CSCE555 Bioinformatics

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.

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

Hypothesis DATA INFORMATION KNOWLEDGE Information is not knowledge - Albert Einstein 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.'

Why Integrative Genomics? Support Complex Queries • Show me all genes involved in brain development that are expressed in the Central Nervous System. • Show me allgenesinvolved in brain developmentinhumanandmouse that also showiron 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 Genome Variome Transcriptome Regulome miRNAome • 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…… Interactome Pharmacogenome Metabolome Physiome Pathome Medical Informatics Bioinformatics & the “omes PubMed Proteome Disease Database Patient Records OMIM Clinical Synopsis Clinical Trials 382 “omes” so far……… and there is “UNKNOME” too - genes with no function known http://omics.org/index.php/Alphabetically_ordered_list_of_omics With Some Data Exchange…

Data Sources: The –Omics Clinical data Disease data

Bioinformatic Data-1978 to present • DNA sequence • Gene expression • Protein 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 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

Information Deluge….. A researcher would have to scan 130 different journals and read 27 papers per dayto follow a single disease, such as breast cancer (Baasiri et al., 1999 Oncogene 18: 7958-7965). • 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

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

Link-driven Federations • Creates explicit links between databanks • 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/

Querying Entrez-Gene

Data Warehousing Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse. • Advantages • Good for very-specific, task-based queries and studies. • Since it is custom-built and usually expert-curated, relatively less error-prone • Disadvantages • Can become quickly outdated – needs constant updates. • 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 (dbSNP) • Computational vs. Literature-based Data Collection and Evaluation (MedLine) • Data Mining • extraction of new knowledge • testable hypotheses (Hypothesis Generation)

Gene World Biomedical World No Integrative Genomics is Complete without Ontologies • Gene Ontology (GO) • 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 annotationsinto 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) • miRNA regulators • Interactions • Other features…..

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

Example: Breast cancer 15 342 2469

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

TOPPGene - Data Sources • Gene Ontology: GO and NCBI Entrez Gene • Mouse Phenotype: MGI (used for the first time for human disease gene prioritization) • Pathways: KEGG, BioCarta, BioCyc, Reactome, GenMAPP, MSigDB • Domains: UniProt (Pfam, Interpro,etc.) • Interactions: NCBI Entrez Gene (Biogrid, Reactome, BIND, HPRD, etc.) • Pubmed IDs: NCBI Entrez Gene • Expression: GEO • Cytoband: MSigDB • Cis-Elements: MSigDB • miRNA Targets: MSigDB New features added

Benefits of Integrative Genomics • To unravel the connection between genotype and phenotype - Systematically identify novel phenotype–genotype relationships. • Hypotheses generator. • Paves way for prognosis, diagnosis, and personalized medicine (adverse drug reactions, etc.). • Deeper understanding of disease and an enhanced integration of medicine with biology. • 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 • 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…

CSCE555 Bioinformatics