1. CSCE555 Bioinformatics Lecture 21 Integrative Genomics Meeting: MW 4:00PM-5:15PM SWGN2A21 Instructor: Dr. Jianjun Hu Course page: http://www.scigen.org/csce555 University of South Carolina Department of Computer Science and Engineering 2008 www.cse.sc.edu. www.cse.sc.edu

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

3. Integrative Genomics - what is it? Acquisition, Integration, Curation, and Analysis of biological data 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.' Hypothesis Information is not knowledge - Albert Einstein

4. 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?

5. How to integrate multiple types of genome-scale data across experiments and phenotypes in order to find genes associated with diseases 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.

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

7. Data Sources: The –Omics Clinical data Disease data

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

9. Human Genome Project – Data Deluge Database nameRecords Nucleotide 12,427,463 Protein 419,759 Structure 11,232 Genome Sequences 75 Popset 21,010 SNP 11,751,216 3D Domains 41,857 Domains 19 GEO Datasets 5,036 GEO Expressions 16,246,778 UniGene 123,777 UniSTS 323,773 PubMed Central 4,278 HomoloGene 19,520 Taxonomy 1 No. of Human Gene Records currently in NCBI: 29413 (excluding pseudogenes, mitochondrial genes and obsolete records). Includes ~460 microRNAs NCBI Human Genome Statistics – as on February12, 2008

10. 3 scientific journals in 1750 Now - >120,000 scientific journals! >500,000 medical articles/year >4,000,000 scientific articles/year >16 million abstracts in PubMed derived from >32,500 journals Information Deluge….. 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).

11. 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……… Methods for Integration

12. 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 Link-driven Federations

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

18. Querying Entrez-Gene

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

21. 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

22. 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 (dbSNP) Computational vs. Literature-based Data Collection and Evaluation (MedLine) Data Mining –extraction of new knowledge –testable hypotheses (Hypothesis Generation)

23. No Integrative Genomics is Complete without Ontologies Gene Ontology (GO) Unified Medical Language System (UMLS) Gene WorldBiomedical World

24. 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’ ) The 3 Gene Ontologies (Recap) http://www.geneontology.org

25. 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 What can researchers do with GO? 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

26. 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.

28. 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) miRNA regulators Interactions Other features…..

29. Known Disease Genes Direct Interactants of Disease Genes Mining human interactome HPRD BioGrid Which of these interactants are potential new candidates? Indirect Interactants of Disease Genes 7 7 66 778 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 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

30. Example: Breast cancer OMIM genes (level 0) Directly interacting genes (level 1) Indirectly interacting genes (level2) 153422469! 15 342 2469

31. ToppGene – General Schema http://toppgene.cchmc.org

32. 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, BioCarta, BioCyc, Reactome, GenMAPP, MSigDB 4.Domains: UniProt (Pfam, Interpro,etc.) 5.Interactions: NCBI Entrez Gene (Biogrid, Reactome, BIND, HPRD, etc.) 6.Pubmed IDs: NCBI Entrez Gene 7.Expression: GEO 8.Cytoband: MSigDB 9.Cis-Elements: MSigDB 10.miRNA Targets: MSigDB New features added

33. 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…….. Benefits of Integrative Genomics

34. 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.

35. Thank You!

36. 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…