CSCE 555 Bioinformatics Lecture 21 Integrative Genomics Meeting

CSCE 555 Bioinformatics Lecture 21 Integrative Genomics Meeting: MW 4: 00 PM-5: 15 PM SWGN 2 A 21 Instructor: Dr. Jianjun Hu Course page: http: //www. scigen. org/csce 555 University of South Carolina Department of Computer Science and Engineering 2008 www. cse. sc. edu.

Outline What is Integrative Genomics Why integrative genomics The Data Sources Integrating strategies Issues in Integrative genomics Application Example: disease gene prioritization

Integrative Genomics - what is it? Acquisition, Integration, Curation, and Analysis of biological data Hypothesis Integrative Genomics: the study of complex interactions between genes, organism and environment, the triple helix of biology. Gene <–> Organism <-> Environment It is definitely beyond the buzzword stage - Universities now have programs named 'Integrated Genomics. ' Information is not knowledge - Albert Einstein

Why Integrative Genomics? Support Complex Queries Show me all genes involved in brain development that are expressed in the Central Nervous System. • Show me all genes involved in brain development in human and mouse that also show iron ion binding activity. • For this set of genes, what aspects of function and/or cellular localization do they share? • For this set of genes, what mutations are reported to cause pathological conditions? •

Integrative genomics for Biomedicine • To correlate diseases with • anatomical parts affected, • the genes/proteins involved, and • the underlying physiological processes (interactions, pathways, processes). • support personalized or “tailor-made” medicine. How to integrate multiple types of genome-scale data across experiments and phenotypes in order to find genes associated with diseases

Two Separate Worlds…. . Disease World Medical Informatics Bioinformatics & the “omes Genome Regulome Transcriptome mi. RNAome Proteome Disease Database Interactome Metabolome Patient Records Clinical Trials Variome Pharmacogenom e Pub. Med →Name Physiome OMIM →Synonyms Clinical →Related/Similar Diseases Synopsis →Subtypes Pathome →Etiology →Predisposing Causes →Pathogenesis →Molecular Basis 382 “omes” so far……… →Population Genetics →Clinical findings and there is “UNKNOME” too - genes with →System(s) involved →Lesions no function known →Diagnosis →Prognosis http: //omics. org/index. php/Alphabetically_ordered_list_of_omics →Treatment With Some Data Exchange… →Clinical Trials……

Data Sources: The –Omics Clinical data Disease data

Bioinformatic Data-1978 to present • • • DNA sequence Gene expression Protein Structure Genome mapping SNPs & Mutations • • • Metabolic networks Regulatory networks Trait mapping Gene function analysis Scientific literature and others………. .

Human Genome Project – Data Deluge Database name Nucleotide Protein Structure Genome Sequences Popset SNP 3 D Domains GEO Datasets No. of Human Gene Records currently in NCBI: 29413 (excluding pseudogenes, mitochondrial genes and obsolete records). Includes ~460 micro. RNAs GEO Expressions Records 12, 427, 463 419, 759 11, 232 75 21, 010 11, 751, 216 41, 857 19 5, 036 16, 246, 778 Uni. Gene 123, 777 Uni. STS 323, 773 Pub. Med Central Homolo. Gene Taxonomy 4, 278 19, 520 1 NCBI Human Genome Statistics – as on February 12, 2008

Information Deluge…. . • 3 scientific journals in 1750 • Now - >120, 000 scientific journals! • >500, 000 medical articles/year • >4, 000 scientific articles/year • >16 million abstracts in Pub. Med derived from >32, 500 journals A researcher would have to scan 130 different journals and read 27 papers per day to follow a single disease, such as breast cancer (Baasiri et al. , 1999 Oncogene 18: 7958 -7965).

Methods for Integration 1. Link driven federations • Explicit links between databanks. 2. Warehousing • Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse. • Integrative analysis 3. Others…. . Semantic Web, etc………

Link-driven Federations 1. Creates explicit links between databanks 2. query: get interesting results and use web links to reach related data in other databanks Examples: NCBI-Entrez, SRS

http: //www. ncbi. nlm. nih. gov/Database/datamodel/

http: //www. ncbi. nlm. nih. gov/Database/data

Querying Entrez-Gene

Data Warehousing Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse. Advantages Disadvantages 1. Good for very-specific, task-based queries and studies. 1. Can become quickly outdated – needs constant updates. 2. Since it is custom-built and usually expert-curated, relatively less errorprone 2. Limited functionality – For e. g. , one disease-based or one system-based.

Integrative data analysis Data is downloaded, filtered Inference algorithms that integrate heterogeneous data Evidences are usually weak from one data source, integration will enhance signals Cross-validation effect to reduce false positive

Common Issues in Integrative Genomics • Heterogeneous Data Sets - Data Integration – From Genotype to Phenotype – Experimental and Consensus Views • Incorporation of Large Datasets – Whole genome annotation pipelines – Large scale mutagenesis/variation projects (db. SNP) • Computational vs. Literature-based Data Collection and Evaluation (Med. Line) • Data Mining – extraction of new knowledge – testable hypotheses (Hypothesis Generation)

No Integrative Genomics is Complete without Ontologies Gene World Gene (GO) Ontology Biomedical World • Unified Medical Language System (UMLS)

The 3 Gene Ontologies (Recap) • Molecular Function = elemental activity/task – the tasks performed by individual gene products; examples are carbohydrate binding and ATPase activity – What a product ‘does’, precise activity • Biological Process = biological goal or objective – broad biological goals, such as dna repair or purine metabolism, that are accomplished by ordered assemblies of molecular functions – Biological objective, accomplished via one or more ordered assemblies of functions • Cellular Component = location or complex – subcellular structures, locations, and macromolecular complexes; examples include nucleus, telomere, and RNA polymerase II holoenzyme – ‘is located in’ (‘is a subcomponent of’ ) http: //www. geneontology. org

What can researchers do with GO? • Access gene product functional information • Find how much of a proteome is involved in a process/ function/ component in the cell • Map GO terms and incorporate manual annotations into own databases • Provide a link between biological knowledge and • gene expression profiles • proteomics data • Getting the GO and GO_Association Files • Data Mining – My Favorite Gene – By GO – By Sequence • Analysis of Data – Clustering by function/process • Other Tools

Unified Medical Language System (UMLS) http: //umlsks. nlm. nih. gov/kss/ The UMLS Metathesaurus contains information about biomedical concepts and terms from many controlled vocabularies and classifications used in patient records, administrative health data, bibliographic and full-text databases, and expert systems. The Semantic Network, through its semantic types, provides a consistent categorization of all concepts represented in the UMLS Metathesaurus. The links between the semantic types provide the structure for the Network and represent important relationships in the biomedical domain. The SPECIALIST Lexicon is an English language lexicon with many biomedical terms, containing syntactic, morphological, and orthographic information for each term or word.

Example Study: Disease Gene Identification and Prioritization Hypothesis: Majority of genes that impact or cause disease share membership in any of several functional relationships OR Functionally similar or related genes cause similar phenotype. Functional Similarity – Common/shared • Gene Ontology term • Pathway • Phenotype • Chromosomal location • Expression • Cis regulatory elements (Transcription factor binding sites) • mi. RNA regulators • Interactions • Other features…. .

7 Known Disease Genes Mining human interactome 66 Which of these interactants are potential new candidates? HPRD Bio. Grid Direct Interactants of Disease Genes Indirect Interactants of Disease Genes Prioritize candidate genes in the interacting partners of the disease-related genes • Training sets: disease related genes • Test sets: interacting partners of the training genes 778

Example: Breast cancer OMIM genes (level 0) Directly interacting genes (level 1) Indirectly interacting genes (level 2) 15 342 2469! 15 342 2469

Topp. Gene – General Schema http: //toppgene. cchmc. org

TOPPGene - Data Sources 1. Gene Ontology: GO and NCBI Entrez Gene 2. Mouse Phenotype: MGI (used for the first time for human disease gene prioritization) 3. Pathways: KEGG, Bio. Carta, Bio. Cyc, Reactome, Gen. MAPP, MSig. DB 4. Domains: Uni. Prot (Pfam, Interpro, etc. ) 5. Interactions: NCBI Entrez Gene (Biogrid, Reactome, BIND, HPRD, etc. ) 6. Pubmed IDs: NCBI Entrez Gene 7. Expression: GEO 8. Cytoband: MSig. DB New 9. Cis-Elements: MSig. DB features 10. mi. RNA Targets: MSig. DB added

Benefits of Integrative Genomics 1. To unravel the connection between genotype and phenotype Systematically identify novel phenotype–genotype relationships. 2. Hypotheses generator. 3. Paves way for prognosis, diagnosis, and personalized medicine (adverse drug reactions, etc. ). 4. Deeper understanding of disease and an enhanced integration of medicine with biology. 5. Increasing knowledge of the genes associated with diseases will allow researchers to address more complicated issues, including the relative contributions to disease of genes in the core biological set shared by all species and those encoding proteins specific to humans; how sequence features (such as conservation and polymorphism) relate to disease characteristics; and how protein function relates to the outcome of clinical treatment 6. And MANY MORE……. .

Summary Networks and integration of databases are keys to success in Bioinformatics. Integration of computation and data into a single cohesive whole will increase the efficiency of research effort ◦ by reducing the serendipity & hit and miss nature of empirical research and ◦ will provide valuable clues to the biomedical researchers on their choice of experiments limitations of funds, manpower and time. Users have to know what is available and how to access (what are the limitations) and use the resources they are offered.

Thank You!

Algorithms in bioinformatics • string algorithms • dynamic programming • machine learning (NN, k-NN, SVM, GA, . . ) • Markov chain models • hidden Markov models • Markov Chain Monte Carlo (MCMC) algorithms • stochastic context free grammars • EM algorithms • Gibbs sampling • clustering • tree algorithms • text analysis • hybrid/combinatorial techniques and more…