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

• 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

GenotypePhenotypeGenotype-Phenotype relationshipSNPs PathwaysDrug 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

BiologyDomain knowledge

• Hypothesis testingExperimental work

• Genetic manipulation• Quantitative measurement• Validation

System SciencesTheoryAnalysisModeling

• Synthesis/prediction• Simulation• Hypothesis generation

InformaticsData management

• DatabaseComputational infrastructure

• Modeling tools• High performance computing

Visualization

System Biology

Understanding! Prediction!

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 ProjectThe Beginning (1988)

Cold Spring Harbor LaboratoryLong 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

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

Genome Mapping

Shotgun Sequencing

• Segments are short ~2kb• Problem with repeated segments or genes

Concepts in Biochemistry, 2nd Ed., R. Boyer

$1000 genome project

SolexaSOLiD454

Re-sequencing using massive parallel sequencer

http://www.sanger.ac.uk/HGP/havana/

The value of sequenced genome lies in the annotation.

Gene discoveryPolymorphismTSSCpG regionncRNATF binding sites

Annotation projects:• HAVANA (Sanger Inst.)• ENCODE • CCDS

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 techniquesHigh 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

• Proteomics

• Microarray analysis

• Other aspects

• Ontology

• Imaging informatics

• System biology

• Machine/statistical learning

• Visualization

• Data sources (databases)

• Available tools

• Major issues in using the

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.jpSynchronized daily.Accession numbers are managed in a consistent way.

AceDBDDJP DNAJJPIDMIPSPHREDPIRPROSITERDPTIGRUNIGENE…

Resources

Local: OSU library

Web: PubMedJSTOR (http://www.jstor.com)http://www.expasy.orghttp://www.genecards.orghttp://www.pathguide.org/

Resources – What’s out there?

PubMed – Entrez

PubMed : http://www.pubmed.gov, http://www.ncbi.nlm.nih.gov/entrez/query.fcgiPubMed training : http://www.nlm.nih.gov/bsd/disted/pubmed.htmlEntrez : 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• Gene• Genome• Sequence• mRNA• cDNA• SNP

• 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 FormatFASTA (.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. PDBNCBIhttp://proteome.nih.gov/links.html

ExercisesQuestion 1 - Database search

Find the following genes in GenBank. Write down their accessionnumbers, 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 searchFind the function and alias for the following genes:

TCF3, Col4A1, MMP9 and WASP.

Reading – Entrez tutorialhttp://www.ncbi.nlm.nih.gov/entrez/query/static/help/entrez_tutorial_BIB.pdf