1. Disease s Anatom y Genes Physiolog y Diseases Physiology Anatomy Genes Diseases Medical Informatics Bioinformatics Novel relationships & Deeper insights

2. 10/6/2015 Mining Bio-Medical Mountains Anil Jegga Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center (CCHMC) Department of Pediatrics, University of Cincinnati http://anil.cchmc.org Anil.Jegga@cchmc.org Integrative Genomics For Understanding Disease Process

3. Acknowledgement Biomedical Engineering/Bioinformatics Jing Chen Sivakumar Gowrisankar Vivek Kaimal Computer Science Amit Sinha Mrunal Deshmukh Divya Sardana Sandhya Shahdeo Electrical Engineering Nishanth Vepachedu

4. Medical Informatics Bioinformatics & the “omes Patient Records 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 Clinical Trials Two Separate Worlds….. With Some Data Exchange… Genome Transcriptome miRNAome Interactome Metabolome Physiome Regulome Variome Pathome Pharmacogenome 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

5. now…. The number 1 FAQ How much biology should I know?? No simple or straight-forward answer… unfortunately! But the mantra is : Interact routinely with biologists OR Work with the biologists or the biological data

6. But I want to learn some basics… 1.http://www.ncbi.nlm.nih.gov/Education 2.http://www.ebi.ac.uk/2can/ 3.http://www.genome.gov/Education/ 4.http://genomics.energy.gov/ Books 1.Introduction to Bioinformatics by Teresa Attwood, David Parry- Smith 2.A Primer of Genome Science by Gibson G and Muse SV 3.Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition by Andreas D. Baxevanis, B. F. Francis Ouellette 4.Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology by Dan Gusfield 5.Bioinformatics: Sequence and Genome Analysis by David W. Mount 6.Discovering Genomics, Proteomics, and Bioinformatics by A. Malcolm Campbell and Laurie J. Heyer

7. And the other FAQs…. 1.What bioinformatics topics are closest to computer science? 2.Should computer science departments involve themselves in preparing their graduates for careers in bioinformatics? 3.And if so, what topics should they cover? 4.And how much biology should they be taught? 5.Lastly, how much effort should be expended in re-directing computer scientists to do work in bioinformatics? Cohen, 2005; Communications of the ACM

8. Issues to be considered…….. 1.Computer science Vs molecular biology – Subject & Scientists - Cultural differences 2.Current goals of molecular biology, genomics (or biomedical research in a broader sense) 3.Data types used in bioinformatics or genomics 4.Areas within computer science of interest to biologists 5.Bioinformatics research - Employment opportunities

9. Biological Challenges - Computer Engineers Post-genomic Era and the goal of bio- medicine –to develop a quantitative understanding of how living things are built from the genome that encodes them. Deciphering the genome code –Identifying unknown genes and assigning function by computational analysis of genomic sequence –Identifying the regulatory mechanisms –Identifying their role in normal development/states vs disease states

10. Data Deluge: exponential growth of data silos and different data types –Human-computer interaction specialists need to work closely with academic and clinical biomedical researchers to not only manage the data deluge but to convert information into knowledge. Biological data is very complex and interlinked! –Creating information systems that allow biologists to seamlessly follow these links without getting lost in a sea of information - a huge opportunity for computer scientists. Biological Challenges - Computer Engineers

11. Networks, networks, and networks! –Each gene in the genome is not an independent entity. Multiple genes interact to perform a specific function. –Environmental influences – Genotype- environment interaction –Integrating genomic and biochemical data together into quantitative and predictive models of biochemistry and physiology –Computer scientists, mathematicians, and statisticians - ALL are/will be an integral and critical part of this effort. Biological Challenges - Computer Engineers A major goal in molecular biology is Functional Genomics – Study of the relationships among genes in DNA & their function – in normal and disease states

12. Informatics – Biologists’ Expectations Representation, Organization, Manipulation, Distribution, Maintenance, and Use of information, particularly in digital form. Functional aspect of bioinformatics: Representation, Storage, and Distribution of data. –Intelligent design of data formats and databases –Creation of tools to query those databases –Development of user interfaces or visualizations that bring together different tools to allow the user to ask complex questions or put forth testable hypotheses.

13. Developing analytical tools to discover knowledge in data –Levels at which biological information is used: comparing sequences – predict function of a newly discovered gene breaking down known 3D protein structures into bits to find patterns that can help predict how the protein folds modeling how proteins and metabolites in a cell work together to make the cell function……. Informatics – Biologists’ Expectations

14. Finally…. What does informatics mean to biologists? The ultimate goal of analytical bioinformaticians is to develop predictive methods that allow biomedical researchers and scientists to model the function and phenotype of an organism based only on its genomic sequence. This is a grand goal, and one that will be approached only in small steps, by many scientists from different but allied disciplines working cohesively.

15. Biology – Data Structures Four broad categories : 1.Strings: To represent DNA, RNA, amino acid sequences of proteins 2.Trees: To represent the evolution of various organisms (Taxonomy) or structured knowledge (Ontologies) 3.Sets of 3D points and their linkages: To represent protein structures 4.Graphs: To represent metabolic, regulatory, and signaling networks or pathways

16. Biology – Data Structures Biologists are also interested in 1.Substrings 2.Subtrees 3.Subsets of points and linkages, and 4.Subgraphs. Beware: Biological data is often characterized by huge size, the presence of laboratory errors (noise), duplication, and sometimes unreliability.

17. Support Complex Queries – A typical demand Get me all genes involved in or associated with brain development that are differentially expressed in the Central Nervous System. Get 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?

18. Model Organism Databases: Common Issues 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)

19. 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………..

20. 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

21. The Gene Expression Data Deluge Till 2000: 413 papers on microarray! Year PubMed Articles 2001834 20021557 20032421 20043508 20054400 20064824 20075108 20085023… Problems Deluge! Allison DB, Cui X, Page GP, Sabripour M. 2006. Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet. 7(1): 55-65.

22. 3 scientific journals in 1750 Now - >120,000 scientific journals! >600,000 medical articles/year >4,000,000 scientific articles/year >18 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).

23. Accelerin Antiquitin Bang Senseless Bride of Sevenless Christmas Factor Cockeye Crack Draculin Dickie’s small eye Disease names Mobius Syndrome with Poland’s Anomaly Werner’s syndrome Down’s syndrome Angelman’s syndrome Creutzfeld-Jacob disease Draculin Fidgetin Gleeful Knobhead Lunatic Fringe Mortalin Orphanin Profilactin Sonic Hedgehog Data-driven Problems….. Gene Nomenclature How to name or describe proteins, genes, drugs, diseases and conditions consistently and coherently? How to ascribe and name a function, process or location consistently? How to describe interactions, partners, reactions and complexes? Develop/Use controlled or restricted vocabularies (IUPAC-like naming conventions, HGNC, MGI, UMLS, etc.) Create/Use thesauruses, central repositories or synonym lists (MeSH, UMLS, etc.) Work towards synoptic reporting and structured abstracting Some Solutions 1.Generally, the names refer to some feature of the mutant phenotype 2.Dickie’s small eye (Thieler et al., 1978, Anat Embryol (Berl), 155: 81-86) is now Pax6 3.Gleeful: "This gene encodes a C 2 H 2 zinc finger transcription factor with high sequence similarity to vertebrate Gli proteins, so we have named the gene gleeful (Gfl)." (Furlong et al., 2001, Science 293: 1632) What’s in a name! Rose is a rose is a rose is a rose!

24. Rose is a rose is a rose is a rose….. Not Really! Image Sources: Somewhere from the internet… What is a cell? any small compartment (biology) the basic structural and functional unit of all organisms; they may exist as independent units of life (as in monads) or may form colonies or tissues as in higher plants and animals a device that delivers an electric current as a result of chemical reaction a small unit serving as part of or as the nucleus of a larger political movement cellular telephone: a hand-held mobile radiotelephone for use in an area divided into small sections, each with its own short- range transmitter/receiver small room in which a monk or nun lives a room where a prisoner is kept

25. Foundation Model Explorer Semantic Groups, Types and Concepts: Semantic Group Biology – Semantic Type Cell Semantic Groups Object OR Devices – Semantic Types Manufactured Device or Electrical Device or Communication Device Semantic Group Organization – Semantic Type Political Group

26. Database name No. of Records Query= p53 Query= TP53 (HGNC) Query= p53 OR TP53 PubMed46,838304147,566 PMC16,490103716,750 Book782504820 Nucleotide94735929773 Protein62195096377 Genome22123 OMIM403141414 SNP424337453 Gene16423381750 Homologene63968 GEO Profiles352,68415,140358,999 Cancer Chr302161463

27. Hepatocellular Carcinoma CTNNB1 MET TP53 1.COLORECTAL CANCER [3-BP DEL, SER45DEL] 2.COLORECTAL CANCER [SER33TYR] 3.PILOMATRICOMA, SOMATIC [SER33TYR] 4.HEPATOBLASTOMA, SOMATIC [THR41ALA] 5.DESMOID TUMOR, SOMATIC [THR41ALA] 6.PILOMATRICOMA, SOMATIC [ASP32GLY] 7.OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC [SER37CYS] 8.HEPATOCELLULAR CARCINOMA SOMATIC [SER45PHE] 9.HEPATOCELLULAR CARCINOMA SOMATIC [SER45PRO] 10.MEDULLOBLASTOMA, SOMATIC [SER33PHE] 1.COLORECTAL CANCER [3-BP DEL, SER45DEL] 2.COLORECTAL CANCER [SER33TYR] 3.PILOMATRICOMA, SOMATIC [SER33TYR] 4.HEPATOBLASTOMA, SOMATIC [THR41ALA] 5.DESMOID TUMOR, SOMATIC [THR41ALA] 6.PILOMATRICOMA, SOMATIC [ASP32GLY] 7.OVARIAN CARCINOMA, ENDOMETRIOID TYPE, SOMATIC [SER37CYS] 8.HEPATOCELLULAR CARCINOMA SOMATIC [SER45PHE] 9.HEPATOCELLULAR CARCINOMA SOMATIC [SER45PRO] 10.MEDULLOBLASTOMA, SOMATIC [SER33PHE] 1.HEPATOCELLULAR CARCINOMA SOMATIC [ARG249SER] TP53* aflatoxin B1, a mycotoxin induces a very specific G- to-T mutation at codon 249 in the tumor suppressor gene p53. Environmental Effects Many disease states are complex, because of many genes (alleles & ethnicity, gene families, etc.), environmental effects (life style, exposure, etc.) and the interactions. The REAL Problems

28. HEPATOCELLULAR CARCINOMA LIVER: Hepatocellular carcinoma; Micronodular cirrhosis; Subacute progressive viral hepatitis NEOPLASIA: Primary liver cancer CTNNB1 MET TP53 1.ALK in cardiac myocytes 2.Cell to Cell Adhesion Signaling 3.Inactivation of Gsk3 by AKT causes accumulation of b-catenin in Alveolar Macrophages 4.Multi-step Regulation of Transcription by Pitx2 5.Presenilin action in Notch and Wnt signaling 6.Trefoil Factors Initiate Mucosal Healing 7.WNT Signaling Pathway 1.ALK in cardiac myocytes 2.Cell to Cell Adhesion Signaling 3.Inactivation of Gsk3 by AKT causes accumulation of b-catenin in Alveolar Macrophages 4.Multi-step Regulation of Transcription by Pitx2 5.Presenilin action in Notch and Wnt signaling 6.Trefoil Factors Initiate Mucosal Healing 7.WNT Signaling Pathway 1.CBL mediated ligand-induced downregulation of EGF receptors 2.Signaling of Hepatocyte Growth Factor Receptor 1.CBL mediated ligand-induced downregulation of EGF receptors 2.Signaling of Hepatocyte Growth Factor Receptor 1. Estrogen-responsive protein Efpcontrols cell cycle and breast tumorsgrowth 2. ATM Signaling Pathway 3. BTG family proteins and cell cycleregulation 4. Cell Cycle 5. RB Tumor Suppressor/CheckpointSignaling in response to DNAdamage 6. Regulation of transcriptional activityby PML 7. Regulation of cell cycle progressionby Plk3 8. Hypoxia and p53 in theCardiovascular system 9. p53 Signaling Pathway 10. Apoptotic Signaling in Response toDNA Damage 11. Role of BRCA1, BRCA2 and ATR inCancer Susceptibility….Many More….. 1. Estrogen-responsive protein Efpcontrols cell cycle and breast tumorsgrowth 2. ATM Signaling Pathway 3. BTG family proteins and cell cycleregulation 4. Cell Cycle 5. RB Tumor Suppressor/CheckpointSignaling in response to DNAdamage 6. Regulation of transcriptional activityby PML 7. Regulation of cell cycle progressionby Plk3 8. Hypoxia and p53 in theCardiovascular system 9. p53 Signaling Pathway 10. Apoptotic Signaling in Response toDNA Damage 11. Role of BRCA1, BRCA2 and ATR inCancer Susceptibility….Many More….. The REAL Problems

29. Integrative Genomics - what is it? Another buzzword or a meaningful concept useful for biomedical research? 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

30. 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. 3.Others….. Semantic Web, etc……… Methods for Integration

31. 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

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

37. 1.Advantages complex queries Fast 2.Disadvantages require good knowledge syntax based terminology problem not solved Link-driven Federations

38. 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.

39. GATACA – Genetic Associations to Anatomy and Clinical Abnormalities (http://gataca.cchmc.org)

40. 1.Finding similarities among strings 2.Detecting certain patterns within strings 3.Finding similarities among parts of spatial structures (e.g. motifs) 4.Constructing trees Phylogenetic or taxonomic trees: evolution of an organism Ontologies – structured/hierarchical representation of knowledge 5.Classifying new data according to previously clustered sets of annotated data Algorithms in Bioinformatics

41. 6.Reasoning about microarray data and the corresponding behavior of pathways 7.Predictions of deleterious effects of changes in DNA sequences 8.Computational linguistics: NLP/Text- mining. Published literature or patient records 9.Graph Theory – Gene regulatory networks, functional networks, etc. 10.Visualization and GUIs (networks, application front ends, etc.) Algorithms in Bioinformatics

42. 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…..

43. 1.Most of the common diseases are multi- factorial and modified by genetically and mechanistically complex polygenic interactions and environmental factors. 2.High-throughput genome-wide studies like linkage analysis and gene expression profiling, tend to be most useful for classification and characterization but do not provide sufficient information to identify or prioritize specific disease causal genes. Background, Problems & Issues

44. 3.Since multiple genes are associated with same or similar disease phenotypes, it is reasonable to expect the underlying genes to be functionally related. 4.Such functional relatedness (common pathway, interaction, biological process, etc.) can be exploited to aid in the finding of novel disease genes. For e.g., genetically heterogeneous hereditary diseases such as Hermansky-Pudlak syndrome and Fanconi anaemia have been shown to be caused by mutations in different interacting proteins. Background, Problems & Issues

45. 1.Direct protein–protein interactions (PPI) are one of the strongest manifestations of a functional relation between genes. 2.Hypothesis: Interacting proteins lead to same or similar disease phenotypes when mutated. 3.Several genetically heterogeneous hereditary diseases are shown to be caused by mutations in different interacting proteins. For e.g. Hermansky-Pudlak syndrome and Fanconi anaemia. Hence, protein–protein interactions might in principle be used to identify potentially interesting disease gene candidates. PPI - Predicting Disease Genes

46. 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

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

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

49. 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

52. PubMed Medical Informatics Patient Record s Disease Databas e → 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…… Clinical Trials Bioinformatics Genome Transcriptome Proteome Interactome Metabolome Physiome Regulome Variome Pathome Pharmacogenome Disease World OMIM ► Personalized Medicine ► Decision Support System ► Outcome Predictor ► Course Predictor ► Diagnostic Test Selector ► Clinical Trials Design ► Hypothesis Generator….. the Ultimate Goal…….

53. http://sbw.kgi.edu/ Thank You! & YOU