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IBGP 705 Biomedical Informatics Director: Prof. Kun Huang.

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Presentation on theme: "IBGP 705 Biomedical Informatics Director: Prof. Kun Huang."— Presentation transcript:

1 IBGP 705 Biomedical Informatics Director: Prof. Kun Huang

2 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. SourceSource: Merriam-Webster's Medical Dictionary, © 2002 Merriam-Webster, Inc.

3 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.

4 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!

5 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.

6 Biology – Biomedical informatics – System biology Biomedical Informatics

7 Biology Domain knowledge Hypothesis testing Experimental work Genetic manipulation Quantitative measurement Validation System Sciences Theory Analysis Modeling Synthesis/prediction Simulation Hypothesis generation Informatics Data management Database Computational infrastructure Modeling tools High performance computing Visualization System Biology Understanding! Prediction!

8 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"

9 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

10 Human Genome Project The Beginning (1988) Cold Spring Harbor Laboratory Long Island, New York

11 June 26, 2000 at the Whitehouse

12 Initial Analysis of the Human Genome


14 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

15 Shotgun Sequencing Segments are short ~2kb Problem with repeated segments or genes Concepts in Biochemistry, 2 nd Ed., R. Boyer

16 $1000 genome project Solexa SOLiD 454 Re-sequencing using massive parallel sequencer

17 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

18 UCSC Genome Browser



21 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!!!

22 Phenotype mRNA level Protein expression Protein structure Cell morphology Tissue morphology System physiological functions Behavior …

23 High-throughput techniques High throughput protein crystalization Massive parallel sequencing Mass spectrometry Microarray High throughput cell imaging High throughput in vivo screening …



26 “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.”

27 How to extract the information? Computational tools Building the databases Perform analysis/extract features Data mining Classification/statistical learning Visualization/representation Biological information!!!

28 What we are going to do: Search the databases Perform analysis Present output Be a salient user!

29 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

30 Review of Biology Central dogma

31 Review of Biology Operon

32 Review of Biology mRNA, cDNA, exon, intron

33 Review of Biology Protein folding and structure

34 Databases GenBank EMBL DDBJ Synchronized daily. Accession numbers are managed in a consistent way. AceDB DDJP DNA JJPID MIPS PHRED PIR PROSITE RDP TIGR UNIGENE …

35 Resources Local: OSU library Web: PubMed JSTOR (

36 Resources – What’s out there?

37 PubMed – Entrez PubMed :, PubMed training : Entrez : EntrezEntrez 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.

38 Entrez Databases

39 Nucleotide Gene Genome Sequence mRNA cDNA SNP Name Accession number GI number Version number Alias

40 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.

41 Example : E2F3



44 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 “>”.

45 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.proteinSwiss-ProtTrEMBLPIR 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.Georgetown Universitygenomicproteomic 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.biological databaseproteinSwiss Institute of BioinformaticsEuropean Bioinformatics Institute Pfam is a large collection of multiple sequence alignments and hidden Markov models covering many common protein domains and families. PDB NCBI

46 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

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