TAEC

TAEC improves taxonomic composition estimation in metagenomic samples by combining genomic sequence similarity and alignment results to provide precise species- and strain-level microbial profiles.


Key Features:

  • Homology-Based Integration: Incorporates genomic sequence similarity together with alignment tool outputs such as BLAST to refine taxonomic assignments.
  • Low-Rank Precision: Produces taxonomic composition estimates at low ranks within the taxonomy tree, including species and strain levels.
  • Enhanced Accuracy: Eliminates potential misclassifications and corrects taxonomic assignments to improve quantification of microbial genomes in samples.
  • Benchmark Testing: Has been evaluated on diverse simulated benchmark datasets to assess robustness across varying microbial community complexities.
  • Real-World Application: Applied to real metagenomic datasets, including oral cavity samples and Crohn's disease studies, to derive detailed species- and strain-level compositions.

Scientific Applications:

  • Complex microbial community profiling: Supports profiling of microbial communities in complex environments with closely related species or strains.
  • Disease-associated microbiota analysis: Enables species- and strain-level investigation of oral microbiota and inflammatory conditions such as Crohn's disease.
  • Method benchmarking and validation: Facilitates evaluation of taxonomic assignment methods using simulated benchmark datasets.

Methodology:

Combines sequence similarity analysis with alignment results (e.g., BLAST) to eliminate misclassifications and correct taxonomic assignments for improved taxonomic composition estimates at low ranks.

Topics

Details

Tool Type:
library
Operating Systems:
Linux, Mac
Programming Languages:
R
Added:
8/3/2017
Last Updated:
11/25/2024

Operations

Publications

Sohn MB, An L, Pookhao N, Li Q. Accurate genome relative abundance estimation for closely related species in a metagenomic sample. BMC Bioinformatics. 2014;15(1). doi:10.1186/1471-2105-15-242. PMID:25027647. PMCID:PMC4131027.

Documentation

Links