TAMPA

TAMPA visualizes and compares taxonomic profiles produced by taxonomic profilers to support interpretation of taxa and their relative abundance in whole-genome sequencing metagenomic samples.


Key Features:

  • Comprehensive visualization: Generates detailed visualizations of taxonomic profiles from diverse taxonomic profilers.
  • Comparison beyond single metrics: Compares profiler outputs beyond single-value metrics such as the F1 score to reveal biological differences.
  • Highlighting taxonomic distinctions: Identifies and emphasizes taxonomic distinctions and relative abundance shifts across samples.
  • Profiler comparison for method selection: Facilitates comparison of profiling methods to aid selection of suitable taxonomic profilers for a dataset.
  • Facilitating interpretation: Provides interpretive views of profiler outputs to support downstream biological analysis.

Scientific Applications:

  • Benchmarking taxonomic profilers: Enables evaluation and comparison of taxonomic profilers using visual analyses beyond single-value metrics.
  • Method selection for metagenomics: Supports choosing appropriate taxonomic profiling methods for whole-genome sequencing metagenomic samples.
  • Detection of biological differences: Reveals taxonomic differences and abundance changes between samples to inform biological conclusions.
  • Hypothesis generation: Highlights taxonomic distinctions across samples to generate novel biological hypotheses.

Methodology:

Performs comparative visualization and analysis of taxonomic profiles produced by taxonomic profilers, emphasizing comparisons beyond single-value metrics such as the F1 score.

Topics

Details

License:
MIT
Cost:
Free of charge
Tool Type:
command-line tool
Operating Systems:
Mac, Linux, Windows
Programming Languages:
Python
Added:
1/7/2023
Last Updated:
11/24/2024

Operations

Publications

Sarwal V, Brito J, Mangul S, Koslicki D. TAMPA: interpretable analysis and visualization of metagenomics-based taxon abundance profiles. GigaScience. 2022;12. doi:10.1093/gigascience/giad008. PMID:36852763. PMCID:PMC9972184.