ArticleBMC bioinformatics2011
Using context to improve protein domain identification.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 34 citations in OpenAlex.
- Scaling the profile of life by function with SPIN.Bioinformatics advances · 2026Article
- AGODB: a comprehensive domain annotation database of argonaute proteins.Database : the journal of biological databases and curation · 2022Article
- In silico structural and functional characterization of Antheraea mylitta cocoonase.Journal, genetic engineering & biotechnology · 2022Article
- Study on cocoonase, sericin, and degumming of silk cocoon: computational and experimental.Journal, genetic engineering & biotechnology · 2021Article
- Protein domain identification methods and online resources.Computational and structural biotechnology journal · 2021Review
- Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions.Nucleic acids research · 2019Article
- Characterization of a Protein Phosphatase Type-1 and a Kinase Anchoring Protein inFrontiers in microbiology · 2018Article
- Improving pairwise comparison of protein sequences with domain co-occurrence.PLoS computational biology · 2018Article
- Domain prediction with probabilistic directional context.Bioinformatics (Oxford, England) · 2017Article
- Plasmobase: a comparative database of predicted domain architectures for Plasmodium genomes.Malaria journal · 2017Article
- A practical guide to buildPeerJ · 2017Article
- Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence.PLoS computational biology · 2016Article
- A multi-objective optimization approach accurately resolves protein domain architectures.Bioinformatics (Oxford, England) · 2016Article
- Beyond the E-Value: Stratified Statistics for Protein Domain Prediction.PLoS computational biology · 2015Article
- Genome-Wide Collation of the Plasmodium falciparum WDR Protein Superfamily Reveals Malarial Parasite-Specific Features.PloS one · 2015Article
- Article
- Identification of divergent protein domains by combining HMM-HMM comparisons and co-occurrence detection.PloS one · 2014Article
- Powerful sequence similarity search methods and in-depth manual analyses can identify remote homologs in many apparently "orphan" viral proteins.Journal of virology · 2014Article
- An insight into the lignin peroxidase of Macrophomina phaseolina.Bioinformation · 2013Article
- Multiple graph regularized protein domain ranking.BMC bioinformatics · 2012Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIdentifying domains in protein sequences is an important step in protein structural and functional annotation. Existing domain recognition methods typically evaluate each domain prediction independently of the rest. However, the majority of proteins are multidomain, and pairwise domain co-occurrences are highly specific and non-transitive.
resultsHere, we demonstrate how to exploit domain co-occurrence to boost weak domain predictions that appear in previously observed combinations, while penalizing higher confidence domains if such combinations have never been observed. Our framework, Domain Prediction Using Context (dPUC), incorporates pairwise "context" scores between domains, along with traditional domain scores and thresholds, and improves domain prediction across a variety of organisms from bacteria to protozoa and metazoa. Among the genomes we tested, dPUC is most successful at improving predictions for the poorly-annotated malaria parasite Plasmodium falciparum, for which over 38% of the genome is currently unannotated. Our approach enables high-confidence annotations in this organism and the identification of orthologs to many core machinery proteins conserved in all eukaryotes, including those involved in ribosomal assembly and other RNA processing events, which surprisingly had not been previously known.
conclusionsOverall, our results demonstrate that this new context-based approach will provide significant improvements in domain and function prediction, especially for poorly understood genomes for which the need for additional annotations is greatest. Source code for the algorithm is available under a GPL open source license at http://compbio.cs.princeton.edu/dpuc/. Pre-computed results for our test organisms and a web server are also available at that location.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.