ArticleBioinformatics (Oxford, England)2019
Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Article
- Proteomic analysis of metabolic adaptation in a unicellular cyanobacterium during light-dark cycles and nitrogen fixation.Scientific reports · 2025Article
- NaviGO: An Interactive Tool for Gene Ontology Functional Analysis with Free Text GO Summaries.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Translating a GO Term List to Human Readable Function Description Using GO2Sum.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Predicting Protein Functions with Function-Aware Domain Embeddings Using Domain-PFP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Proteomic changes orchestrate metabolic acclimation of a unicellular diazotrophic cyanobacterium during light-dark cycle and nitrogen fixation states.bioRxiv : the preprint server for biology · 2024Article
- Proteomic analysis of unicellular cyanobacteriumbioRxiv : the preprint server for biology · 2024Article
- Mutual annotation-based prediction of protein domain functions with Domain2GO.Protein science : a publication of the Protein Society · 2024Article
- GO2Sum: generating human-readable functional summary of proteins from GO terms.NPJ systems biology and applications · 2024Article
- A High-Quality Blue Whale Genome, Segmental Duplications, and Historical Demography.Molecular biology and evolution · 2024Article
- Chromosome level genome assembly of the Etruscan shrew Suncus etruscus.Scientific data · 2024Article
- Domain-PFP allows protein function prediction using function-aware domain embedding representations.Communications biology · 2023Article
- Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations.bioRxiv : the preprint server for biology · 2023Article
- Advanced Situation with Recombinant Toxins: Diversity, Production and Application Purposes.International journal of molecular sciences · 2023Review
- Machine learning for the identification of respiratory viral attachment machinery from sequences data.PloS one · 2023Article
- A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes.BMC biology · 2022Article
- ContactPFP: Protein function prediction using predicted contact information.Frontiers in bioinformatics · 2022Article
- CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation.PLoS computational biology · 2022Article
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2 authors.
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Abstract
motivationFunction annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics-scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search; however, this approach has substantial short comings including a low coverage in genome annotation.
resultsHere we have developed Phylo-PFP, a new sequence-based protein function prediction method, which mines functional information from a broad range of similar sequences, including those with a low sequence similarity identified by a PSI-BLAST search. To evaluate functional similarity between identified sequences and the query protein more accurately, Phylo-PFP reranks retrieved sequences by considering their phylogenetic distance. Compared to the Phylo-PFP's predecessor, PFP, which was among the top ranked methods in the second round of the Critical Assessment of Functional Annotation (CAFA2), Phylo-PFP demonstrated substantial improvement in prediction accuracy. Phylo-PFP was further shown to outperform prediction programs to date that were ranked top in CAFA2. AVAILABILITY AND IMPLEMENTATION: Phylo-PFP web server is available for at http://kiharalab.org/phylo_pfp.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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