IBGP 705 Biomedical Informatics
Director: Prof. Kun Huang
What is Bio(medical)-informatics?
bio·in·for·mat·ics
: the collection, classification, storage, and analysis of biochemical and
biological information using computers especially as applied in molecular
genetics and genomics.
Source: Merriam-Webster's Medical Dictionary, © 2002 Merriam-Webster,
Inc.
Myth1 : Bioinformatics is about
genomics
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•
Nucleotide – DNA, RNA, …
Genome – Sequences, chromosomes, expressed data, …
Protein – Sequences, 3-D structure, interaction, …
System – Gene network, protein network, TFs, …
Other – Masspec, microarray, images, lab records, journals, literatures, …
The goal is to understand how the system works.
Myth2 : Data vs. Information
Data
Nucleotide – DNA, RNA, …
Genome – Sequences, chromosomes, expressed data, …
Protein – Sequences, 3-D structure, interaction, …
System – Gene network, protein network, TFs, …
Other – Masspec, microarray, images, lab records, journals, literatures, …
Information
Genotype
Phenotype
Genotype-Phenotype relationship
SNPs
Pathways
Drug targets
Getting data is “easy”, extracting information is hard!
Myth3 : Computer is intelligent
Pros
• Repeated work
• Accurate storage
• Precise computation
• Fast communication
…
Cons
• Cannot generalize
• No real intelligence
…
The results must be reviewed and validated by
biologists. In addition, biologists must have some
understanding of how computer processes data
(algorithms) – that’s why we need to learn
bioinformatics.
Biology – Biomedical informatics –
System biology
Biomedical Informatics
System Sciences
Understanding!
Theory
Analysis
Modeling
• Synthesis/prediction
• Simulation
• Hypothesis generation
Prediction!
System Biology
Biology
Informatics
Domain knowledge
Data management
• Hypothesis testing
Experimental work
• Genetic manipulation
• Quantitative measurement
• Validation
• Database
Computational infrastructure
• Modeling tools
• High performance computing
Visualization
Where does large data come from
(who to blame)?
High-throughput techniques
Fred Sanger
• Nobel prize in chemistry
in 1958
"for his work on the
structure of proteins,
especially that of insulin"
• Nobel prize in chemistry
in 1980
"for their contributions
concerning the
determination of base
sequences in nucleic
acids"
High-throughput techniques
DNA Sequencing
• 1970’s – Nobel prize
• 1980’s – Ph.D. thesis
• Early 1990’s – Major
research projects
• Late 1990’s to now - $20
Human Genome Project
The Beginning (1988)
Cold Spring Harbor Laboratory
Long Island, New York
June 26, 2000 at the Whitehouse
Initial Analysis of the Human Genome
http://www.sanger.ac.uk/HGP/draft2000/gfx/fig2.gif
Genome Mapping
STS – sequence-tagged sites (short segments of unique
DNA on every chromosome – defined by a pair of PCR
primers that amplified only one segment of the genome)
BAC – Bacterial artificial chromosome, 100-400kb
YAC – Yeast artificial chromosome, 150kb-1.5Mb
Contig – assembled contiguous overlapping segments of
DNA from BACs and YACs
ESTs – Expressed Sequence Tags
UniGene Database – a database for ESTs
Shotgun Sequencing
Concepts in Biochemistry, 2nd Ed., R. Boyer
• Segments are short ~2kb
• Problem with repeated segments or genes
Re-sequencing using massive parallel
sequencer
$1000 genome project
Solexa
SOLiD
454
The value of sequenced
genome lies in the
annotation.
Gene discovery
Polymorphism
TSS
CpG region
ncRNA
TF binding sites
Annotation projects:
• HAVANA (Sanger Inst.)
• ENCODE
• CCDS
http://www.sanger.ac.uk/HGP/havana/
UCSC Genome Browser
What information do we want to extract?
Science, 9/2/2005
Total genetic difference (# of bases) is 4%
35 million single base substitutions plus 5
million insertions or deletions (indels)
The average protein differs by only two
amino acids, and 29% of proteins are
identical.
Genotype – Phenotype relationship!!!
Phenotype
• mRNA level
• Protein expression
• Protein structure
• Cell morphology
• Tissue morphology
• System physiological functions
• Behavior
•…
High-throughput techniques
High throughput protein crystalization
Massive parallel sequencing
Mass spectrometry
Microarray
High throughput cell imaging
High throughput in vivo screening
…
“A key element of the GTL program is an
integrated computing and technology
infrastructure, which is essential for timely and
affordable progress in research and in the
development of biotechnological solutions. In
fact, the new era of biology is as much about
computing as it is about biology. Because of
this synergism, GTL is a partnership between
our two offices within DOE’s Office of Science—
the Offices of Biological and Environmental
Research and Advanced Scientific Computing
Research.
Only with sophisticated computational power and
information management can we apply new
technologies and the wealth of emerging data to
a comprehensive analysis of the intricacies and
interactions that underlie biology. Genome
sequences furnish the blueprints,
technologies can produce the data, and
computing can relate enormous data sets to
models linking genome sequence to
biological processes and function.”
How to extract the information?
Computational tools
• Building the databases
• Perform analysis/extract features
• Data mining
• Classification/statistical learning
• Visualization/representation
Biological information!!!
What we are going to do:
• Search the databases
• Perform analysis
• Present output
Be a salient user!
What we are going to teach:
• Genomics
• Data sources
• Proteomics
(databases)
• Microarray analysis
• Available tools
• Other aspects
• Major issues in using the
• Ontology
• Imaging informatics
• System biology
• Machine/statistical learning
• Visualization
databases and tools
• Other resources
Review of Biology
Central dogma
Review of Biology
Operon
Review of Biology
mRNA, cDNA,
exon, intron
Review of Biology
Protein folding and structure
Databases
GenBank www.ncbi.nlm.nih.gov/GenBank/
EMBL www.ebi.ac.uk/embl/
DDBJ www.ddbj.nig.ac.jp
Synchronized daily.
Accession numbers are managed in a consistent way.
AceDB
DDJP DNA
JJPID
MIPS
PHRED
PIR
PROSITE
RDP
TIGR
UNIGENE
…
Resources
Local:
OSU library
Web:
PubMed
JSTOR (http://www.jstor.com)
http://www.expasy.org
http://www.genecards.org
http://www.pathguide.org/
Resources – What’s out there?
PubMed – Entrez
PubMed : http://www.pubmed.gov,
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi
PubMed training : http://www.nlm.nih.gov/bsd/disted/pubmed.html
Entrez : http://www.ncbi.nlm.nih.gov/Database/index.html
Entrez is the integrated, text-based search and retrieval system used
at NCBI for the major databases, including PubMed, Nucleotide and
Protein Sequences, Protein Structures, Complete Genomes,
Taxonomy, and others. Click on the graphic below for a more detailed
view of Entrez integration.
Entrez Databases
Nucleotide
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Gene
Genome
Sequence
mRNA
cDNA
SNP
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Name
Accession number
GI number
Version number
Alias
Accession number, GI number, Version
• accession number (GenBank) - The accession number is the unique identifier
assigned to the entire sequence record when the record is submitted to
GenBank. The GenBank accession number is a combination of letters and
numbers that are usually in the format of one letter followed by five digits (e.g.,
M12345) or two letters followed by six digits (e.g., AC123456).
•
The accession number for a particular record will not change even if the author submits a request to change
some of the information in the record. Take note that an accession number is a unique identifier for a
complete sequence record, while a Sequence Identifier, such as a Version, GI, or ProteinID, is an
identification number assigned just to the sequence data. The NCBI Entrez System is searchable by
accession number using the Accession [ACCN] search field.
• GI (GenBank) - A GI or "GenInfo Identifier" is a sequence identifier that can be
assigned to a nucleotide sequence or protein translation. Each GI is a numeric
value of one or more digits. The protein translation and the nucleotide sequence
contained in the same record will each be assigned different GI numbers.
•
Every time the sequence data for a particular record is changed, its version number increases and it
receives a new GI. However, while each new version number is based upon the previous version number, a
new GI for an altered sequence may be completely different from the previous GI. For example, in the
GenBank record M12345, the original GI might be 7654321, but after a change in the sequence is submitted,
the new GI for the changed sequence could be 10529376. Individuals can search for nucleotide sequences
and protein translations by GI using the UID search field in the NCBI sequence databases.
• GI number is NOT GeneID.
Example : E2F3
Example : E2F3
Data Format
FASTA (.fasta file)
>gi|33469954|ref|NM_000240.2| Homo sapiens monoamine oxidase A (MAOA), nuclear gene
encoding mitochondrial protein, mRNA
GGGCGCTCCCGGAGTATCAGCAAAAGGGTTCGCCCCGCCCACAGTGCCCGGCTCCCCCCGGGTATCAAAA
GAAGGATCGGCTCCGCCCCCGGGCTCCCCGGGGGAGTTGATAGAAGGGTCCTTCCCACCCTTTGCCGTCC
CCACTCCTGTGCCTACGACCCAGGAGCGTGTCAGCCAAAGCATGGAGAATCAAGAGAAGGCGAGTATCGC
GGGCCACATGTTCGACGTAGTCGTGATCGGAGGTGGCATTTCAGGACTATCTGCTGCCAAACTCTTGACT
GAATATGGCGTTAGTGTTTTGGTTTTAGAAGCTCGGGACAGGGTTGGAGGAAGAACATATACTATAAGGA
ATGAGCATGTTGATTACGTAGATGTTGGTGGAGCTTATGTGGGACCAACCCAAAACAGAATCTTACGCTT
GTCTAAGGAGCTGGGCATAGAGACTTACAAAGTGAATGTCAGTGAGCGTCTCGTTCAATATGTCAAGGGG
AAAACATATCCATTTCGGGGCGCCTTTCCACCAGTATGGAATCCCATTGCATATTTGGATTACAATAATC
TGTGGAGGACAATAGATAACATGGGGAAGGAGATTCCAACTGATGCACCCTGGGAGGCTCAACATGCTGA
CAAATGGGACAAAATGACCATGAAAGAGCTCATTGACAAAATCTGCTGGACAAAGACTGCTAGGCGGTTT
GCTTATCTTTTTGTGAATATCAATGTGACCTCTGAGCCTCACGAAGTGTCTGCCCTGTGGTTCTTGTGGT
ATGTGAAGCAGTGCGGGGGCACCACTCGGATATTCTCTGTCACCAATGGTGGCCAGGAACGGAAGTTTGT
AGGTGGATCTGGTCAAGTGAGCGAACGGATAATGGACCTCCTCGGAGACCAAGTGAAGCTGAACCATCCT
GTCACTCACGTTGACCAGTCAAGTGACAACATCATCATAGAGACGCTGAACCATGAACATTATGAGTGCA
AATACGTAATTAATGCGATCCCTCCGACCTTGACTGCCAAGATTCACTTCAGACCAGAGCTTCCAGCAGA
GAGAAACCAGTTAATTCAGCGGCTTCCAATGGGAGCTGTCATTAAGTGCATGATGTATTACAAGGAGGCC
TTCTGGAAGAAGAAGGATTACTGTGGCTGCATGATCATTGAAGATGAAGATGCTCCAATTTCAATAACCT
TGGATGACACCAAGCCAGATGGGTCACTGCCTGCCATCATGGGCTTCATTCTTGCCCGGAAAGCTGATCG
ACTTGCTAAGCTACATAAGGAAATAAGGAAGAAGAAAATCTGTGAGCTCTATGCCAAAGTGCTGGGATCC
CAAGAAGCTTTACATCCAGTGCATTATGAAGAGAAGAACTGGTGTGAGGAGCAGTACTCTGGGGGCTGCT
ACACGGCCTACTTCCCTCCTGGGATCATGACTCAATATGGAAGGGTGATTCGTCAACCCGTGGGCAGGAT
TTTCTTTGCGGGCACAGAGACTGCCACAAAGTGGAGCGGCTACATGGAAGGGGCAGTTGAGGCTGGAGAA
CGAGCAGCTAGGGAGGTCTTAAATGGTCTCGGGAAGGTGACCGAGAAAGATATCTGGGTACAAGAACCTG
…
>gi|4557735|ref|NP_000231.1| monoamine oxidase A [Homo sapiens]
MENQEKASIAGHMFDVVVIGGGISGLSAAKLLTEYGVSVLVLEARDRVGGRTYTIRNEHVDYVDVGGAYV
GPTQNRILRLSKELGIETYKVNVSERLVQYVKGKTYPFRGAFPPVWNPIAYLDYNNLWRTIDNMGKEIPT
DAPWEAQHADKWDKMTMKELIDKICWTKTARRFAYLFVNINVTSEPHEVSALWFLWYVKQCGGTTRIFSV
TNGGQERKFVGGSGQVSERIMDLLGDQVKLNHPVTHVDQSSDNIIIETLNHEHYECKYVINAIPPTLTAK
IHFRPELPAERNQLIQRLPMGAVIKCMMYYKEAFWKKKDYCGCMIIEDEDAPISITLDDTKPDGSLPAIM
GFILARKADRLAKLHKEIRKKKICELYAKVLGSQEALHPVHYEEKNWCEEQYSGGCYTAYFPPGIMTQYG
RVIRQPVGRIFFAGTETATKWSGYMEGAVEAGERAAREVLNGLGKVTEKDIWVQEPESKDVPAVEITHTF
WERNLPSVSGLLKIIGFSTSVTALGFVLYKYKLLPRS
Data Format
Other formats
NBRF/PIR (.pir file)
Begin with “>P1;” for protein sequence and “>N1;”
for nucleotide.
GDE (.gde file)
Similar to FASTA file, begin with “%” instead of “>”.
Protein Databases
UniProt is the universal protein database, a central repository of protein
data created by combining Swiss-Prot, TrEMBL and PIR. This makes it the
world's most comprehensive resource on protein information.
The Protein Information Resource (PIR), located at Georgetown
University Medical Center (GUMC), is an integrated public bioinformatics
resource to support genomic and proteomic research, and scientific
studies.
Swiss-Prot is a curated biological database of protein sequences from
different species created in 1986 by Amos Bairoch during his PhD and
developed by the Swiss Institute of Bioinformatics and the European
Bioinformatics Institute.
Pfam is a large collection of multiple sequence alignments and hidden
Markov models covering many common protein domains and families.
PDB
NCBI
http://proteome.nih.gov/links.html
Exercises
Question 1 - Database search
Find the following genes in GenBank. Write down their accession
numbers, GI number, chromosome numbers:
Rb1 (human), Rb1 (mouse), Rb1(rat), Rb1(bovine)
Find the protein sequences for the above. Present them in FASTA format.
Note: find the most close ones (e.g., if both Rb1 and Rb are present,
choose Rb1).
Question 2 – Gene information search
Find the function and alias for the following genes:
TCF3, Col4A1, MMP9 and WASP.
Reading – Entrez tutorial
http://www.ncbi.nlm.nih.gov/entrez/query/static/help/entrez_tutorial_BIB.pdf