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Cited by 2,010Genomics and Phylogenetic StudiesBioinformatics and Genomic NetworksMachine Learning in Bioinformatics

PANTHER : Making genome‐scale phylogenetics accessible to all

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Thomas, Paul D., Ebert, Dustin, Muruganujan, Anushya, Mushayahama, Tremayne, Albou, Laurent‐Philippe, Mi, Huaiyu·2022·Protein Science
DOI 10.1002/pro.4218PMC PMC8740835
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Abstract

Phylogenetics is a powerful tool for analyzing protein sequences, by inferring their evolutionary relationships to other proteins. However, phylogenetics analyses can be challenging: they are computationally expensive and must be performed carefully in order to avoid systematic errors and artifacts. Protein Analysis THrough Evolutionary Relationships (PANTHER; http://pantherdb.org ) is a publicly available, user‐focused knowledgebase that stores the results of an extensive phylogenetic reconstruction pipeline that includes computational and manual processes and quality control steps. First, fully reconciled phylogenetic trees (including ancestral protein sequences) are reconstructed for a set of “reference” protein sequences obtained from fully sequenced genomes of organisms across the tree of life. Second, the resulting phylogenetic trees are manually reviewed and annotated with function evolution events: inferred gains and losses of protein function along branches of the phylogenetic tree. Here, we describe in detail the current contents of PANTHER, how those contents are generated, and how they can be used in a variety of applications. The PANTHER knowledgebase can be downloaded or accessed via an extensive API. In addition, PANTHER provides software tools to facilitate the application of the knowledgebase to common protein sequence analysis tasks: exploring an annotated genome by gene function; performing “enrichment analysis” of lists of genes; annotating a single sequence or large batch of sequences by homology; and assessing the likelihood that a genetic variant at a particular site in a protein will have deleterious effects.

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  • The current Unknome database is based on PANTHER version 17.0, which covers over 143 organisms, is continuously developed, and has good sensitivity and accuracy.

    “However, OrthoMCL was not being updated, and so the current Unknome database is based on the PANTHER database (version 17.0) which covers over 143 organisms, is currently in continuous development, and has a good level of sensitivity and accuracy [24–26].”

    From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [26]

    The Introduction says PANTHER has been continually updated, and §3.1.2 reports benchmarked ortholog predictions with high sensitivity for the full set and high specificity for a subset. But §3.1.2 counts 142 genomes in its trees, not over 143; our copy also does not establish that Unknome uses PANTHER version 17.0. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Thomas, P. D., Ebert, D., Muruganujan, A., Mushayahama, T., Albou, L., & Mi, H.
Affiliations
University of Southern California (US)
Corresponding author
Thomas, Paul D.
ORCID
Venue
Protein Science
Publisher
Wiley
Published
2021-11-25
Volume
31
Issue
1
Pages
8-22
ISSN
  • 0961-8368 (ISSN-L)
  • 1469-896X
DOI
10.1002/pro.4218
PMC
PMC8740835
OpenAlex
W3209545809
Cited by
2,010
References
4
Citations by year
2026: 411 · 2025: 642 · 2024: 618 · 2023: 303 · 2022: 32 · 2021: 2 · 2020: 1 · 2019: 1
FWCI
26.55
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Protein Science (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Human Genome Research Institute (U41HG002273)
  • National Science Foundation of Sri Lanka (1917302)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
panther, protein evolution, phylogenetic tree, knowledge base, enrichment analysis, deleterious variant prediction
MeSH terms
Phylogeny, Proteins, Software, Evolution, Molecular, Sequence Analysis, Protein, Databases, Protein, Molecular Sequence Annotation

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