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.