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.

Documentation

Downloads

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