Protocol Update for Large-scale genome and gene function analysis with PANTHER Classification System (v.14.0)

By Huaiyu Mi, Anushya Muruganujan, Xiaosong Huang, Dustin Ebert, Caitlin Mills, Xinyu Guo, Paul D. Thomas. Nature Protocols, 2019

1,325 citations recorded by the source corpus.

The PANTHER classification system (http://www.pantherdb.org) is a comprehensive system that combines genomes, gene function classifications, pathways and statistical analysis tools to enable biologists to analyze large-scale genome-wide experimental data. The current system (PANTHER v.14.0) covers 131 complete genomes organized into gene families and subfamilies; evolutionary relationships between genes are represented in phylogenetic trees, multiple sequence alignments and statistical models (hidden Markov models (HMMs)). The families and subfamilies are annotated with Gene Ontology (GO) terms, and sequences are assigned to PANTHER pathways. A suite of tools has been built to allow users to browse and query gene functions and analyze large-scale experimental data with a number of statistical tests. PANTHER is widely used by bench scientists, bioinformaticians, computer scientists and systems biologists. Since the protocol for using this tool (v.8.0) was originally published in 2013, there have been substantial improvements and updates in the areas of data quality, data coverage, statistical algorithms and user experience. This Protocol Update provides detailed instructions on how to analyze genome-wide experimental data in the PANTHER classification system. Here the authors provide an update to their 2013 protocol for using the PANTHER classification system, detailing how to analyze genome-wide experimental data with the newest version of PANTHER (v.14.0), with improvements in the areas of data quality, data coverage, statistical algorithms and user experience.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8a270829fc20daaf46f10bb42f317068c258b7f1

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In Molecular Biology

  1. Anushya Muruganujan
  2. Huaiyu Mi
  3. Paul D. Thomas
  4. Dustin Ebert
  5. Tremayne Mushayahama
  6. Laurent-Philippe Albou
  7. Caitlin Mills
  8. John T. Casagrande

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