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PhyloFacts Plant Disease and Stress Resistance Phylogenomic Explorer

v. 2.0.   24 November 2009: 145 families; 3,794 Hidden Markov Models (family and subfamily).

The Plant Disease and Stress Resistance Phylogenomic Explorer focuses on proteins and protein domains involved in plant-pathogen interaction and disease resistance and stress response processes. Our collaborators on this project include Dr. Brian Staskawicz (UC Berkeley), Dr. Barbara Baker (PGEC), Dr. Jonathan Jones (Sainsbury Laboratory, UK) and Dr. Richard Bruskiewich (IRRI).

This resource is supported by a grant from the National Science Foundation, Presidential Early Career Award in Science and Engineering (PECASE) Program, DBI-0238311.

Please cite the following paper in references to this resource: "PhyloFacts: An online structural phylogenomic encyclopedia for protein functional and structural classification", Genome Biology 2006, 7:R83

Protein Search

Submit sequences for classification against the HMM library. This library is designed to help biologists do the following:

  • Predict molecular function by phylogenomic analysis, using the phylogenetic tree for the family.
  • Classify novel sequences to functional subtypes, using the subfamily HMMs for the family.
  • Predict specificity positions, using the alignment analysis plots for each family.
  • Predict 3D structure, using structural domain analyses.

Browse books in our library

Each "book" in the HMM library corresponds roughly to a (whole-chain) protein family or domain, and contains the following data (generally downloadable, in different formats):

  • A multiple sequence alignment of homologous proteins, typically from many species
  • One or more phylogenetic trees.
  • A decomposition of the tree into subtrees, to identify functional subfamilies.
  • Hidden Markov models for the family and for individual subfamilies.
  • GO (Gene Ontology) annotations and evidence codes.
  • Other annotations and experimental data.
  • Hyperlinks to papers and online resources.
  • An analysis of the family's multiple sequence alignment using the subfamily decomposition to predict specificity positions defining the individual subtypes.
  • A predicted structure, including construction of comparative models for some families.
  • A predicted cellular localization (i.e., membrane-localized, secreted, cytoplasmic, nuclear, etc.).