DiseasesDiseasesDiseases

DiseasesDiseasesDiseases

Diseases

Anatomy

Anatomy

Anatomy

Anatomy

Anatomy

Anatomy

Gen

esG

enes

Gen

esG

enes

Gen

esG

enes

Physiology Physiolog

y Physiology Physiolog

y Physiology Physiology

Diseases

PhysiologyAnatomy

Genes

Genes

GenesDiseases

Diseases

Medical Informatics

Bioinformatics

Novel relationships & Deeper insights

04/19/23

Mining Bio-Medical Mountains

How Computer Science can help Biomedical Research and Health

Sciences

& YOU

Anil JeggaDivision of Biomedical Informatics,

Cincinnati Children’s Hospital Medical Center (CCHMC)Department of Pediatrics, University of Cincinnati

http://anil.cchmc.orgAnil.Jegga@cchmc.org

AcknowledgementBiomedical

Engineering/Bioinformatics• Jing Chen• Sivakumar Gowrisankar• Vivek KaimalComputer Science• Amit Sinha• Mrunal Deshmukh• Divya SardanaElectrical Engineering• Nishanth Vepachedu

Medical Informatics Bioinformatics & the “omes

Patient Records

Patient Records

Disease Database

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

Clinical Trials

Two Separate Worlds…..

With Some Data Exchange…

Genome

Transcriptome

miRNAome

Interactome

Metabolome

Physiome

Regulome Variome

Pathome Ph

arm

acog

en

om

e

OMIMClinical

Synopsis

Disease

World

382 “omes” so far………

and there is “UNKNOME” too - genes with no function knownhttp://omics.org/index.php/Alphabetically_ordered_list_of_omics

Proteome

now…. The number 1 FAQHow much biology should I

know??No simple or straight-forward answer…

unfortunately!But the mantra is:

Interact routinely with biologists

ORWork with the biologists or the

biological data

But I want to learn some basics…1. http://www.ncbi.nlm.nih.gov/Education2. http://www.ebi.ac.uk/2can/3. http://www.genome.gov/Education/4. http://genomics.energy.gov/Books1. Introduction to Bioinformatics by Teresa Attwood, David Parry-

Smith2. A Primer of Genome Science by Gibson G and Muse SV3. 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

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

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

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

• 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

• 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 will ALL be an integral and critical part of this effort.

Biological Challenges - Computer EngineersA major goal in

molecular biology is Functional Genomics

– Study of the relationships among genes in DNA & their function – in normal and disease states

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.

• Developing analytical tools to discover knowledge in data– Levels at 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

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.

Biology – Data StructuresFour 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

Biology – Data StructuresBiologists are also interested in1.Substrings2.Subtrees3.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.

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?

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)

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

Database name Records

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

The Gene Expression Data Deluge

Till 2000: 413 papers on microarray!

YearPubMed Articles

2001 834

2002 1557

2003 2421

2004 3508

2005 4400

2006 4824

2007 5108

2008 647…

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.

• 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).

•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 C2H2 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!

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

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

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]

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

HEPATOCELLULAR CARCINOMALIVER:

•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 Efp

controls cell cycle and breast tumors growth

2. ATM Signaling Pathway 3. BTG family proteins and cell

cycle regulation 4. Cell Cycle 5. RB Tumor

Suppressor/Checkpoint Signaling in response to DNA damage

6. Regulation of transcriptional activity by PML

7. Regulation of cell cycle progression by Plk3

8. Hypoxia and p53 in the Cardiovascular system

9. p53 Signaling Pathway 10. Apoptotic Signaling in Response

to DNA Damage 11. Role of BRCA1, BRCA2 and ATR

in Cancer Susceptibility….Many More…..

1. Estrogen-responsive protein Efp controls cell cycle and breast tumors growth

2. ATM Signaling Pathway 3. BTG family proteins and cell

cycle regulation 4. Cell Cycle 5. RB Tumor

Suppressor/Checkpoint Signaling in response to DNA damage

6. Regulation of transcriptional activity by PML

7. Regulation of cell cycle progression by Plk3

8. Hypoxia and p53 in the Cardiovascular system

9. p53 Signaling Pathway 10. Apoptotic Signaling in Response

to DNA Damage 11. Role of BRCA1, BRCA2 and ATR

in Cancer Susceptibility….Many More…..

The REAL Problems

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

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

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

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

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

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

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

1.Advantages• complex queries• Fast

2.Disadvantages• require good knowledge• syntax based• terminology problem not solved

Link-driven Federations

Data is downloaded, filtered, integrated and stored in a warehouse. Answers to queries are taken from the warehouse.

Data Warehousing

Advantages1. Good for very-specific,

task-based queries and studies.

2. Since it is custom-built and usually expert-curated, relatively less error-prone.

Disadvantages1. Can become quickly

outdated – needs constant updates.

2. Limited functionality – For e.g., one disease-based or one system-based.

1. Finding similarities among strings2. Detecting certain patterns within

strings3. 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

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

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…..

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

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

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

Known Disease Genes

Direct Interactants of Disease Genes

Mining human interactome

HPRDBioGrid

Which of these interactants are potential new candidates?

Indirect Interactants of Disease Genes

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

Example: Breast cancer

OMIM genes (level 0)

Directly interacting genes (level 1)

Indirectly interacting genes (level2)

15 342 2469!

15 342 2469

ToppGene – General Schema

PubMed

Medical Informatics

Patient Records

Patient Records

Disease Database

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……

Clinical Trials

Clinical Trials

Bioinformatics

Genome

Transcriptome

Proteome

Interactome

Metabolome

Physiome

Regulome Variome

Pathome

Ph

arm

acog

enom

e

Disease

World

OMIM

►Personalized Medicine►Decision Support System►Outcome Predictor►Course Predictor►Diagnostic Test Selector►Clinical Trials Design►Hypothesis Generator…..

Computer

Engineers

the Ultimate Goal…….

http://sbw.kgi.edu/Thank You!

“To him who devotes his life to science, nothing can give more happiness than increasing the number of discoveries, but his cup of joy is full when the results of his studies immediately find practical applications”

— Louis Pasteur

& YOU