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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 1 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. 2 Access to iProLINK homepage 3 iProLINK http://pir.georgetown.edu/iprolink/ Testing and Benchmarking Dataset • RLIMS-P text mining tool • Protein dictionaries • Name tagging guideline • Protein ontology 4 Protein Phosphorylation Annotation Extraction Manual tagging assisted with computational extraction Training sets of positive and negative samples Evidence attribution RLIMS-P P-group Enzyme (e.g., MAP kinase) P-site Substrate (e.g., cPLA2) (e.g., Ser505) Phosphorylation phosphorylated-cPLA2 Ser-P <AGENT> Enzyme (kinase catalyzing the phosphorylation) 3 objects <THEME> Substrate (protein being phosphorylated) <SITE> P-Site (amino acid residue being phosphorylated) 5 RLIMS-P Rule-based LIterature Mining System for Protein Phosphorylation Preprocessing Entity Recognition Sentence extraction Abstracts Full-Length Texts Acronym detection Part of speech tagging Extracted Annotations Tagged Abstracts PostProcessing Term recognition Phrase Detection Relation Identification Nominal level relation Semantic Type Classification Verbal level relation Noun and verb group detection Other syntactic structure detection Pattern 1: <AGENT> <VG-active-phosphorylate> <THEME> (in/at <SITE>)? ATR/FRP-1 also phosphorylated p53 in Ser 15 download 6 http://pir.georgetown.edu/iprolink/ Benchmarking of RLIMS-P Bioinformatics. 2005 Jun 1;21(11):2759-65 High recall for paper retrieval and high precision for information extraction UniProtKB site feature annotation Proteomics Mass Spec. data analysis: protein identification 7 Online RLIMS-P (version 1.0) http://pir.georgetown.edu/iprolink/rlimsp/ • Search interface • Summary table with top hit of all sites 1. 2. 3. • All sites and tagged text evidence 8 BioThesaurus http://pir.georgetown.edu/iprolink/biothesaurus/ NCBI Genome Entrez Gene RefSeq GenPept UniProt FlyBase WormBase MGD SGD RGD UniProtKB UniRef90/5 0 PIR-PSD Name Filtering Name Extraction iProClass Highly Ambiguous Nonsensical Terms Raw Thesaurus Semantic Typing Other HUGO EC OMIM BioThesaurus v1.0 BioThesaurus UniProtKB Entries: Protein/Gene Names & Synonyms UMLS m = million # UniProtKB entry 1.86m # Source DB record 6.6m # Gene/protein names/terms 3.6m (May, 2005) Applications: • Biological entity tagging • Name mapping • Database annotation • literature mining • Gateway to other resources 9 BioThesaurus Report Synonyms for Metalloproteinase inhibitor 3 1 Gene/Protein Name Mapping 1. Search Synonyms 2. Resolve Name Ambiguity 3. Underlying ID Mapping Name ambiguity 2 3 ID Mapping TMP3 10 Protein Name Tagging Tagging guideline versions 1.0 and 2.0 Dictionary pre-tagging Generation of domain expert-tagged corpora Inter-coder reliability – upper bound of machine 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 intercoder reliability. BioThesaurus for pre-tagging 11 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 PIRSF in DAG View 12 PIRSF to GO Mapping Mapped 5363 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 DynGO viewer Hongfang Liu University of Maryland Superimpose GO and PIRSF hierarchies Bidirectional display (GOor PIRSF-centric views) 13 Protein Ontology Can Complement GO GO-centric view 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 14 Exploration of Gene and Protein Ontology Molecular function PIRSF-centric view Biological process Estrogen receptor alpha (PIRSF50001) Systematic links between three GO sub-ontologies, e.g., linking molecular function and biological process: Estrogen receptor binding Estrogen receptor signaling pathway 15 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. 16 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. 17