: insights from metagenomic and transcriptomic data analysis.Gastroenterology and hepatology from bed to bench · 2025Article
- Immuno-informatics voyage through molecular mimicry of Heat Shock Proteins: Potential IBD immunopathogenesis.PloS one · 2025Article
- Baseline microbiota of blueberries, soil, and irrigation water from blueberry farms located in three geographical regions.Heliyon · 2024Article
- Article
- PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data.BMC bioinformatics · 2023Article
- OMARU: a robust and multifaceted pipeline for metagenome-wide association study.NAR genomics and bioinformatics · 2022Article
- Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges.Frontiers in genetics · 2021Review
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Authors and funding
4 authors.
Funding
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Abstract
Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.
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Registered trials
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