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Basics in bioinformatics

Basics in bioinformatics. G ábor Rákhely PhD. Institute of Biophysics BRC HAS Department of Biotecnology University of Szeged rakhely @brc.hu (599)-726. This presentation can be found : http://biotech.szbk.u-szeged.hu/bioinf/bioinfo_itc.html. B ooks are available in English.

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Basics in bioinformatics

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  1. Basics in bioinformatics Gábor Rákhely PhD. Institute of Biophysics BRC HAS Department of Biotecnology University of Szeged rakhely@brc.hu (599)-726

  2. This presentation can be found: http://biotech.szbk.u-szeged.hu/bioinf/bioinfo_itc.html Books are available in English

  3. BIOINFORMATICS INFORMATICS BIOINFORMATICS BIOLOGY “The >99% of the ever-lived scientists is contemporary It is true for data  revolution in informatics

  4. 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 - interactivelink between databanksand researchers automation, usage of special softwares and knowledge

  5. PREBIOINFORMATICS: RESOLVING THE INFORMATIONCARRIER 1866 Mendel: crossing experiments with peas  heredity in units 1869 Miescher: purification of salmon sperm DNA DNAas 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 1925-1928F. Griffith mouse infections with Streptococcus pneumoniae  transforming principle 1944Avery: the transforming compound is DNA

  6. PREBIOINFORMATICS: RESOLVING THE INFORMATIONCARRIER 1952. Hershey és Chase From T2 phage DNA enters into the cells THE ROAD TO THE DOUBLE HELIX Chargaff E.: the ratio of the nucleotidesis 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

  7. The double helical DNA

  8. degradation degradation The central dogma and the main areas of the bioinformaticsin molecular biology Genomics Transcriptomics, transcriptome proteomics, proteosome biochemical activity metabolicpathways metabolomics

  9. 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.

  10. The mainthree branches of the evolutionary tree (by Woese and colleagues)

  11. 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. The human genome is thought to contain ~30,000-40,000 genes. amphibians reptiles birds mammals 104 105 106 107 108 109 1010 1011

  12. COMPARISON OF THE CELL ORGANIZATION IN PROKARYOTESANN EUKARYOTES

  13. neurofibromatosis type I gene exons introns OGMP EVI2B EVI2A STRUCTURE OF GENESIN EUKARYOTES exon intron exon Regulatory elements upstream downstream End of biological information (coding region) Start of the biological information (coding region) altenative splicing Genes within genes

  14. THE ORGANIZATION OF THE PROKARYOTE GENOME The model of theE. coli nucleoide

  15. THE ORGANIZATION OF THE GENES IN PROKARYOTES: polycistronic structure

  16. DNS MANIPULTION WITH COMPUTER

  17. DNA sequencing according to SANGER

  18. THE PRINCIPLE OF THE AUTOMATIC DNA SEQUENCENG

  19. GENOME SEQUENCING STRATEGIES Shot gun Primer walking

  20. ALTERNATIVE SHOT GUN STRATEGIES

  21. PRODUCTION OF BACTERIAL SHOT GUN LIBRARY

  22. Preparation of shotgun library E. coli transformation electroporation chromosomal DNA 2-3,5 kb fragments blunting the ends dephosphorylation broken DNA fragments Preparative gel electrophoresis

  23. SEQUENCE PROCESSING Sequence analysis checking, validation Phrap SeqMan/DNASTAR STADEN programme Removal of vectorial and othercontaminating sequences Vector_clipping Removal of the low qualitysequences Phrap Contig assembly from the overlapping fragments Phred

  24. Manual checking the sequences

  25. ARRANGMENT OF PRIMARY SEQUENCES INTO CONTIG an example S19T7 S12SK S19SK S148O22 S148O20 SC110SK S11T7 S148O15 SC110T7 S148O7 S148O17 S17SK S148019 S148O13 S148O12 S148O8 S148O10 S13SK S148SK S17T7 S11SK S148O11 S148O9 S148O18 S12T7 S14SK S148T7 S18SK S148O14 S13T7 S16SK S148O21 macA orf-3 pcaG orf2 pcaB orf1 pcaH 2000 4000 6000 pSC1/2 pSC1/3 pSC1/8 pSC1/1 pSC1/10 pSC1/4 PSC148 (7405 bps) pSC1/6

  26. Ampr ori COSMIDLIBRARY  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 A tool for connectingnon-overlapping contigs ligation 30 – 45 kb fragments cos cos in vitro packing with GigaPack l extrackt Selection for ampicillin rezisztent clones Cosmid library

  27. PRIMER WALKING In cosmid, BAC, YAC libraries TEMPLATE GENERATING SYSTEMS The location of the integrationmust be known High throughput automatic Southern hybiridization

  28. 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

  29. ASSEMBLY OF THE CONTIGS: gap closure

  30. DIFFICULTIES IN THE ASSEMBLY: Abnormal genetic elements:formation of pseudogenes B. A. The coding region is sérült No regulatory region, driving elements of transcripion convencional pseudogene: loss of function mutation

  31. DIFFICULTIES IN THE ASSEMBLYRetroelementsand retrotransposition

  32. DIFFICULTIES IN THE ASSEMBLY DNA transposons the retrotransposons arerather characteristic for Eukaryotes

  33. interspersed repeats 2 chromosome tandem repeated DNA 1 chromosome DIFFICULTIES IN THE ASSEMBLY REPETITIVE SEQUENCES IN THE GENOMES • microsatellites(short tandem repeat, STR) •  13 bp repeat  150 bp long: • pl. CACACACACACA • On the average it occurs by 2 kb • Minisatellites • 25 bp repeat  20 kbp length For genetic profile analysis Long Interspersed Nuclear Elements: LINE Short Interspersed Nuclear Elements: SINE

  34. “THE COMEDY OF ERRORS”

  35. A SEGMENT OF THE HUMAN GENOME

  36. 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 ATGTCTGGATCAAGCTTACGGGGGCAGATCGTATTAGCGGTTCCGGCGCGCCATATGACGATGTCGTGCCCTTCGCGCACGCTTTGGCAGATGTGGCGCCCGACCGCCTCCTCTGGGGTT CGGATTGGCCGCATTCAGGCTATTTCGATCCGAAGCACATACCCAATGACGGCGACTTGTTGAACCTTTTGGCGCGTTTTGCCCCCGATGCTGAACTGCGTCGTAAGATCCTTGTGGACA ACCCGCAGCGCCTGTTCGGGGCTGCTTGAGGAGCCGAGCCGATGCAACCTTTCGTCTACGAAACAGCCCCAGCGCGCGTCGTTTTCGGGCGCGGCACTTCGCAGAATCTGCGGCGGGAAC TTGAGGCCCTGAATTTTGGCAGGGCGCTGGTTCTTTCCACGCCCGACCAAAAAGAACAATCGCTGCGAATTGCCCAGGGCCTGGGTTCTCAGCTGGCGGGGTCGTTCCACGCCGCTGCCA TGCATACGCCTGTCGAGGTCACCTTGCAGGCGCTTGAGGTGCTGAAGGATGTGCAGGCCGATTGCATCGTGGCGATTGGCGGCGGCTCAACCATTGGGTTGGGCAAGGCACTGGCCCTGC GCACCGATCTGCCGCAGATCGTCGTCCCGACGACTTATGCCGGCTCGGAAATGACGCCGATCCTGGGAGAGACGGAAAACGGGCTGAAGACCACACAGCGTAATCCCAAAGTGCAGCCGA GGGTGGTTCTCTACGATGTGGACCTGACTGTGACGCTTCCGGTGCAGGCCTCGGTTACATCAGGCATGAATGCGATCGCCCATGCGGCCGAGGCATTATATGCGCGGGACGGCAATCCGG TGATCTCGCTGATGGCCGAAGAGGCGATCCGCGCGCTGGCCCATGCCCTGCCGCGTATCGTTGCCACTCCCGACGATATCGAAGCGCGCAGCGATGCCCTCTATGGCGCGTGGCTGTGCG GAACGTGCCTGGGTTCGGCCGGAATGGCGTTGCACCATAAGCTCTGCCACACCCTCGGCGGAAGTTTCGATTTGCCACATGCCCCGACCCACACGGTCATCCTCCCCTATGCGCTCGCCT ATAATAGTGATGCGGCCAGGCCCGCAATGGCAGCCATCGCGCGCGCGCTGGGCATGGCGGATGCAGCGATGGGCATGAGAGCGTTGTCCATGCGGTTGGGCGCCCCGACATCGCTGCGTG AGTTGGGCATGGCAGAAGCCGATCTTGACCGCGCCGCCGACCTGGCCACGCAAAATGCCTATTGGAACCCGCGACCCATCGAGCATGGGCCGATTCGTAACCTTCTGGGACGGGCCTGGG CTGGAACTCCGGTCTGAAGGACCTAGAGGACAGTCAATTCATTGATCTGAAGTCACCAACGAGGAGATATGGGATGAACGAGAACATTGCGATCCGCAAATTGGGCCGCCGACTCCGATT GGGCATTGCCGGTGGCGCGGGTCATTCGCTGATTGGTCCGGTTCACCGGGAGGCGGCTCGGCTTGACGATTTGTTCTCTCTCGATGCTGCGGTGCTGTCCAGTAACGCGGAACGCGGGGA TGCTGAGGCCGCGGCTCTCGGAATTCCGCGCTCCTATTCGTCCACCGCCGAGATGTTCGCAATGGAGAAGGCTAGGCCCGACGGTATTGAGGCCGTTGCCATAGCCACGCCGAATGACAG CCATTACCGGATTCTGTGCGAGGCGCTGGACGCCGGGTTGCATGTAATCTGCGACAAGCCTTTAACCTCCACGAAGGCCGAGGCCGACGACGTGCTGGTGCGGGCGAAGGCCGCGGGCAA GGTTGTGGTCCTGACCCACAATTATTCTGGCTACGCCATGGTACGCCAAGCCCGCGCCATGGTCGCCGCCGGTGAACTTGGGAAAATCCACCAGATTCACGGGGTCTACGCTCTGGGCCA GATGGGCCGTTTGTTCGAGGCCGACGAAGGGGGCGTGCCTCCGGGGATGCGTTGGCGGATTGATCCTGCGCGCGGTGGCGACAGTCACGCCCTGGTGGATATCGGCACCCATGTGCACCA TCTGGCTACCTTCATCACGCAGTTACAGGTCGTTGAGGTAATGGCCGATCTTGGGCCGGCGGTTCAAGGCCGCGCGGCCCATGACAGTGCCAACGTCATGTTCCGTATGGAAAACGGAGC TTTCGGATCGTTCTGGGCCACCAAGGCGGCATCGGGGGCCAGCAAGCTGGCGATCGAAGTCTACGGTGACAAGGGCGGCGTCCTGTGGGAGCAGGCCGACGCCAATAACTTGCTACATAT GCGGCAGGGCCAACCCCCAGCCCTGATTGGTCGACAAGTTGCCGGGCTGCATCCTGCGGCAATCCGCGCGATGCGGGGGCCGGGTTATCATTTCGTGGAAGGCTATCGCGAGGCCTTTGC GAATATGTACGTGGATTTCGCCGAACAGATCTTGGCCATGATGGGCAAGGGGGCCGCAGATCACCTGGCATTGGAAGCGCCGTCGGTCGTGGACGGCCTGCGCTCCATGGCGTTCATCGA AGCCTGTGTGGCGTCGTCGCAGGACCGCCAATGGCGGCAGGTGGAGCAAGTCAGTTGATCTCTCAGCGGCTTCGGCATTTTTCCCGGGCTGGCGGCTCCCCGCAGCTCCCTCCGGTGGAA AGAACGGGTAATCAAAATAATATTCTGATTTTAAAGGATGTTCCAGACAGCTGATTATTCCTGAAATTTAGGGCTCTTTCGGCTGTAGCAATTGACTAAAAGCCGAATTTAAGGGTAATTAAACAAACGCTGTTCGTATTATTTAAACAGGTGAGTGATGGCGATATTCCTGGAAGGCTGGCCGATGGTTTCATCTGAATACCCGGCCAGAAGCGTTGAGGCGCACCCGGCCTATCTGAC GCCAGACTATGTTTTCACGCGAAAGCGTGCGCCGACTCGACCGCTGCGGTTAATTCCTCAGTCTGCGACGGAGCTGTATGGCCCGGTTTATGGACAAGAGAGCGTCCGTCCGGGGGATAA CGACCTGACCCGTCAGCACGAAGCTGAGCCGGTGGGGGAGCGGATTCTGGTGACGGGGCGCGTGACCGACGAAGACGGGCGGGGTGTCCCTAATACGCTGCTAGAGATCTGGCAGGCCAA TGCCGCCGGTCGCTATATCCACAAGCTTGACCAGCATCTTGCCCCGCTTGATCCAAATTTCTCGGGGGCAGGGCGTACGGTTACGGGGGCTGATGGCTCTTATTCCTTCATCACGATCGT GCCGGGCGCCTATCCGGTCGTGGGGCTGCACAATGTCTGGCGCCCGCGCCACATCCATGTGTCGTTGTTCGGTCCGTCCTTCGTGACCCGCTTGGTTACCCAGATATATTTCGAGGGCGA TCCGCTGCTGAAATATGACACGATCTACAACACGGCGCCCGACATCTCGAAGCGCAGCATGGTGGCGCAGTTGGACATGGGCGCCACGCAATCCGAATGGGGCCTGACCTATCGCTTCGA CATCGTTCTGCGTGGGCGCAACGGCAGCTATTTCGAGGAACCCCATGACCACTAAGACCCCACTGACCATCACCCCCTCGCAGACTGTCGGGCCTTTCTATGCCTATTGCCTGACCCCGG AGGACTACGGGACGCTTCCACCGCTGTTCGGCGCGCAGCTTGCGACCGAGGACGCCGAAGGGGAACGGATTACGATCCAGGGAACGATCACGGACGGAGAGGGGGCCATGGTTCCCGATG CCTTGATCGAGATCTGGCAGCCGGACGGGCAGGGGCGTTTTGCTGGAGCCCATCCAGAGCTGCGGAATTCGGCCTTCAAGGGCTTCGGGCGCCGCCACTGTGACAAAAGCGGAAACTTCA GTTTCCAAACCGTGAAGCCTGGCCGGGTGCCCACTGCCGACGGCGTGATGCAGGCACCCCATATCGCTTTGTCGATCTTCGGCAAGGGATTGAACCGCCGGCTCTATACGCGGATCTACT TCGCAGACGAGGCATCGAATGCCGAGGACCCCGTTCTGTCGATGCTGTCCGAGGATGAGCGCGTGACCCTGATCGCCACCTCTGAATCGCCCGCCGCATATCGCCTCGACATCCGCCTGC AAGGCGACGGCGAAACGGTGTTTTTCGAGGCCTGAGTCGGCCGGCAAGTTTGCGGGGATCCGTCCGCCGCAATTGTGTTTCGCTATAGACGCCACGGCTGCCGCATGCCGCCGGGTGGAA GGGCCTTGCAAGGCCTGTCAACGGCGGAGTAAAATCCGGCCAGGCGGCGGAGTAAAACCAGGCCACTTGTGGCCCACGCATGAGACACCCGGGAGGGCGTAGCCCAAGCGGGGGTCTCAT GCGTGTGCGGCGGTTTTCTGGGGGTTCAGCCAGCCTTGCGGGCGCGGCTTTGAGCGAGACGATAGCTGTCGCCGTTCATCTCGAG

  37. 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?

  38. SIMILARITY CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGGCTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGCGGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAAGATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGCTGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGGTTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTCCCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTCCCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTTTATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTGATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTATTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATTGTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTGGCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAACGTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTTACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAGATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTCGCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTATTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCGACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTGGCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGCAGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCGATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGAATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTCAGAACGCTCAATCAATCAGAATGTAATCTTGACATAGAATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGTCCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCCCTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACGGTCTTCTCCGAACGGCGTTTTTTGGC CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGGCTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGCGGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAAGATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGCTGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGGTTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTCCCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTCCCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTTTATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTGATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTATTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATTGTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTGGCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAACGTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTTACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAGATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTCGCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTATTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCGACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTGGCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGCAGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCGATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGAATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTCAGAACGCTCAATCAATCAGAATGTAATCTTGACATAGAATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGTCCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCCCTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACGGTCTTCTCCGAACGGCGTTTTTTGGC the two sequences are (and look) the same

  39. SIMILARITY As now– but almost the same,but they seem to be dissimilar CTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGGCTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGCGGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAAGATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGCTGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGGTTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTCCCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTCCCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTTTATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTGATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTATTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATTGTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTGGCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAACGTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTTACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAGATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTCGCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTATTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCGACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTGGCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGCAGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCGATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGAATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTCAGAACGCTCAATCAATCAGAATGTAATCTTGACATAGAATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGTCCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCCCTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACGGTCTTCTCCGAACGGCGTTTTTTGGC AAACTCGAGACGCTGTTTCTGGGGTCATTCATTCTTGGCGGGCTGCAACTGCTGGTGTGACCGACGCGACCTGGCAGGCCGCGGTGCGCAACTGGCCGGGCGGACTAATGGTGGAGCAAAAGATCGGCATGTCCAGCGCACCTGAAGCTTGGGTGGTTGCTGCAATAGCAGCCTTCCTTATTGGCATGGCGAAGGGCGGTTTGGCCAATGTGGGGGTTATCGCCGTTCCCTTGATGTCCCTGGTCAAGCCGCCGCTTACCGCTGCCGGATTGCTGCTCCCGATCTATGTCGTTTCTGATGCATTCGGCGTCTGGCTTTATCGGCACCGGTATTCTGCCTCCAATCTGCGCATCCTGATTCCTTCGGGATTTTTTGGGGTCCTGATTGGCTGGTTATTGGCCGGGCAGATCTCCGACGCGATTGCCAGTGTCATTGTTGGTTTCACCGGCTGCGGCTTCGTGGCTGTGCTGCTGGCACGACGAGGGGTGCCATCGGTGCCGCGTCAAGCCAACGTGCCCAAAGGATGGTTTCTGGGGGTGGCCACCGGCTTTACCAGCTTTTTGACTCATTCCGGTGCGGCGACCTTCCAGATGTTCGTGCTGCCGCAACGGCTGGACAAGACCATGTTCGCGGGCACATCAACGCTTACCTTTGCTGCCATAAACCTATTCAAGATTCCGTCCTACTGGGCATTGGGACAGCTTTCGACTTCCTCGGTCATGTCCGCGCTAGTGTTGATTCCGGTGGCCGTGGCCGGGACGTTCGCAGGTGTTTTTGCGACGCGCAGGCTATCGACATCCTGGTTCTTCATTCTGGTCCAGGCGATGTTGCTGGTGGTCTCCATTCAGCTTCTGTGGAGGGGAATGTCGGATATCCTGAACTAGCTGGAGATCGCAATGTCAGAACGCTCAATCAATCAGAATGTAATCTTGACATAGAATACCGTTCCGATTTATTGCTTCGAGTGAAGCTGCCCGTCCGCTGAGATGTCATGACATTTTCCCCGCTTGATTCCGCCCTGCTTGGACCGTTGTTCGCGACCGATGAAATGCGCACGGTCTTCTCCGAACGGCGTTTTTTGGC BLAST, FASTA GLOBAL, LOCAL

  40. 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

  41. … 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

  42. Finding orfs

  43. Finding orfs

  44. … 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?

  45. FRAME SHIFT MUTATION – A SOLUTION FOR IT Translation in each open reading frame Stop codonsare not taken into account, just as missing aa It compares everything to everything at the protein level example BLASTX

  46. Six frame translation

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