BMI705 Lecture1

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Information about BMI705 Lecture1

Published on October 15, 2007

Author: Mertice


Slide1:  IBGP/BMI 730 Biomedical Informatics Director: Prof. Kun Huang Slide2:  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. Slide3:  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. Slide4:  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! Slide5:  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. Slide6:  Biology – Biomedical informatics – System biology Biomedical Informatics Slide7:  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 Prediction! Slide8:  What information do we want to extract? Slide9:  The Theme of Modern Biology Slide10:  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" Slide11:  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 Slide12:  Human Genome Project The Beginning (1988) Cold Spring Harbor Laboratory Long Island, New York Slide13:  June 26, 2000 at the Whitehouse Slide14:  Initial Analysis of the Human Genome Slide15:  What information do we want to extract? 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!!! Slide17:  Phenotype mRNA level Protein expression Protein structure Cell morphology Tissue morphology System physiological functions Behavior … Slide18:  High-throughput techniques High throughput protein crystalization Mass spectrometry Microarray High throughput cell imaging High throughput in vivo screening … Slide20:  “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.” Slide21:  How to extract the information? Computational tools Building the databases Perform analysis/extract features Data mining Classification/statistical learning Visualization/representation Biological information!!! Slide22:  What we are going to do: Search the databases Perform analysis Present output Be a salient user! Slide23:  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 Slide24:  Jump Start for Bioinformatics Biology PubMed GenBank Slide25:  Review of Biology Central dogma Slide26:  Review of Biology Operon Slide27:  Review of Biology mRNA, cDNA, exon, intron Slide28:  Review of Biology Codon, reading frames Sequence – open reading frame (ORF) – amino acids Six possible reading frames instead of three !!! (Why) In eukaryotes there is usually only one reading frame and is often the longest one. An ORF starts with an ATG(Met) in most species and ends with a stop codon (TAA, TAG, or TGA). 5'                                                   3'    atgcccaagctgaatagcgtagaggggttttcatcatttgaggacgatgtataa  1 atg ccc aag ctg aat agc gta gag ggg ttt tca tca ttt gag gac gat gta taa     M   P   K   L   N   S   V   E   G   F   S   S   F   E   D   D   V   *   2  tgc cca agc tga ata gcg tag agg ggt ttt cat cat ttg agg acg atg tat      C   P   S   *   I   A   *   R   G   F   H   H   L   R   T   M   Y   3   gcc caa gct gaa tag cgt aga ggg gtt ttc atc att tga gga cga tgt ata       A   Q   A   E   *   R   R   G   V   F   I   I   *   G   R   C   I  Slide29:  Review of Biology Protein folding and structure Slide30:  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 … Slide31:  Resources Local: OSU library Web: PubMed JSTOR ( Slide32:  Resources – What’s out there? Slide33:  PubMed – Entrez PubMed :, PubMed training : Entrez : 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. Slide34:  Entrez Databases Slide35:  Literatures Examples: E2F3 Retinoblastoma Constraints: automatics vs. manual Save: Tutorial at Slide36:  Literatures Slide37:  Literatures Slide38:  Literatures Examples: E2F3 Retinoblastoma Constraints: automatics vs. manual Slide39:  Literatures Slide40:  Nucleotide Gene Genome Sequence mRNA cDNA SNP Name Accession number GI number Version number Alias Slide41:  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. Slide42:  Example : E2F3 Slide43:  Example : E2F3 Slide44:  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 Slide45:  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 “>”. Slide46:  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(dog), 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: PTEN, Col4A1, MMP9 and WASP. Reading – Entrez tutorial

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