Transcript 02_Aug22_BiolDB

BCB 444/544
Finish: Lecture 1- What is Bioinformatics?
Lecture 2
Biological Databases
&
ISU Resources
#2_Aug22
BCB 444/544 F07 ISU
Dobbs #2 - Biological Databases
8/22/07
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BCB 444/544 - Website
http://bindr.gdcb.iastate.edu/bcb544
• Updated Syllabus
• Lecture & Lab Schedules
(with Homework Assignments)
• Lecture PPTs & PDFs
• Lab Exercises
• Practice Exams
• Grading Policy
• Project Guidelines, etc.
• Links
• Check regularly for updates!
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BCB 444/544 - Computer Lab
Meets in 1304 MBB every week
EXCEPT this week:
1st Lab meets in Library Rm 32
Current schedule: Thurs 1-3 PM
Conflicts? See Drena
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Assignment #1:
Tell us about you
Due: Today - Wed, Aug 22
1- Complete HW1_Aug20 for Drena
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Required Reading
(must read before lecture)
Wed Aug 22 - for Lecture #2
• Xiong Textbook:
• Chp 1 - Introduction
• Chp 2 - Biological Databases
Thurs Aug 23 - for Lab #1:
• Literature Resources for Bioinformatics
Andrea Dinkelman, see Lab Schedule for URL
Fri Aug 24
• Genomics & Its Impact on Science & Society:
Genomics & Human Genome Project Primer
see Lecture Schedule for URL
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Assignment #2 (& for Fun):
DNA Interactive "Genomes"
http://www.dnai.org/c/index.html
A tutorial on genomic sequencing, gene structure,
genes prediction
Howard Hughes Medical Institute (HHMI)
Cold Spring Harbor Laboratory (CSHL)
1.
2.
3.

Take the Tour
Read about the Project
Do some Genome Mining with:
Nothing to turn in - just do it!
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#1- What is Bioinformatics?
(cont.)
Xiong: Chp 1
1 Introduction
What Is Bioinformatics?
Goal
Scope
Applications
Limitations
New Themes
Further Reading
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1st Draft Human Genome:
"Finished" in 2001
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Modified from Eric Green
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Human Genome Sequencing
Two approaches:
• Public (government) - International Consortium
(mainly 6 countries, NIH-funded in US)
• Hierarchical cloning & BAC-to-BAC sequencing
• Map-based assembly
• Private (industry) - Celera, Craig Venter, CEO
• Whole genome random "shotgun" sequencing
• Computational assembly
(took advantage of public maps & sequences, too)
Guess which human genome they sequenced?
How many genes?
~
Craig's
20,000 (Science, May 2007)
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Public Sequencing:
International Consortium
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Modified from Eric Green
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Comparison of Sequenced Genome Sizes
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Plants? Many have much larger genomes than human!
Modified from Eric Green
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"Complete" Human Genome Sequence:
What next?
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
from Eric Green
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Next Step after the Complete Sequence?
Understanding Gene Function on a Genomic Scale
• Expression Analysis
• Structural Genomics
• Protein Interactions
• Network Analysis
• Systems Biology
Evolutionary Implications of:
• Intergenic Regions as "Gene Graveyard"
• Introns & Exons
Modified from Mark Gerstein
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How can we begin to understand the
complete Human Genome Sequence?
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
from Eric Green
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Comparative Genomics:
Compare entire genomes
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
from Eric Green
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Comparing Genomes:
Identifying functional elements
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
from Eric Green
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Gene Expression Data:
the Transcriptome
MicroArray Data
Yeast Expression Data:
• Levels for all 6,000 genes!
• Investigate how all genes
respond to changes in
environment or, in humans,
e.g., how patterns of RNA
expression change in
normal vs cancerous tissue
Modified from Mark Gerstein
BCB 444/544 F07 ISU
ISU's Biotechnology Facilities
include state-of-the-art
Microarray Instrumentation
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Other "Omes"
Proteome, Metabolome, Glycome, etc.
ISU has state-of-the-art
Proteomics Instrumentation
ISU's has state-of-the-art
Metabolomics Instrumentation
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How are "Omes" related?
Systems Biology seeks to integrate all of these to
explain the complex behaviors of whole systems
(cells, organisms, ecosystems)
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Molecular Biology Information:
Integrating Data
Understanding the function of genomes
requires integration of many diverse and
complex types of information:
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Metabolic pathways
Regulatory networks
Whole organism physiology
Evolution, phylogeny
Environment, ecology
Literature (MEDLINE)
Modified from Mark Gerstein
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Other Genome-Scale Experiments
Systematic Knockouts:
2-hybrid Experiments:
Make "knockout" (null)
mutations in every gene
- one at a time - and
analyze the resulting
phenotypes!
For each (and every)
protein, identify every
other protein with which it
interacts!
For yeast:
6,000 KO mutants!
For yeast: 6000 x 6000 / 2
~ 18M interactions!!
Modified from Mark Gerstein
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Storing & Analyzing Geonomic Information:
Exponential Growth of Data Coupled with
Development of Fast Computer Technology
• Increases in computer speed &
starage capacity have been dramatic
• Improved computing resources &
more efficient algorithms have been
driving forces in Bioinformatics &
Computational Biology
ISU's supercomputer "CyBlue" is among 100
most powerful computers in the world!
Modified from Mark Gerstein
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Bioinformatics is born!
& more Bioinformaticists are needed!
(Internet picture adapted
from D Brutlag, Stanford)
Modified from Mark Gerstein
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“Informatics” techniques used in
Bioinformatics
• Databases
• Computational Geometry
Building & querying objectoriented & relational DBs
• Robotics
• Graphics (surfaces, volumes)
• Comparison & 3D matching
• String Comparison
• Text search
• Alignment
• Significance statistics
• Simulation & Modeling
• Patterns Finding
•
•
•
•
Machine Learning
Data Mining
Statistics
Linguistics
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Newtonian mechanics
Electrostatics
Numerical algorithms
Simulation
Network modeling
Population modeling
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Challenges in Organizing Information:
Redundancy and Multiplicity
• Different protein sequences can assume
the same 3-D structure
• Organisms have many similar genes with
redundant functions
• A single gene may have several different
functions
• Genes & proteins function in complex
genetic & regulatory pathways
• How do we organize all this
information so that we can make
sense of it?
Functional Genomics & Systems Biology:
sequences <> motifs <> genes <> RNAs <> proteins <> structures <> functions <>
expression levels <> pathways <> regulatory networks <> functional systems
Modified from Mark Gerstein
BCB 444/544 F07 ISU
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One Strategy:
Molecular Parts = Conserved Domains
Modified from Mark Gerstein
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"Parts List" approach to bike maintenance:
Where are the parts
located?
Which are the common parts
(bolt, nut,washer, spring,
bearing)?
Which are unique parts
(cogs, levers)?
How flexible and adaptable
are parts mechanically?
Modified from Mark Gerstein
BCB 444/544 F07 ISU
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World of macromolecular structures is also
finite, providing a valuable simplification
H. sapiens
1
2
3
4
5
6
7
8
9
10 11
12 13
14 15 16
17 18 19
20
…
~ 20,000 genes
~ 2,000 folds
T. pallidum
1
2
3
4
5
6
7
8
9
10 11
12 13
14 15 …
~ 2,000 genes
Global surveys of a finite set
of parts from different
perspectives
Same logic for pathways, functions,
sequence families, blocks, motifs....
Modified from Mark Gerstein
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BUT, what actually happens inside cells or
within whole organisms is very complex providing a challenging complication !
Exploring the Virtual Cell at ISU
Virtual Cell projects elsewhere...
NCBI's Bookshelf - a great resource!
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So, having a list of parts is not enough!
BIG QUESTION?
How do parts work together to form a
functional system?
SYSTEMS BIOLOGY
What is a system? Macromolecular complex, pathway,
network, cell, tissue, organism, ecosystem…
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So, this is Bioinformatics
What is it good for?
Just a few examples…
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Designing drugs
• Understanding how proteins bind other molecules
• Structural modeling & ligand docking
• Designing inhibitors or modulators of key proteins
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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Finding homologs of "new" human genes
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 to determine function
• Often, function of an ortholog in at least one organism is known
Best Sequence Similarity Matches to Date Between Positionally Cloned
Human Genes and S. cerevisiae Proteins
Human Disease
MIM #
Human
Gene
GenBank
BLASTX
Acc# for
P-value
Human cDNA
Yeast
Gene
GenBank
Yeast Gene
Acc# for
Description
Yeast cDNA
Hereditary Non-polyposis Colon Cancer
Hereditary Non-polyposis Colon Cancer
Cystic Fibrosis
Wilson Disease
Glycerol Kinase Deficiency
Bloom Syndrome
Adrenoleukodystrophy, X-linked
Ataxia Telangiectasia
Amyotrophic Lateral Sclerosis
Myotonic Dystrophy
Lowe Syndrome
Neurofibromatosis, Type 1
120436
120436
219700
277900
307030
210900
300100
208900
105400
160900
309000
162200
MSH2
MLH1
CFTR
WND
GK
BLM
ALD
ATM
SOD1
DM
OCRL
NF1
U03911
U07418
M28668
U11700
L13943
U39817
Z21876
U26455
K00065
L19268
M88162
M89914
9.2e-261
6.3e-196
1.3e-167
5.9e-161
1.8e-129
2.6e-119
3.4e-107
2.8e-90
2.0e-58
5.4e-53
1.2e-47
2.0e-46
MSH2
MLH1
YCF1
CCC2
GUT1
SGS1
PXA1
TEL1
SOD1
YPK1
YIL002C
IRA2
M84170
U07187
L35237
L36317
X69049
U22341
U17065
U31331
J03279
M21307
Z47047
M33779
DNA repair protein
DNA repair protein
Metal resistance protein
Probable copper transporter
Glycerol kinase
Helicase
Peroxisomal ABC transporter
PI3 kinase
Superoxide dismutase
Serine/threonine protein kinase
Putative IPP-5-phosphatase
Inhibitory regulator protein
Choroideremia
Diastrophic Dysplasia
Lissencephaly
Thomsen Disease
Wilms Tumor
Achondroplasia
Menkes Syndrome
303100
222600
247200
160800
194070
100800
309400
CHM
DTD
LIS1
CLC1
WT1
FGFR3
MNK
X78121
U14528
L13385
Z25884
X51630
M58051
X69208
2.1e-42
7.2e-38
1.7e-34
7.9e-31
1.1e-20
2.0e-18
2.1e-17
GDI1
SUL1
MET30
GEF1
FZF1
IPL1
CCC2
S69371
X82013
L26505
Z23117
X67787
U07163
L36317
GDP dissociation inhibitor
Sulfate permease
Methionine metabolism
Voltage-gated chloride channel
Sulphite resistance protein
Serine/threoinine protein kinase
Probable copper transporter
Modified from Mark Gerstein
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Comparative Genomics:
Genome/Transcriptome/Proteome/Metabolome
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
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Molecular Recognition:
Analyzing & Predicting Macromolecular Interfaces
(in DNA, RNA & protein complexes)
Drena Dobbs, GDCB
Jae-Hyung Lee
Michael Terribilini
Jeff Sander
Pete Zaback
Vasant Honavar, Com S
Feihong Wu
Cornelia Caragea
Fadi Towfic
Jivo Sinapov
Robert Jernigan, BBMB
Taner Sen
Andrzej Kloczkowski
Kai-Ming Ho, Physics
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Designing Zinc Finger DNA-binding Proteins
to Recognize Specific Sites in Genomic DNA
Drena Dobbs, GDCB
Jeff Sander
Pete Zaback
Dan Voytas, GDCB
Fengli Fu
Les Miller, ComS
Vasant Honavar, ComS
Keith Joung, Harvard
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Structure & Function of Human Telomerase:
Predicting structure & functional sites in a clinically
important but "recalcitrant" RNP
Cell Biologist:
www.intl-pag.org/
Biochemist:
www.chemicon.com
Imagined structure:
Lingner et al (1997) Science 276: 561-567.
How would a systems biologist study telomerase?
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SUMMARY:
#1- What is Bioinformatics?
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#2- Biological Databases
Xiong: Chp 2
2 Introduction to Biological Databases
What Is a Database?
Types of Databases
Biological Databases
Pitfalls of Biological Databases
Information Retrieval from Biological Databases
Summary
Further Reading
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What is a Database?
Duh!!
OK: skip we'll skip that!
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Types of Databases
3 Major types of electronic databases:
1- Flat files - simple text files
• no organization to facilitate retrieval
2- Relational - data organized as tables ("relations")
• shared features among tables allows rapid search
3- Object-oriented - data organized as "objects"
• objects associated hierarchically
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Biological Databases
Currently - all 3 types, but MANY flat files
What are goals of biological databases?
1- Information retrieval
2- Knowledge discovery
Important issue:
Interconnectivity
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Types of Biological Databases
1- Primary
• "simple" archives of sequences, structures, images, etc.
• raw data, minimal annotations, not always well curated!
2- Secondary
• enhanced with more complete annotation of sequences,
structures, images, etc.
• usually curated!
3- Specialized
• focused on a particular research interest or organism
• usually - not always - highly curated
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Examples of Biological Databases
1- Primary
• DNA sequences
• GenBank - US
• European Molecular Biology Lab - EMBL
• DNA Data Bank of Japan - DDBI
• Structures (Protein, DNA, RNA)
• PDB - Protein Data Bank
• NDB - Nucleic Acid Databank
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Examples of Biological Databases
2- Secondary
• Protein sequences
• Swiss-Prot, TreEMBL, PIR
• these recently combined into UniProt
3- Specialized
• Species-specific (or "taxonomic" specific)
• Flybase, WormBase, AceDB, PlantDB
• Molecule-specific,disease-specific
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Pitfalls of Biological Databases
• Errors!
&
• Lack of documentation re: quality or reliability of data
• Limited mechanisms for "data checking" or preventing
propagation of errors (esp. annotation errors!!)
• Redundancy
• Inconsistency
• Incompatibility (format, terminology, data types, etc.)
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Information Retrieval
from Biological Databases
2 most popular retrieval systems:
• ENTREZ - NCBI
• will use a LOT - Introduced in Lab 1
• SRS - Sequence Retrieval Systems - EBI
• will use less, similar to ENTREZ
Both:
• Provide access to multiple databases
• Allow complex queries
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Web Resources:
Bioinformatics & Computational Biology
• Wikipedia:
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Bioinformatics
NCBI - National Center for Biotechnology Information
ISCB - International Society for Computational Biology
JCB - Jena Center for Bioinformatics
UBC - Bioinformatics Links Directory
UWa - BioMolecules
Pitt - OBRC Online Bioinformatics Resources Collection
• ISU - Bioinformatics Resources - Andrea Dinkelman
• ISU - YABI = "Yet Another Bioinformatics Index"
(from BCB Lab at ISU)
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ISU Resources & Experts
ISU Research Centers & Graduate Training Programs:
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•
BCB Lab - (Student-Led Consulting & Resources)
BCB - Bioinformatics & Computational Biology
LH Baker Center - Bioinformatics & Biological Statistics
CIAG - Center for Integrated Animal Genomics
CILD - Computational Intelligence, Learning & Discovery
NSF IGERT Training Grant - Computational Molecular Biology
ISU Facilities:
• Biotechnology - Instrumentation Facilities
• PSI - Plant Sciences Institute
• PSI Centers
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SUMMARY:
#2- Biological Databases
BEWARE!
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