CCMetagen
CCMetagen classifies organisms in metagenomes and metatranscriptomes by weighing all high-scoring read mappings against the entire reference database to improve taxonomic assignment accuracy for analyses of high-throughput sequencing data.
Key Features:
- Advanced Read Mapping: Weighs all high-scoring read mappings collectively against the entire reference database to produce more informed alignments.
- High Precision and F1 Scores: Demonstrated superior species-level precision (3–1580-fold increases) and F1 scores (2–922-fold increases) relative to Kraken2, Centrifuge, and KrakenUniq in simulated fungal and bacterial metagenomes.
- Comprehensive Reference Database: Supports using the entire NCBI nucleotide collection (nt) as reference, enabling assessment of species with incomplete genome sequences across all biological kingdoms.
- Efficiency: Operates with fast runtime and memory-efficient requirements suitable for large-scale analyses.
Scientific Applications:
- Microbiome species-level profiling: Provides accurate species-level classification in microbiome studies.
- Eukaryotic and prokaryotic community analysis: Processes complex datasets containing both eukaryotic and prokaryotic organisms.
- Environmental and host-associated microbial community studies: Supports analysis of environmental and host-associated microbial communities from metagenomic and metatranscriptomic data.
Methodology:
Collectively weighs high-scoring read mappings against the reference database and supports use of the NCBI nucleotide collection (nt) as the reference.
Topics
Details
- License:
- GPL-3.0
- Maturity:
- Mature
- Cost:
- Free of charge
- Tool Type:
- api, command-line tool, web application
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- Python
- Added:
- 8/9/2019
- Last Updated:
- 6/16/2020
Operations
Publications
Marcelino VR, Clausen PT, Buchmann JP, Wille M, Iredell JR, Meyer W, Lund O, Sorrell TC, Holmes EC. CCMetagen: comprehensive and accurate identification of eukaryotes and prokaryotes in metagenomic data. Unknown Journal. 2019. doi:10.1101/641332.
DOI: 10.1101/641332