Title: Bioinformatics
1Bioinformatics Computational Biology
- Thanks to Mark Gerstein (Yale), Eric Green (NIH)
Drena Dobbs (ISU) - for many borrowed modified PPTs
2What 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
- Complications
- Cutup into Pieces
- Chemical Modifications
- Central Paradigm for Bioinformatics
- Genomic (DNA) Sequence
- -gt mRNAs other RNA sequences
- -gt Protein sequences -gt RNA
Protein Structures -gt RNA Protein
Functions -gt Phenotype - Large Amounts of Information
- Standardized
- Statistical
Modified from Mark Gerstein
idea from D Brutlag, Stanford, graphics from S
Strobel)
3Explosion of "Omes" "Omics!"Genome,
Transcriptome, Proteome
- 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 of
proteins expressed in an organism
4Genome ConstantTranscriptome Proteome
Variable
- Genome - the complete collection of DNA (genes
and "non-genes") of an organism
Note Although the DNA is "identical" in all
cells of an organism, the sets of RNAs or
proteins expressed in different cells tissues
of a single organism vary greatly -- and depend
on variables such as environmental conditions,
age. developmental stage disease state, etc.
- Transcriptome - the complete collection of RNAs
(mRNAs others) expressed in an organism
- Proteome - the complete collection of proteins
expressed in an organism
5Molecular 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
6Molecular 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
- 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
7Molecular 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
8We don't yet understand the protein folding code
- but we try to engineer proteins anyway!
Modified from Mark Gerstein
9Molecular 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?
10On 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
11Human Genome Sequencing
- Two approaches
- Public (government) - International Consortium
- (6 countries, NIH-funded in US)
- "Hierarchical" cloning BAC-by-BAC sequencing
- Map-based assembly
- Private (industry) - Celera (Craig Venter)
- Whole genome random "shotgun" sequencing
- Computational assembly
- (took advantage of public maps
sequences,too) - Guess which human genome they sequenced? Craig
Venters -
12Next 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
13Gene 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)
14Other 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
15Molecular 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
16Storing 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" was among 100 most
powerful in the world
Modified from Mark Gerstein
(Internet picture adaptedfrom D Brutlag, Stanford)
17Bioinformatics is born! more Bioinformaticists
are needed!
(Internet picture adaptedfrom D Brutlag,
Stanford)
(courtesy of Finn Drablos)
Modified from Mark Gerstein
18Informatics 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
19Challenges 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
20Molecular Parts Conserved Domains
Modified from Mark Gerstein
21"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
22World 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....
23Is this Bioinformatics? (1,with Answers)
- Creating digital libraries
- Automated bibliographic search and textual
comparison - Knowledge bases for biological literature
- Motif discovery using Gibb's sampling
- Methods for structure determination
- Computational X-ray crystallography
- NMR structure determination
- Distance Geometry
- Metabolic pathway simulation
- The DNA Computer
YES
YES
No?? YES
YES
No
Modified from Mark Gerstein
24Is this Bioinformatics? 2
- Gene identification by sequence inspection
- Prediction of splice sites, promoters, etc.
- DNA methods in forensics
- Modeling populations of organisms
- Ecological Modeling
- Genomic sequencing methods
- Assembling contigs
- Physical and genetic mapping
- Linkage analysis
- Linking specific genes to various traits
YES
YES
YES
YES
YES
Modified from Mark Gerstein
25Is this Bioinformatics? 3
- Rational drug design
- RNA structure prediction
- Protein structure prediction
- Radiological image processing
- Computational representations for human anatomy
- (e.g., Visible Human)
- Artificial life simulations
- Artificial immunology
- Computer security
YES
Maybe
Yes/No
Modified from Mark Gerstein
26So, this is Bioinformatics
What is it good for?
27Application 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
28Application II Finding homologs
Modified from Mark Gerstein
29Finding 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
30Application IIIGenome/Transcriptome/ProteomeCha
racterization Comparison
- Databases, statistics
- Occurrence of a 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
31Resources for Bioinformatics Computational
Biology
- NCBI - National Center for Biotechnology
Information - ISCB - International Society for Computational
Biology - JCB - Jena Center for Bioinformatics
- UBC - Bioinformatics Links Directory
32ISU Resources Experts
- ISU Research Centers Graduate Training
Programs - BCB - Bioinformatics Computational Biology
- Baker Center - Bioinformatics Biological
Statistics - CIAG - Center for Integrated Animal Genomics
- CILD - Computational Intelligence, Learning
Discovery - ISU Facilities
- Biotech - Instrumentation Facilities
- CIAG - Center for Integrated Animal Genomics
- PSI - Plant Sciences Institute
- PSI Centers
33For fun DNA Interactive "Genomes" A tutorial
on genomic sequencing, gene structure, genes
prediction Howard Hughes Medical Institute
(HHMI) Cold Spring Harbor Laboratory (CSHL)
http//www.dnai.org/c/index.html
34 Building Designer Zinc Finger DNA-binding
Proteins J Sander, P Zaback, Fengli Fu, J
Townsend, R Winfrey D Wright, K Joung, L
Miller, D Dobbs, K Joung, D Voytas
35 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
36Designing New HIV Therapies
Susan Carpenter, WSU Sijun Liu Wendy
SparksDrena Dobbs, GDCB Jae-Hyung LeeKai-Ming
Ho, Physics Yungok Ihm Haibo Cao Cai-zhuang
WangGloria Culver, BBMBAmy Andreotti,
BBMBBruce Fulton, NMR FacilityVasant Honavar,
Com S
37Predicting Macromolecular Interfaces from Amino
Acid Sequence
Vasant Honavar, Com S Changhui Yan Cornelia
CarageaDrena Dobbs, GDCB Jae-Hyung Lee Michael
Terribilini Jeff Sander Pete ZabackRobert
Jernigan, BBMB Taner Sen Andrzej Kloczkowski
Kai-Ming Ho, Physics