Literature Data Mining and Protein Ontology Development

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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

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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)
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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.

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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
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iProLINK
http//pir.georgetown.edu/iprolink/

• RLIMS-P text mining tool
• Protein dictionaries
• Name tagging guideline
• Protein ontology

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Protein Phosphorylation Annotation Extraction

• Manual tagging assisted with computational
  extraction
• Training sets of positive and negative samples

Evidence attribution
RLIMS-P
3 objects
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RLIMS-P Rule-based LIterature Mining System for
Protein Phosphorylation
download
http//pir.georgetown.edu/iprolink/
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Benchmarking of RLIMS-P
High recall for paper retrieval and high
precision for information extraction

• UniProtKB site feature annotation
• Proteomics Mass Spec. data analysis protein
  identification

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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

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BioThesaurus http//pir.georgetown.edu/iprolink/bi
othesaurus/
BioThesaurus v1.0
m million
UniProtKB entry 1.86m
Source DB record 6.6m
Gene/protein names/terms 3.6m
(May, 2005)
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BioThesaurus Report
Synonyms for Metalloproteinase inhibitor 3

• Gene/Protein Name Mapping
• Search Synonyms
• Resolve Name Ambiguity
• Underlying ID Mapping

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ID Mapping
TMP3
Name ambiguity
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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

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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

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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

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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

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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

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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.

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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.