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Basics in bioinformatics Gábor Rákhely PhD. Institute of Biophysics BRC HAS Department of Biotecnology University of Szeged [email protected] (599)-726 This presentation can be found: http://biotech.szbk.u-szeged.hu/bioinf/bioinfo_itc.html Books are available in English BIOINFORMATICS INFORMATICS BIOINFORMATICS BIOLOGY “The >99% of the ever-lived scientists is contemporary It is true for data revolution in informatics INFORMATICS - experiments information production of new information - treatment, classification (grouping) and displaying of data - harmonizing of data Entering data, arrangement of data databanks Processing, displaying and evaluation of data newer information newer, other databanks Databanks: - fast exchange of data - interactive link between databanks and researchers automation, usage of special softwares and knowledge PREBIOINFORMATICS: RESOLVING THE INFORMATION CARRIER 1866 Mendel: crossing experiments with peas heredity in units 1869 Miescher: purification of salmon sperm DNA DNA as inheriting material 1903 WS Sutton the inheritable pattern is linked to the properties of chromosomes during proliferation cytochemsitry: the chromosome consist of DNA and protein 1925-1928 F. Griffith mouse infections with Streptococcus pneumoniae transforming principle 1944 Avery: the transforming compound is DNA PREBIOINFORMATICS: RESOLVING THE INFORMATION CARRIER 1952. Hershey és Chase From T2 phage DNA enters into the cells THE ROAD TO THE DOUBLE HELIX Chargaff E.: the ratio of the nucleotides is equal in humans and E. coli Biophysical data: e.g. water content of DNA Rosalind Franklin and Maurice Wilkins X-ray diffraction data Crick és Watson 1952-1953 The model of the double helices The double helical DNA The central dogma and the main areas of the bioinformatics in molecular biology Genomics Transcriptomics, transcriptome degradation proteomics, proteosome degradation biochemical activity metabolic pathways metabolomics Genomics Basically to determine the nucleotide sequence of a genome or extrachromosomal elements In silico prediction of functional regions, including coding, regulatory regions, splice sites e.t.c. The main three branches of the evolutionary tree (by Woese and colleagues) Genome sizes in nucleotide base pairs plasmids viruses bacteria fungi plants algae insects mollusks bony fish The size of the human genome is ~ 3 X 109 bp; almost all of its complexity is in single-copy DNA. amphibians reptiles birds The human genome is thought to contain ~30,000-40,000 genes. 104 105 106 107 mammals 108 109 1010 1011 COMPARISON OF THE CELL ORGANIZATION IN PROKARYOTES ANN EUKARYOTES STRUCTURE OF GENES IN EUKARYOTES exon intron exon Regulatory elements upstream Start of the biological information (coding region) downstream End of biological information (coding region) altenative splicing Genes within genes neurofibromatosis type I gene exons introns OGMP EVI2B EVI2A THE ORGANIZATION OF THE PROKARYOTE GENOME The model of the E. coli nucleoide THE ORGANIZATION OF THE GENES IN PROKARYOTES: polycistronic structure DNS MANIPULTION WITH COMPUTER DNA sequencing according to SANGER THE PRINCIPLE OF THE AUTOMATIC DNA SEQUENCENG GENOME SEQUENCING STRATEGIES Shot gun Primer walking ALTERNATIVE SHOT GUN STRATEGIES PRODUCTION OF BACTERIAL SHOT GUN LIBRARY Preparation of shotgun library E. coli chromosomal DNA transformation electroporation 2-3,5 kb fragments blunting the ends broken DNA fragments dephosphorylation Preparative gel electrophoresis SEQUENCE PROCESSING Sequence analysis checking, validation Phrap SeqMan/DNASTAR STADEN programme Removal of vectorial and other contaminating sequences Removal of the low quality sequences Vector_clipping Phrap Contig assembly from the overlapping fragments Phred Manual checking the sequences ARRANGMENT OF PRIMARY SEQUENCES INTO CONTIG an example S19T7 S12SK S19SK S148O20 S11T7 S148O22 S148O15 S148O7 S148O17 S17SK S148019 S148O13 S148O8 S17T7 S13SK S148O14 S148O18 S12T7 S13T7 S148O21 pcaB macA orf2 2000 pSC1/1 S148SK S148O11 S148O9 orf1 S148O12 S148O10 S11SK S148T7 SC110SK SC110T7 orf-3 pSC1/2 PSC148 6000 pSC1/3 (7405 bps) pcaG pcaH 4000 S14SK S18SK S16SK pSC1/8 pSC1/10 pSC1/4 pSC1/6 COSMID LIBRARY Partial digestion of genomic DNA with MboI (Sau3AI) (compatible end with BamHI end) Size fractionation for 30 – 45 kb fragmnets BamHI- XbaI digestion cos cos Ampr A tool for connecting non-overlapping contigs ori ligation 30 – 45 kb fragments cos cos in vitro packing with GigaPack l extrackt Selection for ampicillin rezisztent clones Cosmid library PRIMER WALKING In cosmid, BAC, YAC libraries TEMPLATE GENERATING SYSTEMS The location of the integration must be known High throughput automatic Southern hybiridization USEFUL TOOLS FOR ASSEMBLYING: MAPPING - genetic: positioning of genes and properties - physical: arrangment of sequences and genes - EST: expressed sequence tag - STS: sequence tagged site single 100-500 bp fragment ASSEMBLY OF THE CONTIGS: gap closure DIFFICULTIES IN THE ASSEMBLY: A. Abnormal genetic elements: formation of pseudogenes B. The coding region is sérült No regulatory region, driving elements of transcripion convencional pseudogene: loss of function mutation DIFFICULTIES IN THE ASSEMBLY Retroelements and retrotransposition DIFFICULTIES IN THE ASSEMBLY DNA transposons the retrotransposons are rather characteristic for Eukaryotes DIFFICULTIES IN THE ASSEMBLY REPETITIVE SEQUENCES IN THE GENOMES 1 chromosome microsatellites (short tandem repeat, STR) 13 bp repeat 150 bp long: interspersed repeats pl. CACACACACACA 2 chromosome tandem repeated DNA Long Interspersed Nuclear Elements: LINE On the average it occurs by 2 kb Minisatellites 25 bp repeat 20 kbp length Short Interspersed Nuclear Elements: SINE For genetic profile analysis “THE COMEDY OF ERRORS” A SEGMENT OF THE HUMAN GENOME IF EVERYTHING OK, WE HAVE SEQUENCES What does it contain, a gene or non-coding region? How do we know we can find anything, e.g. a gene? CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGGCTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGCGGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAAGA TCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGCTGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGGTTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTCCCTGG TCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTCCCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTTTATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTGATTCCTT CGGGATTTTTTGGGGTCCTGATTGGCTGGTTATTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATTGTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTGGCACGACGAG GGGTGCCATCGGTGCCGCGTCAAGCCAACGTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTTACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAGATGTTCGTGCTGC CGCAACGGCTGGACAAGACCATGTTCGCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTATTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCGACTTCCTCGGTCATGT CCGCGCTAGTGTTGATTCCGGTGGCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGCAGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCGATGTTGCTGGTGGTCTCCA TTCAGCTTCTGTGGAGGGGAATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTCAGAACGCTCAATCAATCAGAATGTAATCTTGACATAGAATACCGTTCCGATTTATTGCTTCG AGTGAAGCTGCCCGTCCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCCCTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACGGTCTTCTCCGAACGGCGTTTTTTGGC GGGAATGCTTCGTGTTGAAGTGGCCCTGGCGCGCGCGCAGGCGGCAGAGGGCCTTGTCAGTTCGGAATTGGCCGACGCGATCGAGGTTGTTGGTACTGCCGGGTTGGACCCCGAGGCGAT GGCGGCGACTACTCGCATGACAGGAGTGCCCGCAATATCGTTCGTCCGTGCGGTGCAATCGGCCCTGCCGCCCTCACTGGCGGGTGGATTTCATTTCGGCGCCACCAGTCAAGACATCGT GGATACGGCCCACGCGCTCCAGCTGGCCGAGGCACTCGATATTATAGAAGTCGATTTACACGCCACTGTCAGCGCAATGATGAATCTGGCCGCTGCTCACTGCAATACACCCTGTATCGG GCGCACGGCCTTGCAGCACGCAGCGCCAGTTACGTTCGGCTACAAGGCGTCCGGCTGGTGCGTTGCCCTGGCGGAGCATCTGGTGCAGCTTCCCGCGCTGCGAAAGCGGGTTCTGGTGGC GTCGCTAGGGGGGCCGGTTGGTACCCTTGCCGCGATGGAGGAGCGGGCCGACGCTGTACTGGAGGGTTTCGCTGCGGACCTGGGGTTGGCCATTCCCGCCCTGGCCTGGCACACGCAGCG GGCCCGGATCGTCGAGGTGGCCAGTTGGCTGGCCATATTGCTGGGAATTCTGGCAAAAATGGCCACCGATGTCGTTCACTTGTCCTCCACGGAAGTGCGCGAGCTTTCCGAACCTGTAGC GCCGGGCAGGGGGGGCTCCTCGGCGATGCCTCACAAGCGGAACCCGATTTCCTCGATTACCATCCTGTCCCAGCATGCTGCGGCAGGGGCCCAGCTCTCCATTCTCGTGAACGGCATGGC CAGTCTGCACGAACGTCCGGTGGGGGCGTGGCATTCGGAATGGTTGGCTCTGCCGACGCTGTTCGGCCTTGCCGGCGGTGCCGTGCGCGAGGGCAGGTTTCTGGCCGAGGGGCTGCTGGT CGATGCCGACCAGATGGGTCGCAATCTACAATTGACCAATGGCCTGATTTTCAGCGACGCGGTAGCCGGCCAGTTGGCAAAGCACTTGGGTCGGGCCGAGGCTTATGCCGCTGTCGAGGA TGCCGCCGCCGAGGTGTTGCGTTCAGGCGGCAGCTTTCAGGGTCAGCTGAACCAGCGCCTGCCCGATCACCGCGACGCTATCGCTATTGCTTTTGATACGACGCCGGCGATCCAGGCCGG GGCCGCCCGCTGCCGTAGTGCGCTGGATCATGTGGCTCGTATTCTTGGACCCGCCTCTACCATCGGATTTCAAGGAGGCTAATGACGTGACGACACTGTTTGAGGCGACGACCATCCCGA TTTGCGAGGGCCCGCGCGACCAGACCGCCGAGATCCTTTTCGAGATGCCGCCGGGTGCGTGGGATACCCATTTTCATGTTTTTGGCCCAGTTTCATCGTTTCCATACGCAGAACACAGGC TCTATTCCCCACCGGAGTCGCCACTTGAGGATTATCTGGTGTTGATGGAGGCTTTGGGGATCGAGCGCGGCGTTTGTGTCCATCCGAATGTTCATGGTGCCGACAATTCGGTGACGCTCG ACGCAGTTGCGCGGTCCGATGGTCGTCTGCTGGCGGTGATCAAGCCACATCACGAGATGACTTTTGTTCAGCTGCGGGACATGAAGGCGCAGGGGGTCTGCGGGGTACGTTTTGCCTTCA ATCCGCAGCATGGCTCGGGCGAGTTGGATACTCGTTTGTTCGAGCGTATGTTGGACTGGTGCCGCGACCTAGGCTGGTGCGTAAAATTGCATTTCGCGCCCGCTGCGCTGGACGGTCTGG CTGAACGTTTGGCGCGCGTCGATATTCCGATCATCATCGATCATTTCGGGCGGGTGGACACCGCGCAAGGTGTGGATCAGCCGCACTTCCTGCGTTTGCTCGATCTGGCCAAACTGGACC Comparison to known sequences The sequence obtained can be compared to known sequences in the databanks Question: what is similar? What to compare DNA or protein? SIMILARITY CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGG CTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGC GGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAA GATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGC TGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGG TTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTC CCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTC CCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTT TATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTG ATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTA TTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATT GTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTG GCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAAC GTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTT ACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAG ATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTC GCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTA TTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCG ACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTG GCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGC AGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCG ATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGA ATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTC AGAACGCTCAATCAATCAGAATGTAATCTTGACATAGA ATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGT CCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCC CTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACG GTCTTCTCCGAACGGCGTTTTTTGGC CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGG CTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGC GGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAA GATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGC TGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGG TTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTC CCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTC CCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTT TATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTG ATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTA TTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATT GTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTG GCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAAC GTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTT ACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAG ATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTC GCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTA TTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCG ACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTG GCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGC AGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCG ATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGA ATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTC AGAACGCTCAATCAATCAGAATGTAATCTTGACATAGA ATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGT CCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCC CTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACG GTCTTCTCCGAACGGCGTTTTTTGGC the two sequences are (and look) the same SIMILARITY As now – but almost the same, but they seem to be dissimilar CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGG CTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGC GGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAA GATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGC TGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGG TTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTC CCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTC CCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTT TATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTG ATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTA TTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATT GTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTG GCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAAC GTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTT ACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAG ATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTC GCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTA TTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCG ACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTG GCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGC AGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCG ATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGA ATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTC AGAACGCTCAATCAATCAGAATGTAATCTTGACATAGA ATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGT CCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCC CTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACG GTCTTCTCCGAACGGCGTTTTTTGGC GLOBAL, LOCAL AAACTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGC GGGCTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGG CCGCGGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGC AAAAGATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGG TTGCTGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGG GCGGTTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGA TGTCCCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCT GCTCCCGATCTATGTCGTTTCTGATGCATTCGGCGTCTG GCTTTATCGGCACCGGTATTCTGCCTCCAATCTGCGCATC CTGATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGG TTATTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTC ATTGTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTG CTGGCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCC AACGTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGC TTTACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTC CAGATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATG TTCGCGGGCACATCAACGCTTACCTTTGCTGCCATAAAC CTATTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTT TCGACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCG GTGGCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACG CGCAGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAG GCGATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGG GGAATGTCGGATATCCTGAACTAGCTGGAGATCGCAAT GTCAGAACGCTCAATCAATCAGAATGTAATCTTGACAT AGAATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCC CGTCCGCTGAGATGTCATGACATTTTCCCCGCTTGATTC CGCCCTGCTTGGACCGTTGTTCGCGACCGATGAAATGCG CACGGTCTTCTCCGAACGGCGTTTTTTGGC BLAST, FASTA Problems with DNA comparison Codon usage preference: various codons may code for the same amino acid, the DNA sequences are different, the protein sequences are the same … AND DOES IT CODE FOR ANY PROTEIN? Open reading frames: Usually they start with ATG, but in softwares it’s option Length: default 100 aminoacid, but option The result is hypothetical, it should be checked compared to the existing data Putative protein list Finding orfs Finding orfs … AND DOES IT CODE FOR ANY PROTEIN? Open reading frames: Usually they start with ATG, but in softwares it’s option Length: default 100 aminoacid, but option The result is hypothetical, it should be checked compared to the existing data Putative protein list similarity BLASTP Generation of information from information Problems: frameshift mutation, the global failure of global similarity Where does it start? What is start? FRAME SHIFT MUTATION – A SOLUTION FOR IT Translation in each open reading frame Stop codons are not taken into account, just as missing aa It compares everything to everything at the protein level example BLASTX Six frame translation FRAMESHIFT WHERE DOES IT START FROM? Who knows? 2290 2300 2310 2320 2330 2340 GCCGCCCGCTGCCGTAGTGCGCTGGATCATGTGGCTCGTATTCTTGGACCCGCCTCTACC A A R C R S A L D H V A R I L G P A S T M W L V F L D P P L P 2350 2360 2370 2380 2390 2400 ATCGGATTTCAAGGAGGCTAATGACGTGACGACACTGTTTGAGGCGACGACCATCCCGAT I G F Q G G * S D F K E A N D V T T L F E A T T I P I - Identification of other elements - Genomic context - Experimental control GENOMIC CONTEXT OH NH3+ gén hossz (aa) funkcó homológia (%) orf1 259 hypothetical conserved membrane protein, permease? 45 pcaB ~ 450 3-carboxy-cis-cis muconate cycloizomerase Putative hydrolase 40-45 orf2 359 maleil acetate redukase 45-55 OH SO34-szulfocatechol P340 II dioxygenase COOCOO 40 319 macA O 2 SO3Sulfanilic acid + SO3sulfomuconate sulfomuconate cycloisomerase orf3 395 pcaH 245 pcaG 195 istB 19 putative oxidase, dehydrogenase NAD binding domain protocatechol-3,4 dioxygenase beta subunit protocatechol-3,4 dioxygenase alpha subunit 80, 67, < 60 IS21 transposase, C-terminal 100 40-45 COO - O O SO 3 sulfolaktone sulfolaktone hydrolase 64, 61, HSO33 COO COO O maleilacetate maleilacetate redukase pSC1/48 (7404bp) orf1 pcaB orf2 In a genomic locus the neighboring genes may be functionally/metabolically linked macA TCA cycle orf3 pcaH pcaG istB Identification with MS CODON USAGE The codon usage is characteristic for the organism, species Codon usage tables, databanks APPLICATION OF CODON USAGE FOR IDENTIFICATION OF CODING REGIONS ESTABLISHMENT OF THE FUNCTION OF THE PREDICTED GENE PRODUCT– BY ANALOGY Comparison to the known sequences available in the databanks Similarity search can be made at the DNA or protein level What is a database ? • A collection of... – structured – searchable (index) – updated periodically (release) – cross-referenced (hyperlinks) db • Associated tools (software) access, update, insertion, deletion…. -> table of contents -> new edition -> links with other Types of Databases Primary Databases Original submissions by experimentalists Content controlled by the submitter Examples: GenBank, SNP, GEO Derivative Databases Built from primary data Content controlled by third party (NCBI) Examples: Refseq, TPA, RefSNP, UniGene, NCBI Protein, Structure, Conserved Domain Sequence Databases Main nucleic acid sequence databases • EMBL • GenBank • DDBJ Main protein sequence databases • Swiss Prot • also TREMBL, GenPept Often integrated with other databases International Sequence Database Collaboration Entrez NIH NCBI GenBank •Submissions •Updates •Submissions •Updates EMBL CIB NIG DDBJ •Submissions •Updates getentry EBI SRS EMBL Integrating Sequence and Bibliographic Databases Entrez • Links nucleic acid sequences, protein sequences and MEDLINE • Powerful and easy to use • US-based: can be slow from Africa SRS • Universal system for searching sequence and other databases • Available worldwide The (ever expanding) Entrez System OMIM PubMed PubMed Central 3D Domains Journals Structure Books CDD/CDART Entrez Protein Taxonomy Genome GEO/GDS UniSTS UniGene Nucleotide SNP PopSet The Entrez System GenBank: NCBI’s Primary Sequence Database Release 139 December 2003 30,968,418 36,553,368,485 >140,000 Records Nucleotides Species 138 Gigabytes 570 files • full release every two months • incremental and cumulative updates daily • available only through internet ftp://ftp.ncbi.nih.gov/genbank/ The Growth of GenBank 35 40 Sequence records Total base pairs 35 25 Release 139: 31.0 million records 36.6 billion nucleotides 30 25 20 Average doubling time ≈ 12 months 20 15 15 10 10 5 0 '82 '84 '85 '86 '87 '88 '90 '91 '92 '93 '95 '96 '97 '98 '00 '01 '02 '03 5 0 Total Base Pairs (billions) Sequence Records (millions) 30 European Bioinformatics Institute (EBI) SEARCHING IN THE DATABANKS: SIMILARITY - ALIGNMENT Comparison of primary DNA or protein sequences to other primary or secondary sequences Expecting that the function of the similar sequence is known from experiments !!! Thinking by analogy Assuming that if the sequence is similar, the function is also similar question: what is responsible for the function? the whole protein or its part How many function (activity) does a protein have? globality - locality Alignment - background “For many protein sequences, evolutionary history can be traced back 1-2 billion years” -William Pearson When we align sequences, we assume that they share a common ancestor They are then homologous Protein fold is much more conserved than protein sequence DNA sequences tend to be less informative than protein sequences Aligning Sequences…. • There are lots of possible alignments. • Two sequences can always be aligned. • Sequence alignments have to be scored. • Often there is more than one solution with the same score. Alignment methods Rigorous algorithms Needleman-Wunsch Smith-Waterman Heuristic algorithms BLAST FASTA Pairwise comparison Local alignment Identify the most similar region shared between two sequences Smith-Waterman Global alignment Align over the length of both sequences Needleman-Wunsch Global – local alignment TEGNAP VELED VOLTAM TEGNAP VELED MAGOLTAM VELE DALOLTAM :::::::::::: : ::::: TEGNAP VELED----------V-------OLTAM Global TEGNAP VELED MAGOLTAM VELE DALOLTAM :::::::::::: .::::: TEGNAP-VELED---VOLTAM-------------- TEGNAP VELED MAGOLTAM VELE DALOLTAM :::::::::::: .::::: TEGNAP VELED ----------------VOLTAM TEGNAP VELED MAGOLTAM VELE DALOLTAM :::::: :::: : .::::: TEGNAP----------------VELE-D-VOLTAM Local TEGNAP VELED MAGOLTAM :::::::::::: .::::: TEGNAP VELED---VOLTAM TEGNAP VELED :::::: ::::: TEGNAP VELED VELE DALOLTAM :::: : .::::: VELE-D-VOLTAM Parameters of Sequence Alignment Scoring Systems: • Each symbol pairing is assigned a numerical value, based on a symbol comparison table. Gap Penalties: • Opening: • Extension: The cost to introduce a gap The cost to elongate a gap DNA Scoring Systems actaccagttcatttgatacttctcaaa Sequence 1 taccattaccgtgttaactgaaaggacttaaagact Sequence 2 Negative scoring values to penalize mismatches: A T C G A 5 -4 -4 -4 T -4 5 -4 -4 C -4 -4 G -4 -4 -4 5 -4 5 Matches: 5 Mismatches: 19 Score: 5 x 5 + 19 x (-4) = - 51 Dotplots A A CCTCCTTTGT CCTCCTTTGG CCTCCCTTAG Pro Leu 5 -4 C -4 -4 -4 Leu 5 5 5 5 5 -4 5 5 -4 5 C 5 -4 -4 G –4 -4 CCTCCTTTGT Pro G 5 -4 -4 -4 T -4 Point = 50 5 5 5 5 5 5 5 5 5 5 T Point = 32 5 Protein Scoring Systems • Amino acids have different biochemical and physical properties that influence their relative replaceability in evolution. tiny aliphatic P C S+S I V A L hydrophobic M Y F small G G CSH T S D K W H N E R Q aromatic positive polar charged Protein Scoring Systems • Amino acids have different biochemical and physical properties that influence their relative replaceability in evolution. • Scoring matrices reflect • probabilities of mutual substitutions • the probability of occurrence of each amino acid. • Widely used scoring matrices: • PAM • BLOSUM Blosum62 scoring matrix A R N D C Q E G H I L K M F P S T W Y V 4 -1 5 -2 0 6 -2 -2 1 6 0 -3 -3 -3 9 -1 1 0 0 -3 5 -1 0 0 2 -4 2 5 0 -2 0 -1 -3 -2 -2 6 -2 0 1 -1 -3 0 0 -2 8 -1 -3 -3 -3 -1 -3 -3 -4 -3 4 -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -1 2 0 -1 -1 1 1 -2 -1 -3 -2 5 -1 -2 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 A R N D C Q E G H I L K M F P S T W Y V Basic principles of dynamic programming - Creation of an alignment path matrix - Stepwise calculation of score values - Backtracking (evaluation of the optimal path) FastA programs: FastA TFastA searches for similarity between a query sequence and any group of sequences (DNA and Protein). compares a peptide sequence against a set of nucleotid sequences. FastX compares a nucleotide sequence against a protein database taking frameshifts into account. TFastX compares a peptide sequence against a nucleotide sequence database taking frameshifts into account. BLAST programs Program Input Database 1 blastn DNA DNA 1 blastp protein protein 6 blastx DNA protein 6 tblastn protein DNA 36 tblastx DNA DNA What program to use for searching? 1) BLAST is fastest and easily accessed on the Web limited sets of databases nice translation tools (BLASTX, TBLASTN) 2) FASTA works best in GCG integrated with GCG precise choice of databases more sensitive for DNA-DNA comparisons FASTX and TFASTX can find similarities in sequences with frameshifts 3) Smith-Waterman is slower, but more sensitive known as a “rigorous” or “exhaustive” search SSEARCH in GCG and standalone FASTA