Bioinformatics

1
Bioinformatics Computational BiologyPodcast
for Frontiers in Biology - ISU 7/13/06
Drena Dobbs Genetics, Development and Cell
Biology Bioinformatics Computational Biology
Iowa State University

• Thanks to Mark Gerstein (Yale)
• Eric Green (NIH)
• for many borrowed modified PPTs
  

2
What is Bioinformatics?( What is Computational
Biology?)

• Wikipedia
• Bioinformatics computational biology involve
  the use of techniques from mathematics,
  informatics, statistics, and computer science (
  engineering) to solve biological problems

3
What is Bioinformatics?( What is Computational
Biology?)

• Gerstein
• (Molecular) Bioinformatics is conceptualizing
  biology in terms of molecules applying
  informatics techniques - derived from
  disciplines such as mathematics, computer
  science, and statistics - to organize and
  understand information associated with these
  molecules, on a large scale

Modified from Mark Gerstein
4
What is the Information?Biological Sequences,
Structures, Processes

• Central Dogma of Molecular Biology
• DNA sequence -gt RNA -gt Protein -gt
  Phenotype
• Molecules
• Sequence, Structure, Function
• Processes
• Mechanism, Specificity, Regulation

• Central Paradigm for Bioinformatics
• Genomic (DNA) Sequence
• -gt mRNA other RNA sequence
• -gt Protein sequence -gt RNA
  Protein Structure -gt RNA Protein
  Function -gt Phenotype
• Large Amounts of Information
• Standardized
• Statistical

Modified from Mark Gerstein
idea from D Brutlag, Stanford, graphics from S
Strobel)
5
Explosion of "Omes" "Omics!"Genome,
Transcriptome, Proteome
Note the set of specific RNAs or proteins
expressed varies greatly in different cells and
tissues -- and critically depends on the age,
developmental stage, disease state, etc. of the
organism

• Genome - the complete collection of DNA (genes
  and "non-genes") of an organism

• Transcriptome - the complete collection of RNAs
  (mRNAs others) expressed in an organism

• Proteome - the complete collection of proteins
  expressed in an organism

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Molecular Biology Information DNA RNA
Sequences

• Functions
• Genetic material
• Information transfer (mRNA)
• Protein synthesis (tRNA/mRNA)
• Catalytic regulatory activities
• (some very new!)
• Information
• 4 letter alphabet
• (DNA nucleotides AGCT)
• 1,000 base pairs in a small gene
• 3 X 109 bp in a genome (human)

DNA sequence atggcaattaaaattggtatcaatggttttggtc
gtat gcacaacaccgtgatgacattgaagttgtaggtattaa atggct
tatatgttgaaatatgattcaactcacggtcg aaagatggtaacttagt
ggttaatggtaaaactatccg Gcaaacttaaactggggtgcaatcggtg
ttgatatcgctttaactgatgaaactgctcgtaaacatatcactgcaggc
gcaaaaaaagtt RNA sequence has "U" instead of "T"

• Where are the genes?
• Which DNA sequences encode mRNA?
• Which DNA sequences are "junk"?
• Which RNA sequences encode protein?

Modified from Mark Gerstein
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Molecular Biology Information Protein Sequences
Functions Most cellular functions are performed
or facilitated by proteins
Protein sequences d1dhfa_ LNCIVAVSQNMGIGKNGDLPWP
PLRNEFRYFQRMTT d8dfr__ LNSIVAVCQNMGIGKDGNLPWPPLRNE
YKYFQRMTS d4dfra_ ISLIAALAVDRVIGMENAMPWN-LPADLAWFK
RNTL d3dfr__ TAFLWAQDRDGLIGKDGHLPWH-LPDDLHYFRAQTV

• Biocatalysis
• Cofactor transport/storage
• Mechanical motion/support
• Immune protection
• Regulation of growth and differentiation
• Information
• 20 letter alphabet (amino acids)
• ACDEFGHIKLMNPQRSTVWY
• but not BJOUXZ
• 300 aa in an average protein
• (in bacteria)
• 3 X 106 known protein sequences

• What is this protein?
• Which amino acids are most important -- for
  folding, activity, interaction with other
  proteins?
• Which sequence variations are harmful (or,
  beneficial)?

Modified from Mark Gerstein
8
Molecular Biology InformationMacromolecular
Structures

• DNA/RNA/Protein Structures
• How does a protein (or RNA) sequence fold into an
  active 3-dimensional structure?
• Can we predict structure from sequence?
• Can we predict function from structure (or
  perhaps, from sequence alone?)

Modified from Mark Gerstein
9
We don't yet understand the protein folding code
- but we try to engineer proteins anyway!
Modified from Mark Gerstein
10
Molecular Biology InformationBiological
Processes

• Functional Genomics
• How do patterns of gene expression determine
  phenotype?
• Which genes and proteins are required for
  differentiation during during development?
• How do proteins interact in biological networks?
• Which genes and pathways have been most highly
  conserved during evolution?

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On a Large Scale?Whole GenomeSequencing
Genome sequence now accumulate so quickly that,
in less than a week, a single laboratory can
produce more bits of data than Shakespeare
managed in a lifetime, although the latter make
better reading. -- G A Pekso, Nature 401
115-116 (1999)
Modified from Mark Gerstein
12
Next Step after the Sequence?
Understanding Gene Function on a Genomic Scale

• Expression Analysis
• Structural Genomics
• Protein Interactions
• Pathway Analysis
• Systems Biology

• Evolutionary Implications of
• Introns Exons
• Intergenic Regions as "Gene Graveyard"

Modified from Mark Gerstein
13
Gene Expression Data the Transcriptome
MicroArray Data

• Yeast Expression Data
• Levels for all 6,000 genes!
• Experiments to investigate how genes respond to
  changes in environment or how patterns of
  expression change in normal vs cancerous tissue

ISU's Biotechnology Facilities include
state-of-the-art Microarray Proteomics
instrumentation
Modified from Mark Gerstein
(courtesy of J Hager)
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Other Whole-Genome Experiments
Systematic Knockouts Make "knockout" (null)
mutations in every gene - one at a time - and
analyze the resulting phenotypes! For yeast
6,000 KO mutants!
2-hybrid Experiments For each (and every)
protein, identify every other protein with which
it interacts! For yeast 6000 x 6000 / 2
18M interactions!!
Modified from Mark Gerstein
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Molecular Biology InformationIntegrating Data

• Understanding the function of genomes requires
  integration of many diverse and complex types of
  information
• Metabolic pathways
• Regulatory networks
• Whole organism physiology
• Evolution, phylogeny
• Environment, ecology
• Literature (MEDLINE)

Modified from Mark Gerstein
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Storing Analyzing Large-scale
InformationExponential Growth of Data Matched
by Development of Computer Technology

• CPU vs Disk Net
• Both the increase in computer speed and the
  ability to store large amounts of information on
  computers have been crucial
• Improved computing resources have been a driving
  force in Bioinformatics

ISU's supercomputer "CyBlue" is among 100 most
powerful in the world
Modified from Mark Gerstein
(Internet picture adaptedfrom D Brutlag, Stanford)
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Bioinformatics is born! more Bioinformaticists
are needed!
(Internet picture adaptedfrom D Brutlag,
Stanford)
(courtesy of Finn Drablos)
Modified from Mark Gerstein
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Weber Cartoon
from Mark Gerstein
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Informatics techniquesin Bioinformatics

• Databases
• Building, Querying
• Object-oriented DB
• String Comparison
• Text search
• Alignment
• Significance statistics
• Finding Patterns
• Machine Learning
• Data Mining
• Statistics
• Linguistics

• Geometry
• Robotics
• Graphics (Surfaces, Volumes)
• Comparison 3D Matching
• Simulation Modeling
• Newtonian Mechanics
• Electrostatics
• Numerical Algorithms
• Simulation
• Network modeling

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Challenges in Organizing InformationRedundancy
and Multiplicity

• Different sequences can have the same structure
• Organism has many similar genes
• Single gene may have multiple functions
• Genes and proteins function in genetic and
  regulatory pathways
• How do we organize all this information so that
  we can make sense of it?

Integrative Genomics genes gtlt structures ltgt
functions ltgt pathways ltgt expression levels
ltgtregulatory systems ltgt .
Modified from Mark Gerstein
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Molecular Parts Conserved Domains
Modified from Mark Gerstein
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"Parts List" approach to bike maintenance
How many roles can these play? How flexible and
adaptable are they mechanically?
What are the shared parts (bolt, nut, washer,
spring, bearing), unique parts (cogs, levers)?
What are the common parts -- types of parts (nuts
washers)?
Where are the parts located?
Modified from Mark Gerstein
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World of structures is also finite,providing a
valuable simplification
(human)
30,000 genes
2,000 folds
(T. pallidum)
2,000 genes
Global Surveys of a Finite Set of Parts from Many
Perspectives Same logic for pathways, functions,
sequence families, blocks, motifs....
Modified from Mark Gerstein
Functions picture from www.fruitfly.org/suzi
(Ashburner) Pathways picture from,
ecocyc.pangeasystems.com/ecocyc (Karp, Riley).
Related resources COGS, ProDom, Pfam, Blocks,
Domo, WIT, CATH, Scop....
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So, this is Bioinformatics
What is it good for?
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Application IDesigning Drugs

• Understanding how proteins bind other molecules
• Docking structure modeling
• Designing inhibitors

Figures adapted from Olsen Group Docking Page at
Scripps, Dyson NMR Group Web page at Scripps,
and from Computational Chemistry Page at Cornell
Theory Center).
Modified from Mark Gerstein
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Application II Finding homologs
Modified from Mark Gerstein
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Finding WHAT? Homologs - "same genes" in
different organisms

• Human vs. Mouse vs. Yeast
• Much easier to do experiments on yeast!

Best Sequence Similarity Matches to Date Between
Positionally Cloned Human Genes and S. cerevisiae
Proteins Human Disease
MIM Human GenBank BLASTX Yeast
GenBank Yeast Gene
Gene Acc for P-value
Gene Acc for Description
Human cDNA
Yeast cDNA Hereditary
Non-polyposis Colon Cancer 120436 MSH2
U03911 9.2e-261 MSH2 M84170 DNA
repair protein Hereditary Non-polyposis Colon
Cancer 120436 MLH1 U07418 6.3e-196 MLH1
U07187 DNA repair protein Cystic Fibrosis
219700 CFTR M28668
1.3e-167 YCF1 L35237 Metal resistance
protein Wilson Disease
277900 WND U11700 5.9e-161 CCC2
L36317 Probable copper transporter Glycerol
Kinase Deficiency 307030 GK
L13943 1.8e-129 GUT1 X69049 Glycerol
kinase Bloom Syndrome
210900 BLM U39817 2.6e-119 SGS1
U22341 Helicase Adrenoleukodystrophy,
X-linked 300100 ALD Z21876
3.4e-107 PXA1 U17065 Peroxisomal ABC
transporter Ataxia Telangiectasia
208900 ATM U26455 2.8e-90 TEL1
U31331 PI3 kinase Amyotrophic Lateral
Sclerosis 105400 SOD1 K00065
2.0e-58 SOD1 J03279 Superoxide
dismutase Myotonic Dystrophy
160900 DM L19268 5.4e-53 YPK1
M21307 Serine/threonine protein kinase Lowe
Syndrome 309000 OCRL
M88162 1.2e-47 YIL002C Z47047 Putative
IPP-5-phosphatase Neurofibromatosis, Type 1
162200 NF1 M89914 2.0e-46 IRA2
M33779 Inhibitory regulator
protein Choroideremia
303100 CHM X78121 2.1e-42 GDI1
S69371 GDP dissociation inhibitor Diastrophic
Dysplasia 222600 DTD U14528
7.2e-38 SUL1 X82013 Sulfate
permease Lissencephaly
247200 LIS1 L13385 1.7e-34 MET30
L26505 Methionine metabolism Thomsen Disease
160800 CLC1 Z25884
7.9e-31 GEF1 Z23117 Voltage-gated
chloride channel Wilms Tumor
194070 WT1 X51630 1.1e-20 FZF1
X67787 Sulphite resistance
protein Achondroplasia
100800 FGFR3 M58051 2.0e-18 IPL1
U07163 Serine/threoinine protein
kinase Menkes Syndrome
309400 MNK X69208 2.1e-17 CCC2
L36317 Probable copper transporter
Modified from Mark Gerstein
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Application IIIGenome/Transcriptome/ProteomeCha
racterization Comparison

• Databases, statistics
• Occurrence of specific genes or features in a
  genome
• How many kinases in yeast?
• Compare Tissues
• Which proteins are expressed in cancer vs normal
  tissues?
• Diagnostic tools
• Drug target discovery

Modified from Mark Gerstein
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Building Designer Zinc Finger DNA-binding
Proteins J Sander, Fengli Fu, J Townsend, R
Winfrey D Wright, K Joung, D Dobbs, D Voytas
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Identifying "Missing" Components of Signal
Transduction Pathways
Phil Becraft, GDCB Antony ChettoorDrena
Dobbs, GDCB Jae-Hyung LeeKai-Ming Ho, Physics
Zhong Gao Yungok Ihm Haibo Cao Cai-zhuang
Wang
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Designing New HIV Therapies
Susan Carpenter, VMPM Sijun Liu Wendy
WoodDrena Dobbs, GDCB Jae-Hyung LeeKai-Ming
Ho, Physics Astronomy Yungok Ihm Haibo
Cao Cai-zhuang WangAmy Andreotti,BBMBBruce
Fulton, NMR FacilityVasant Honavar, Com
S Changhui Yan
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Predicting Protein-Protein Interactions from
Amino Acid Sequence
Vasant Honavar, Com S Changhui YanDrena
Dobbs, GDCB Jae-Hyung LeeKai-Ming Ho, Physics
Robert Jernigan, BBMB