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Enhancing Protein Annotation Through Literature Mining and Ontology Development

This paper discusses the advancements in protein annotation at the Protein Information Resource (PIR), focusing on literature mining and ontology development. The goal is to ensure accurate and rich annotations of protein sequences and functions by utilizing controlled vocabularies and biological ontologies like Gene Ontology (GO). The development of IProLINK for integrated literature mining and annotation extraction is highlighted. Additionally, methods for high-quality data collection and annotation extraction, including manual and computational approaches, are reviewed, paving the way for future genomic and proteomic research.

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Enhancing Protein Annotation Through Literature Mining and Ontology Development

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  1. Literature Data Mining and Protein Ontology Development At the Protein Information Resource (PIR) Hu ZZ*, Mani I, Liu H, Hermoso V, Vijay-Shanker K, Nikolskaya A, Natale DA, and Wu CH ISMB 2005, Detroit, Michigan June 29, 2005 Zhang-Zhi Hu, M.D. Senior Bioinformatics Scientist, PIR Georgetown University Medical Center Washington, DC 20007

  2. PIR – Integrated Protein Informatics Resource for Genomic/Proteomic Research (http://pir.georgetown.edu) New version of PIR homepage UniProt– Central international database of protein sequence and function (http://www.uniprot.org)

  3. Objective: Accurate, Consistent, and Rich Annotation of Protein Sequence and Function • Literature-Based Curation – Extract Reliable Information from Literature • Function, domains/sites, developmental stages, catalytic activity, binding and modified residues, regulation, pathways, tissue specificity, subcellular location …... • Ensure high quality, accurate and up-to-date experimental data for each protein. • A major bottleneck! • Ontologies/Controlled Vocabularies – For Information Integration and Knowledge Management • UniProtKB entries will be annotated using widely accepted biological ontologies and other controlled vocabularies, e.g. Gene Ontology (GO) and EC nomenclature.

  4. iProLINK: An integrated protein resource for literature mining and literature-based curation 1. Bibliography mapping - UniProt mapped citations 2. Annotation extraction - annotation tagged literature 3. Protein named entity recognition - dictionary, name tagged literature 4. Protein ontology development - PIRSF-based ontology

  5. Testing and Benchmarking Dataset iProLINK http://pir.georgetown.edu/iprolink/ • RLIMS-P text mining tool • Protein dictionaries • Name tagging guideline • Protein ontology

  6. Protein Phosphorylation Annotation Extraction • Manual tagging assisted with computational extraction • Training sets of positive and negative samples Evidence attribution RLIMS-P 3 objects

  7. Entity Recognition Preprocessing Acronym detection Abstracts Full-Length Texts Sentence extraction Pattern 1: <AGENT> <VG-active-phosphorylate> <THEME> (in/at <SITE>)? Term recognition Part of speech tagging ATR/FRP-1 also phosphorylated p53 in Ser 15 Phrase Detection Relation Identification Extracted Annotations Tagged Abstracts Noun and verb group detection Nominal level relation Other syntactic structure detection Verbal level relation Post-Processing Semantic Type Classification RLIMS-P Rule-based LIterature Mining System for Protein Phosphorylation download http://pir.georgetown.edu/iprolink/

  8. Bioinformatics. 2005 Jun 1;21(11):2759-65 Benchmarking of RLIMS-P • UniProtKB site feature annotation • Proteomics Mass Spec. data analysis: protein identification High recall for paper retrieval and high precision for information extraction

  9. 1. 2. 3. Online RLIMS-P (version 1.0) http://pir.georgetown.edu/iprolink/rlimsp/ • Search interface • Summary table with top hit of all sites • All sites and tagged text evidence

  10. UniProt NCBI UniProtKB UniRef90/50 PIR-PSD Entrez Gene RefSeq GenPept Name Filtering Highly Ambiguous Nonsensical Terms Name Extraction Genome FlyBase WormBase MGD SGD RGD Raw Thesaurus Other HUGO EC OMIM Semantic Typing UMLS iProClass Applications: BioThesaurus • Biological entity tagging • Name mapping • Database annotation • literature mining • Gateway to other resources UniProtKB Entries: Protein/Gene Names & Synonyms BioThesaurus http://pir.georgetown.edu/iprolink/biothesaurus/ BioThesaurus v1.0 m = million (May, 2005)

  11. BioThesaurus Report Synonyms for Metalloproteinase inhibitor 3 Gene/Protein Name Mapping • Search Synonyms • Resolve Name Ambiguity • Underlying ID Mapping 1 3 ID Mapping TMP3 Name ambiguity 2

  12. Protein Name Tagging • Tagging guideline versions 1.0 and 2.0 • Generation of domain expert-tagged corpora • Inter-coder reliability – upper bound of machine tagging • Dictionary pre-tagging • F-measure: 0.412 (0.372 Precision, 0.462 Recall) • Advantages: helpful with standardization and extent of tagging, reducing the fatigue problem, and improve inter-coder reliability. • BioThesaurus for pre-tagging

  13. PIRSF in DAG View PIRSF-Based Protein Ontology • PIRSF family hierarchy based on evolutionary relationships • Standardized PIRSF family names as hierarchical protein ontology • DAG Network structure for PIRSF family classification system

  14. DynGO viewerHongfang Liu University of Maryland • Superimpose GO and PIRSF hierarchies • Bidirectional display (GO- or PIRSF-centric views) PIRSF to GO Mapping • Mapped5363 curated PIRSF homeomorphic families and subfamilies to the GO hierarchy • 68% of the PIRSF families and subfamilies map to GO leaf nodes • 2329 PIRSFs have shared GO leaf nodes • Complements GO: PIRSF-based ontology can be used to analyze GO branches and concepts and to provide links between the GO sub-ontologies

  15. Protein Ontology Can Complement GO • Expanding a Node: Identification of GO subtrees that can be expanded when GO concepts are too broad • IGFBP subfamilies and • High- vs. low-affinity binding for IGF between IGFBP and IGFBPrP GO-centric view

  16. Estrogen receptor alpha (PIRSF50001) Exploration of Gene and Protein Ontology PIRSF-centric view Molecular function Biological process • Systematic links between three GO sub-ontologies, e.g., linking molecular function and biological process: • Estrogen receptor binding • Estrogen receptor signaling pathway

  17. Summary • PIR iProLINK literature mining resource provides annotated data sets for NLP research on annotation extraction and protein ontology development • RLIMS-P text-mining tool for protein phosphorylation from PubMed literature. • BioThesaurus can be used for name mapping to solve name synonym and ambiguity issues. • PIRSF-based protein ontology can complement other biological ontologies such as GO.

  18. Acknowledgements • Research Projects • NIH: NHGRI/NIGMS/NLM/NIMH/NCRR/NIDCR (UniProt) • NSF: SEIII (Entity Tagging) • NSF: ITR (Ontology) • Collaborators • I. Mani from Georgetown University Department of Linguistics on protein name recognition and protein name ontology. • H. Liu from University of Maryland Department of Information System on protein name recognition and text mining. • Vijay K. Shanker from University of Delaware Department of Computer and Information Science on text mining of protein phosphorylation features.

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