TreeSAPP

TreeSAPP performs tree-based phylogenetic profiling to provide sensitive taxonomic and functional classification of genes, reactions, and pathways from cultivated and uncultivated microorganisms.


Key Features:

  • Phylogenetic profiling: Employs linear regression of evolutionary distance against taxonomic rank to place closely related and highly divergent query sequences at appropriate taxonomic levels.
  • Quantitative classification: Produces quantitative functional and taxonomic classifications for assembled and unassembled genomic data.
  • Support for orthologous groups: Integrates complex reference packages such as orthologous groups with additional phylogenetic information for classification.
  • Reference packages for biogeochemical cycles: Leverages reference packages representing coding sequences associated with multiple globally relevant biogeochemical cycles.
  • Tree-of-life visualizations: Generates visualizations of the tree of life to explore microbial community structure and ecological roles.

Scientific Applications:

  • Microbial biogeochemistry: Improves taxonomic assignment and functional annotation of microorganisms that mediate matter and energy transformations in global biogeochemical cycles.
  • Environmental genome analysis: Facilitates characterization of genes, reactions, and pathways from environmental genomes, including taxa underrepresented in reference databases.

Methodology:

Uses linear regression of evolutionary distance against taxonomic rank and reference packages of coding sequences linked to biogeochemical cycles; integrates orthologous-group reference data for quantitative functional and taxonomic classification of assembled and unassembled genomic sequences.

Topics

Details

License:
GPL-3.0
Programming Languages:
Python
Added:
1/18/2021
Last Updated:
3/4/2021

Operations

Publications

Morgan-Lang C, McLaughlin R, Armstrong Z, Zhang G, Chan K, Hallam SJ. TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler. Bioinformatics. 2020;36(18):4706-4713. doi:10.1093/bioinformatics/btaa588. PMID:32637989. PMCID:PMC7695126.

PMID: 32637989
PMCID: PMC7695126
Funding: - Office of Science of the US Department of Energy: DE-AC02-05CH11231