Universitat%20Aut

1
explosion of biological data
2
genome technologies

• DNA sequencing
• DNA microarrays
• mass spectroscopy and 2-D gels
• yeast two hibrids
• X-ray cristallography and NMR

3
growth of sequence data
4
Moores law
5
google hits X-informatics
bioinformatics 2,270,000
chemoinformatics 10,600
astroinformatics 31
neuroinformatics 49,300
socioinformatics 318
geoinformatics 38,000
meteoinformatics 2
econoinformatics 83
ecoinformatics 36,400
biology 17,000,000
6
decodificació del genoma
the genome sequence

• ACTCAGCCCCAGCGGAGGTGAAGGACGTCCTTCCCCAGGAGCCGGTGAGA
  AGCGCAGTCGGGGGCACGGGGATGAGCTCAGGGGCCTCTAGAAAGATGTA
  GCTGGGACCTCGGGAAGCCCTGGCCTCCAGGTAGTCTCAGGAGAGCTACT
  CAGGGTCGGGCTTGGGGAGAGGAGGAGCGGGGGTGAGGCCAGCAGCAGGG
  GACTGGACCTGGGAAGGGCTGGGCAGCAGAGACGACCCGACCCGCTAGAA
  GGTGGGGTGGGGAGAGCATGTGGACTAGGAGCTAAGCCACAGCAGGACCC
  CCACGAGTTGTCACTGTCATTTATCGAGCACCTACTGGGTGTCCCCAGTG
  TCCTCAGATCTCCATAACTGGGAAGCCAGGGGCAGCGACACGGTAGCTAG
  CCGTCGATTGGAGAACTTTAAAATGAGGACTGAATTAGCTCATAAATGGA
  AAACGGCGCTTAAATGTGAGGTTAGAGCTTAGAATGTGAAGGGAGAATGA
  GGAATGCGAGACTGGGACTGAGATGGAACCGGCGGTGGGGAGGGGGAGGG
  GGTGTGGAATTTGAACCCCGGGAGAGAAAGATGGAATTTTGGCTATGGAG
  GCCGACCTGGGGATGGGGAAATAAGAGAAGACCAGGAGGGAGTTAAATAG
  GGAATGGGTTGGGGGCGGCTTGGTAACTGTTTGTGCTGGGATTAGGCTGT
  TGCAGATAATGGAGCAAGGCTTGGAAGGCTAACCTGGGGTGGGGCCGGGT
  TGGGGTCGGGCTGGGGGCGGGAGGAGTCCTCACTGGCGGTTGATTGACAG
  TTTCTCCTTCCCCAGACTGGCCAATCACAGGCAGGAAGATGAAGGTTCTG
  TGGGCTGCGTTGCTGGTCACATTCCTGGCAGGTATGGGGCGGGGCTTGCT
  CGGTTTTCCCCGCTTCTCCCCCTCTCATCCTCACCTCAACCTCCTGGCCC
  CATTCAAGCACACCCTGGGCCCCCTCTTCTTCTGCTGGTCTGTCCCCTGA
  GGGGAAAGCCCAGGTCTGAGGCTTCTATGCTGCTTTCTGGCTCAGAACAG
  CGATTTGACGCTCTGTGAGCCTCGGTTCCTCCCCCGCTTTTTTTTTTTCA
  GCCAGAGTCTCACTCTGTCGCCCAGGCTGGAGTGCAGTGGCGCAATCTCA
  GCTCACTGCAAGCTCCGCCTCCCGGGTTCACGCTATTCTCCCGCCTCAGC
  CTCCCGAGTAGCTGGGACTACAGGCGCCCGCCACCATGCCCGGCTAATTT
  TTTGTACTTTGAGTAGGGAAGGGGTTTCACTGTATTATCCAGGATGGTCT
  CTATCTCCTGACCTCGTGATCTGCCCGCCTGGCCTCCCAAAGTGCTGGAA
  TTACAGGCGTGAGCCTCCGCGCCCGGCCTCCCCATCCTTAATATAGGAGT
  TAGAAGTTTTTGTTTGTTTGTTTTGTTTTGTTTTTGTTTTGTTTTGAGAT
  GAAGTCCCTCTGTCGCCCAGGCTGGAGTGCAGTGGCTCCCAGGCTGGAGT
  TCAGTGGCTGGATCTCGGCTCACTGCAAGCTCCGCCTCCCAGGTTCACGC
  CATTCTCCTGCCTCAGCCTCCGGAGTAGCTGGGACTACAGGAACATGCCA
  CCACACCCGACTAACTTTTTTTGTATTTTTAGTAGAGACGGGGTTTCACC
  ATGTTGGCCAGGCTGGTCTGGAACTCCTGACCTCAGGTGATCTGCCTGCT
  TCAACCTCCCAAAGTGCTGGGATTACAGACGTGGGCCACCGCGCCCGGCT
  GGGAGTTAAGAGGTTTCTAATGCATTGCATTAGAATACCAGACACGGGAC
  AGCTGTGATCTTTATTCTCCATCACCCCACACAGCCCTGCCTGGGGCACA
  CAAGGACACTCAATACACGCTTTTCGGGCGCGGTGGCTCAAGCTGTAATC
  CCAGCACTTTGGGAGGCTGAGGCGGGTGGTACATGAGGTCAGGAGATCGA
  GACCATCCTGGCTAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAA
  AACTAGCCCGGGCGTGGTGGCGGGCGCCTGTAGTCCCAGCTACTCGGAGG
  CTGAGGCAGGAGAATGGCGTGAACCTGGGAGGCGGAGCTTGCAGTGAGCC
  GAGATCGCGCCACTGCACTCCAGCCTGGGTGACACAGCGCGAGACTCCGT
  CTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATACACGCTTTTCCGCTA
  GGCACGGTGGCTCACCCCTGTAATCCCAGCATTTTGGGAGGCCAAGGTGG
  GAGGATCACTTGAGCCCAGGAGTTCAACACCAGACTCAGCAACATAGTGA
  GACTCTCTCTACTAAAAATACAAAAATTAGCCAGGCCTGGTGCCACACAC
  CTGTGGTCCCAGCTACTCAGAAGGCTAAGGCAGGAGGATCGCTTAAGCCC
  AGAAGGTCAAGGTTGCAGTGAACCACGTTCAGGCCACTGCAGTCCAGCCT
  GGGTGACAGAGCAAGACCCTGTCTGTAAATAAATAACGCTTTTCAAGTGA
  TTAAACAGACTCCCCCCTCACCCTGCCCACCATGGCTCCAAAGCAGCATT
  TGTGGAGCACCTTCTGTGTGCCCCTAGGTACTAGCTGCCTGGACGGGGTC
  AGAAGGAACCTGAACCACCTTCAACTTGTTCCACACAGGATGCCAGGCCA
  AGGTGGAGCAACCGGTGGAGCCAGAGACAGAACCCGACGTTCGCCAGCAG
  GCTGAGTGGCAGAGCGGCCAGCCCTGGGAGCTGGCACTGGGTCGCTTTTG
  GGATTACCTGCGCTGGGTGCAGACACTGTCTGAGCAGGTGCAGGAGGAGC
  TGCTCAGCCCCCAGGTCACCCAGGAACTGACGTGAGTGTCCCCATCCCGG
  CCCTTGACCCTCCTGGTGGGCGGCTATACCTCCCCAGGTCCAGGTTTCAT
  TCTGCCCCTGCCACTAAGTCTTGGGGGCCTGGGTCTCTGCTGGTTCTAGC
  TTCCTCTTCCCATTTCTGACTCCTGGCTTTAGCTCTCTGGAATTCTCTCT
  CTCAGTTCTGTTTCTCCCTCTTCCCTTCTGACTCAGCCTGTCACACTCGT
  CCTGGCGCTGTCTCTGTCCTTCACTAGCTCTTTTATATAGAGACAGAGAG
  ATGGGGTCTCACTGTGTTGCCCAGGCTGGTCTTGAACTTCTGGGCTCAAG
  CGATCCTCCCACCTCGCCTCCCAAAGTGCTGGGAATAGAGACATGAGCCA
  CCTTGCTCGGCCTCCTAGCTCTTTCTTCGTCTCTGCCTCTGCTCTCTGCG
  TCTGTCTTTGTCTCCTCTCTGCCTCTGTCCCGTTCCTTCTCTCTTGGTTC
  ACTGCCCTTCTGTCTCTCCCTGTTCTCCTTAGGAGACTCTCCTCTCTTCC
  TTCTCGAGTCTCTCTGGCTGATCCCCATCTCACCCACACCTATCC

7
the genome sequence
ACTCAGCCCCAGCGGAGGTGAAGGACGTCCTTCCCCAGGAGCCGGTGAGA
AGCGCAGTCGGGGGCACGGGGATGAGCTCAGGGGCCTCTAGAAAGATGTA
GCTGGGACCTCGGGAAGCCCTGGCCTCCAGGTAGTCTCAGGAGAGCTACT
CAGGGTCGGGCTTGGGGAGAGGAGGAGCGGGGGTGAGGCCAGCAGCAGGG
GACTGGACCTGGGAAGGGCTGGGCAGCAGAGACGACCCGACCCGCTAGAA
GGTGGGGTGGGGAGAGCATGTGGACTAGGAGCTAAGCCACAGCAGGACCC
CCACGAGTTGTCACTGTCATTTATCGAGCACCTACTGGGTGTCCCCAGTG
TCCTCAGATCTCCATAACTGGGAAGCCAGGGGCAGCGACACGGTAGCTAG
CCGTCGATTGGAGAACTTTAAAATGAGGACTGAATTAGCTCATAAATGGA
AAACGGCGCTTAAATGTGAGGTTAGAGCTTAGAATGTGAAGGGAGAATGA
GGAATGCGAGACTGGGACTGAGATGGAACCGGCGGTGGGGAGGGGGAGGG
GGTGTGGAATTTGAACCCCGGGAGAGAAAGATGGAATTTTGGCTATGGAG
GCCGACCTGGGGATGGGGAAATAAGAGAAGACCAGGAGGGAGTTAAATAG
GGAATGGGTTGGGGGCGGCTTGGTAACTGTTTGTGCTGGGATTAGGCTGT
TGCAGATAATGGAGCAAGGCTTGGAAGGCTAACCTGGGGTGGGGCCGGGT
TGGGGTCGGGCTGGGGGCGGGAGGAGTCCTCACTGGCGGTTGATTGACAG
TTTCTCCTTCCCCAGACTGGCCAATCACAGGCAGGAAGATGAAGGTTCTG
TGGGCTGCGTTGCTGGTCACATTCCTGGCAGGTATGGGGCGGGGCTTGCT
CGGTTTTCCCCGCTTCTCCCCCTCTCATCCTCACCTCAACCTCCTGGCCC
CATTCAAGCACACCCTGGGCCCCCTCTTCTTCTGCTGGTCTGTCCCCTGA
GGGGAAAGCCCAGGTCTGAGGCTTCTATGCTGCTTTCTGGCTCAGAACAG
CGATTTGACGCTCTGTGAGCCTCGGTTCCTCCCCCGCTTTTTTTTTTTCA
GCCAGAGTCTCACTCTGTCGCCCAGGCTGGAGTGCAGTGGCGCAATCTCA
GCTCACTGCAAGCTCCGCCTCCCGGGTTCACGCTATTCTCCCGCCTCAGC
CTCCCGAGTAGCTGGGACTACAGGCGCCCGCCACCATGCCCGGCTAATTT
TTTGTACTTTGAGTAGGGAAGGGGTTTCACTGTATTATCCAGGATGGTCT
CTATCTCCTGACCTCGTGATCTGCCCGCCTGGCCTCCCAAAGTGCTGGAA
TTACAGGCGTGAGCCTCCGCGCCCGGCCTCCCCATCCTTAATATAGGAGT
TAGAAGTTTTTGTTTGTTTGTTTTGTTTTGTTTTTGTTTTGTTTTGAGAT
GAAGTCCCTCTGTCGCCCAGGCTGGAGTGCAGTGGCTCCCAGGCTGGAGT
TCAGTGGCTGGATCTCGGCTCACTGCAAGCTCCGCCTCCCAGGTTCACGC
CATTCTCCTGCCTCAGCCTCCGGAGTAGCTGGGACTACAGGAACATGCCA
CCACACCCGACTAACTTTTTTTGTATTTTTAGTAGAGACGGGGTTTCACC
ATGTTGGCCAGGCTGGTCTGGAACTCCTGACCTCAGGTGATCTGCCTGCT
TCAACCTCCCAAAGTGCTGGGATTACAGACGTGGGCCACCGCGCCCGGCT
GGGAGTTAAGAGGTTTCTAATGCATTGCATTAGAATACCAGACACGGGAC
AGCTGTGATCTTTATTCTCCATCACCCCACACAGCCCTGCCTGGGGCACA
CAAGGACACTCAATACACGCTTTTCGGGCGCGGTGGCTCAAGCTGTAATC
CCAGCACTTTGGGAGGCTGAGGCGGGTGGTACATGAGGTCAGGAGATCGA
GACCATCCTGGCTAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAA
AACTAGCCCGGGCGTGGTGGCGGGCGCCTGTAGTCCCAGCTACTCGGAGG
CTGAGGCAGGAGAATGGCGTGAACCTGGGAGGCGGAGCTTGCAGTGAGCC
GAGATCGCGCCACTGCACTCCAGCCTGGGTGACACAGCGCGAGACTCCGT
CTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATACACGCTTTTCCGCTA
GGCACGGTGGCTCACCCCTGTAATCCCAGCATTTTGGGAGGCCAAGGTGG
GAGGATCACTTGAGCCCAGGAGTTCAACACCAGACTCAGCAACATAGTGA
GACTCTCTCTACTAAAAATACAAAAATTAGCCAGGCCTGGTGCCACACAC
CTGTGGTCCCAGCTACTCAGAAGGCTAAGGCAGGAGGATCGCTTAAGCCC
AGAAGGTCAAGGTTGCAGTGAACCACGTTCAGGCCACTGCAGTCCAGCCT
GGGTGACAGAGCAAGACCCTGTCTGTAAATAAATAACGCTTTTCAAGTGA
TTAAACAGACTCCCCCCTCACCCTGCCCACCATGGCTCCAAAGCAGCATT
TGTGGAGCACCTTCTGTGTGCCCCTAGGTACTAGCTGCCTGGACGGGGTC
AGAAGGAACCTGAACCACCTTCAACTTGTTCCACACAGGATGCCAGGCCA
AGGTGGAGCAACCGGTGGAGCCAGAGACAGAACCCGACGTTCGCCAGCAG
GCTGAGTGGCAGAGCGGCCAGCCCTGGGAGCTGGCACTGGGTCGCTTTTG
GGATTACCTGCGCTGGGTGCAGACACTGTCTGAGCAGGTGCAGGAGGAGC
TGCTCAGCCCCCAGGTCACCCAGGAACTGACGTGAGTGTCCCCATCCCGG
CCCTTGACCCTCCTGGTGGGCGGCTATACCTCCCCAGGTCCAGGTTTCAT
TCTGCCCCTGCCACTAAGTCTTGGGGGCCTGGGTCTCTGCTGGTTCTAGC
TTCCTCTTCCCATTTCTGACTCCTGGCTTTAGCTCTCTGGAATTCTCTCT
CTCAGTTCTGTTTCTCCCTCTTCCCTTCTGACTCAGCCTGTCACACTCGT
CCTGGCGCTGTCTCTGTCCTTCACTAGCTCTTTTATATAGAGACAGAGAG
ATGGGGTCTCACTGTGTTGCCCAGGCTGGTCTTGAACTTCTGGGCTCAAG
CGATCCTCCCACCTCGCCTCCCAAAGTGCTGGGAATAGAGACATGAGCCA
CCTTGCTCGGCCTCCTAGCTCTTTCTTCGTCTCTGCCTCTGCTCTCTGCG
TCTGTCTTTGTCTCCTCTCTGCCTCTGTCCCGTTCCTTCTCTCTTGGTTC
ACTGCCCTTCTGTCTCTCCCTGTTCTCCTTAGGAGACTCTCCTCTCTTCC
TTCTCGAGTCTCTCTGGCTGATCCCCATCTCACCCACACCTATCC
8
the genome sequence
ACTCAGCCCCAGCGGAGGTGAAGGACGTCCTTCCCCAGGAGCCGGTGAGA
AGCGCAGTCGGGGGCACGGGGATGAGCTCAGGGGCCTCTAGAAAGATGTA
GCTGGGACCTCGGGAAGCCCTGGCCTCCAGGTAGTCTCAGGAGAGCTACT
CAGGGTCGGGCTTGGGGAGAGGAGGAGCGGGGGTGAGGCCAGCAGCAGGG
GACTGGACCTGGGAAGGGCTGGGCAGCAGAGACGACCCGACCCGCTAGAA
GGTGGGGTGGGGAGAGCATGTGGACTAGGAGCTAAGCCACAGCAGGACCC
CCACGAGTTGTCACTGTCATTTATCGAGCACCTACTGGGTGTCCCCAGTG
TCCTCAGATCTCCATAACTGGGAAGCCAGGGGCAGCGACACGGTAGCTAG
CCGTCGATTGGAGAACTTTAAAATGAGGACTGAATTAGCTCATAAATGGA
AAACGGCGCTTAAATGTGAGGTTAGAGCTTAGAATGTGAAGGGAGAATGA
GGAATGCGAGACTGGGACTGAGATGGAACCGGCGGTGGGGAGGGGGAGGG
GGTGTGGAATTTGAACCCCGGGAGAGAAAGATGGAATTTTGGCTATGGAG
GCCGACCTGGGGATGGGGAAATAAGAGAAGACCAGGAGGGAGTTAAATAG
GGAATGGGTTGGGGGCGGCTTGGTAACTGTTTGTGCTGGGATTAGGCTGT
TGCAGATAATGGAGCAAGGCTTGGAAGGCTAACCTGGGGTGGGGCCGGGT
TGGGGTCGGGCTGGGGGCGGGAGGAGTCCTCACTGGCGGTTGATTGACAG
TTTCTCCTTCCCCAGACTGGCCAATCACAGGCAGGAAGATGAAGGTTCTG
TGGGCTGCGTTGCTGGTCACATTCCTGGCAGGTATGGGGCGGGGCTTGCT
CGGTTTTCCCCGCTTCTCCCCCTCTCATCCTCACCTCAACCTCCTGGCCC
CATTCAAGCACACCCTGGGCCCCCTCTTCTTCTGCTGGTCTGTCCCCTGA
GGGGAAAGCCCAGGTCTGAGGCTTCTATGCTGCTTTCTGGCTCAGAACAG
CGATTTGACGCTCTGTGAGCCTCGGTTCCTCCCCCGCTTTTTTTTTTTCA
GCCAGAGTCTCACTCTGTCGCCCAGGCTGGAGTGCAGTGGCGCAATCTCA
GCTCACTGCAAGCTCCGCCTCCCGGGTTCACGCTATTCTCCCGCCTCAGC
CTCCCGAGTAGCTGGGACTACAGGCGCCCGCCACCATGCCCGGCTAATTT
TTTGTACTTTGAGTAGGGAAGGGGTTTCACTGTATTATCCAGGATGGTCT
CTATCTCCTGACCTCGTGATCTGCCCGCCTGGCCTCCCAAAGTGCTGGAA
TTACAGGCGTGAGCCTCCGCGCCCGGCCTCCCCATCCTTAATATAGGAGT
TAGAAGTTTTTGTTTGTTTGTTTTGTTTTGTTTTTGTTTTGTTTTGAGAT
GAAGTCCCTCTGTCGCCCAGGCTGGAGTGCAGTGGCTCCCAGGCTGGAGT
TCAGTGGCTGGATCTCGGCTCACTGCAAGCTCCGCCTCCCAGGTTCACGC
CATTCTCCTGCCTCAGCCTCCGGAGTAGCTGGGACTACAGGAACATGCCA
CCACACCCGACTAACTTTTTTTGTATTTTTAGTAGAGACGGGGTTTCACC
ATGTTGGCCAGGCTGGTCTGGAACTCCTGACCTCAGGTGATCTGCCTGCT
TCAACCTCCCAAAGTGCTGGGATTACAGACGTGGGCCACCGCGCCCGGCT
GGGAGTTAAGAGGTTTCTAATGCATTGCATTAGAATACCAGACACGGGAC
AGCTGTGATCTTTATTCTCCATCACCCCACACAGCCCTGCCTGGGGCACA
CAAGGACACTCAATACACGCTTTTCGGGCGCGGTGGCTCAAGCTGTAATC
CCAGCACTTTGGGAGGCTGAGGCGGGTGGTACATGAGGTCAGGAGATCGA
GACCATCCTGGCTAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAA
AACTAGCCCGGGCGTGGTGGCGGGCGCCTGTAGTCCCAGCTACTCGGAGG
CTGAGGCAGGAGAATGGCGTGAACCTGGGAGGCGGAGCTTGCAGTGAGCC
GAGATCGCGCCACTGCACTCCAGCCTGGGTGACACAGCGCGAGACTCCGT
CTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATACACGCTTTTCCGCTA
GGCACGGTGGCTCACCCCTGTAATCCCAGCATTTTGGGAGGCCAAGGTGG
GAGGATCACTTGAGCCCAGGAGTTCAACACCAGACTCAGCAACATAGTGA
GACTCTCTCTACTAAAAATACAAAAATTAGCCAGGCCTGGTGCCACACAC
CTGTGGTCCCAGCTACTCAGAAGGCTAAGGCAGGAGGATCGCTTAAGCCC
AGAAGGTCAAGGTTGCAGTGAACCACGTTCAGGCCACTGCAGTCCAGCCT
GGGTGACAGAGCAAGACCCTGTCTGTAAATAAATAACGCTTTTCAAGTGA
TTAAACAGACTCCCCCCTCACCCTGCCCACCATGGCTCCAAAGCAGCATT
TGTGGAGCACCTTCTGTGTGCCCCTAGGTACTAGCTGCCTGGACGGGGTC
AGAAGGAACCTGAACCACCTTCAACTTGTTCCACACAGGATGCCAGGCCA
AGGTGGAGCAACCGGTGGAGCCAGAGACAGAACCCGACGTTCGCCAGCAG
GCTGAGTGGCAGAGCGGCCAGCCCTGGGAGCTGGCACTGGGTCGCTTTTG
GGATTACCTGCGCTGGGTGCAGACACTGTCTGAGCAGGTGCAGGAGGAGC
TGCTCAGCCCCCAGGTCACCCAGGAACTGACGTGAGTGTCCCCATCCCGG
CCCTTGACCCTCCTGGTGGGCGGCTATACCTCCCCAGGTCCAGGTTTCAT
TCTGCCCCTGCCACTAAGTCTTGGGGGCCTGGGTCTCTGCTGGTTCTAGC
TTCCTCTTCCCATTTCTGACTCCTGGCTTTAGCTCTCTGGAATTCTCTCT
CTCAGTTCTGTTTCTCCCTCTTCCCTTCTGACTCAGCCTGTCACACTCGT
CCTGGCGCTGTCTCTGTCCTTCACTAGCTCTTTTATATAGAGACAGAGAG
ATGGGGTCTCACTGTGTTGCCCAGGCTGGTCTTGAACTTCTGGGCTCAAG
CGATCCTCCCACCTCGCCTCCCAAAGTGCTGGGAATAGAGACATGAGCCA
CCTTGCTCGGCCTCCTAGCTCTTTCTTCGTCTCTGCCTCTGCTCTCTGCG
TCTGTCTTTGTCTCCTCTCTGCCTCTGTCCCGTTCCTTCTCTCTTGGTTC
ACTGCCCTTCTGTCTCTCCCTGTTCTCCTTAGGAGACTCTCCTCTCTTCC
TTCTCGAGTCTCTCTGGCTGATCCCCATCTCACCCACACCTATCC
9
(No Transcript)
10
gagttttatcgcttccatgacgcagaagttaacactttcggatatttctg
atgagtcgaaaaattatcttgataaagcaggaattactactgcttgttta
cgaattaaatcgaagtggactgctggcggaaaatgagaaaattcgaccta
tccttgcgcagctcgagaagctcttactttgcgacctttcgccatcaact
aacgattctgtcaaaaactgacgcgttggatgaggagaagtggcttaata
tgcttggcacgttcgtcaaggactggtttagatatgagtcacattttgtt
catggtagagattctcttgt
MALWTRLRPLLALLALWPPPPARAFVNQHLCGSHLVEALYLVCGERGFFY
TPKARREVEGPQVGALELAGGPGAGGLEGPPQKRGIVEQCCASVCSLYQL
ENYCN
11
probabilistic patterns ingene predictionroderic
guigó serrarobert castelo

• (IMIM-UPF-CRG)

12
decodificació del genoma
the genome sequence

• ACTCAGCCCCAGCGGAGGTGAAGGACGTCCTTCCCCAGGAGCCGGTGAGA
  AGCGCAGTCGGGGGCACGGGGATGAGCTCAGGGGCCTCTAGAAAGATGTA
  GCTGGGACCTCGGGAAGCCCTGGCCTCCAGGTAGTCTCAGGAGAGCTACT
  CAGGGTCGGGCTTGGGGAGAGGAGGAGCGGGGGTGAGGCCAGCAGCAGGG
  GACTGGACCTGGGAAGGGCTGGGCAGCAGAGACGACCCGACCCGCTAGAA
  GGTGGGGTGGGGAGAGCATGTGGACTAGGAGCTAAGCCACAGCAGGACCC
  CCACGAGTTGTCACTGTCATTTATCGAGCACCTACTGGGTGTCCCCAGTG
  TCCTCAGATCTCCATAACTGGGAAGCCAGGGGCAGCGACACGGTAGCTAG
  CCGTCGATTGGAGAACTTTAAAATGAGGACTGAATTAGCTCATAAATGGA
  AAACGGCGCTTAAATGTGAGGTTAGAGCTTAGAATGTGAAGGGAGAATGA
  GGAATGCGAGACTGGGACTGAGATGGAACCGGCGGTGGGGAGGGGGAGGG
  GGTGTGGAATTTGAACCCCGGGAGAGAAAGATGGAATTTTGGCTATGGAG
  GCCGACCTGGGGATGGGGAAATAAGAGAAGACCAGGAGGGAGTTAAATAG
  GGAATGGGTTGGGGGCGGCTTGGTAACTGTTTGTGCTGGGATTAGGCTGT
  TGCAGATAATGGAGCAAGGCTTGGAAGGCTAACCTGGGGTGGGGCCGGGT
  TGGGGTCGGGCTGGGGGCGGGAGGAGTCCTCACTGGCGGTTGATTGACAG
  TTTCTCCTTCCCCAGACTGGCCAATCACAGGCAGGAAGATGAAGGTTCTG
  TGGGCTGCGTTGCTGGTCACATTCCTGGCAGGTATGGGGCGGGGCTTGCT
  CGGTTTTCCCCGCTTCTCCCCCTCTCATCCTCACCTCAACCTCCTGGCCC
  CATTCAAGCACACCCTGGGCCCCCTCTTCTTCTGCTGGTCTGTCCCCTGA
  GGGGAAAGCCCAGGTCTGAGGCTTCTATGCTGCTTTCTGGCTCAGAACAG
  CGATTTGACGCTCTGTGAGCCTCGGTTCCTCCCCCGCTTTTTTTTTTTCA
  GCCAGAGTCTCACTCTGTCGCCCAGGCTGGAGTGCAGTGGCGCAATCTCA
  GCTCACTGCAAGCTCCGCCTCCCGGGTTCACGCTATTCTCCCGCCTCAGC
  CTCCCGAGTAGCTGGGACTACAGGCGCCCGCCACCATGCCCGGCTAATTT
  TTTGTACTTTGAGTAGGGAAGGGGTTTCACTGTATTATCCAGGATGGTCT
  CTATCTCCTGACCTCGTGATCTGCCCGCCTGGCCTCCCAAAGTGCTGGAA
  TTACAGGCGTGAGCCTCCGCGCCCGGCCTCCCCATCCTTAATATAGGAGT
  TAGAAGTTTTTGTTTGTTTGTTTTGTTTTGTTTTTGTTTTGTTTTGAGAT
  GAAGTCCCTCTGTCGCCCAGGCTGGAGTGCAGTGGCTCCCAGGCTGGAGT
  TCAGTGGCTGGATCTCGGCTCACTGCAAGCTCCGCCTCCCAGGTTCACGC
  CATTCTCCTGCCTCAGCCTCCGGAGTAGCTGGGACTACAGGAACATGCCA
  CCACACCCGACTAACTTTTTTTGTATTTTTAGTAGAGACGGGGTTTCACC
  ATGTTGGCCAGGCTGGTCTGGAACTCCTGACCTCAGGTGATCTGCCTGCT
  TCAACCTCCCAAAGTGCTGGGATTACAGACGTGGGCCACCGCGCCCGGCT
  GGGAGTTAAGAGGTTTCTAATGCATTGCATTAGAATACCAGACACGGGAC
  AGCTGTGATCTTTATTCTCCATCACCCCACACAGCCCTGCCTGGGGCACA
  CAAGGACACTCAATACACGCTTTTCGGGCGCGGTGGCTCAAGCTGTAATC
  CCAGCACTTTGGGAGGCTGAGGCGGGTGGTACATGAGGTCAGGAGATCGA
  GACCATCCTGGCTAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAA
  AACTAGCCCGGGCGTGGTGGCGGGCGCCTGTAGTCCCAGCTACTCGGAGG
  CTGAGGCAGGAGAATGGCGTGAACCTGGGAGGCGGAGCTTGCAGTGAGCC
  GAGATCGCGCCACTGCACTCCAGCCTGGGTGACACAGCGCGAGACTCCGT
  CTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATACACGCTTTTCCGCTA
  GGCACGGTGGCTCACCCCTGTAATCCCAGCATTTTGGGAGGCCAAGGTGG
  GAGGATCACTTGAGCCCAGGAGTTCAACACCAGACTCAGCAACATAGTGA
  GACTCTCTCTACTAAAAATACAAAAATTAGCCAGGCCTGGTGCCACACAC
  CTGTGGTCCCAGCTACTCAGAAGGCTAAGGCAGGAGGATCGCTTAAGCCC
  AGAAGGTCAAGGTTGCAGTGAACCACGTTCAGGCCACTGCAGTCCAGCCT
  GGGTGACAGAGCAAGACCCTGTCTGTAAATAAATAACGCTTTTCAAGTGA
  TTAAACAGACTCCCCCCTCACCCTGCCCACCATGGCTCCAAAGCAGCATT
  TGTGGAGCACCTTCTGTGTGCCCCTAGGTACTAGCTGCCTGGACGGGGTC
  AGAAGGAACCTGAACCACCTTCAACTTGTTCCACACAGGATGCCAGGCCA
  AGGTGGAGCAACCGGTGGAGCCAGAGACAGAACCCGACGTTCGCCAGCAG
  GCTGAGTGGCAGAGCGGCCAGCCCTGGGAGCTGGCACTGGGTCGCTTTTG
  GGATTACCTGCGCTGGGTGCAGACACTGTCTGAGCAGGTGCAGGAGGAGC
  TGCTCAGCCCCCAGGTCACCCAGGAACTGACGTGAGTGTCCCCATCCCGG
  CCCTTGACCCTCCTGGTGGGCGGCTATACCTCCCCAGGTCCAGGTTTCAT
  TCTGCCCCTGCCACTAAGTCTTGGGGGCCTGGGTCTCTGCTGGTTCTAGC
  TTCCTCTTCCCATTTCTGACTCCTGGCTTTAGCTCTCTGGAATTCTCTCT
  CTCAGTTCTGTTTCTCCCTCTTCCCTTCTGACTCAGCCTGTCACACTCGT
  CCTGGCGCTGTCTCTGTCCTTCACTAGCTCTTTTATATAGAGACAGAGAG
  ATGGGGTCTCACTGTGTTGCCCAGGCTGGTCTTGAACTTCTGGGCTCAAG
  CGATCCTCCCACCTCGCCTCCCAAAGTGCTGGGAATAGAGACATGAGCCA
  CCTTGCTCGGCCTCCTAGCTCTTTCTTCGTCTCTGCCTCTGCTCTCTGCG
  TCTGTCTTTGTCTCCTCTCTGCCTCTGTCCCGTTCCTTCTCTCTTGGTTC
  ACTGCCCTTCTGTCTCTCCCTGTTCTCCTTAGGAGACTCTCCTCTCTTCC
  TTCTCGAGTCTCTCTGGCTGATCCCCATCTCACCCACACCTATCC

13
the amino acid sequence of the proteins

• QIKDLLVSSSTDLDTTLVLVNAIYFKGMWKTAFNAEDTREMPFHVTKQES
  KPVQMMCMNNSFNVATLPAEKMKILELPFASGDLSMLVLLPDEVSDLERI
  EKTINFEKLTEWTNPNTMEKRRVKVYLPQMKIEEKYNLTSVLMALGMTDL
  FIPSANLTGISSAESLKISQAVHGAFMELSEDGIEMAGSTGVIEDIKHSP
  ESEQFRADHPFLFLIKHNPTNTIVYFGRYWS

14
eukaryotic gene structure
15
(No Transcript)
16
eukaryotic gene structure
acceptor
donor
17
modeling donor sites
GGG GTGAGCCCAG GTG GTAAGAGACA TAG GTGAGTGTGA GCG
GTAGGTACTC CAG GTAATTTTCT AAG GTAGGCTCTG AGG
GTGAGTCCAG GAG GTGGGTCACA CAG GTCAGTCTTT ACG
GTAAGACCTG CAG GTGGGTGCTG CAG GTAAGCAGTG AGG
GTGAGTTCAG CAG GTAAGCATTG AGG GTGAGTTCAG
18
the donor site pattern reflects underlying
biological constraints
19
the donor site pattern reflects underlying
biological constraints
20
the donor site pattern
21
prediction of splice sites
22
modeling dependencies
23
modeling dependencies, first order markov models
Weigth Array Models (WAM) Zhang and Marr (1993)
24
(No Transcript)
25
extending the Markov order

• Salzberg et al., (1998) Interpolated Markov
  Models
• Cawley (2000) Variable length Markov Models

26
modeling non-local dependencies in splice sites

• Burge and Karlin, 1997. Maximal Dependence
  Decomposition (MDD)
• Agarwal and Bafna, 1998
• Yeo and Burge, 2003
• Zhao et al., 2004. Permutated Variable Length
  Markov Models (PVMLL)
• Cai et al., 2000 Dash and Gopalakrishman, 2001.
  Bayesian Networks
• Castelo and Guigó, 2004, Inclusion-Driven Learned
  Bayesian Networks (idlBNS)

27
idlBNs

• Bayesian Networks allow one to learn from the
  data those (in)dependencies that conform an
  acyclic digraph (DAG).
• Inclusion-driven structure learning algorithms
  (Castelo and Kocka, 2003) under the assumption
  that the data is sampled from a DAG-distribution,
  and in the limit of the size of the sample they
  learn a correct DAG structure using a consistent
  scoring metric.

28
(No Transcript)
29
prediction of splice sites vs. gene prediction
30
sites
exons
genes
e8
e1
the gene prediction problem
31
(No Transcript)
32
gene prediction accuracy
BG-570 SN SP (SN.SP)/2
PWM 0.36 0.35 0.355
FMM 0.38 0.43 0.405
idlBN 0.45 0.37 0.410
SN fraction of true exons predicted correctly
SP fraction of predicted exons that are correct
33
(codon usage table)
34
coding statistics
35
the real accuracy
Accuracy on human chromosome 22
sensitivity specificity
genscan 0.79 0.53
twinscan 0.80 0.62
SGP 0.79 0.66
36
search for additional patterns

• real exons with weak splice sites, Fairbrother
  et al., 2002
• pseudoexons with strong splice sites, Zhang and
  Chasin, 2004

37
Fairbrother et al., 2002. splicing enhancers in
exons with weak sites
38
Zhang and Chasin, 2004. splicing silencers in
pseudoexons with strong sites
39
Bioinformatic approach scheme
40
G-rich motifs are able to influence 5 splice
site recognition
NE
?U1
U1
(1)
(2)
(3)
41
in collaboration with Juan Valcárcel, CRG
42
INHIBITORY EFFECT OF ON 5SS RECOGNITION BY U1
snRNP
U1
Weak 5ss followed by a G-rich element
(1)
Deletion of the G-rich element in (1)
(2)
Strong 5 ss
(3)
43
TIA-1 promotes U1 snRNP binding to weak 5 splice
sites Followed by uridine-rich sequences
XXXXXXXXX
TIA-1
44
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45
the second genetic code

• genetic code
• mapping of nucleotide triplets into 3 into the
  twenty aminoacids
• highly deterministic a given triplet always
  codes for the same amino acid
• splicing code
• mapping of nucleotide sequences into 3 and 5
  intron boundaries.
• inherently stochastic the probability of an
  splicing sequence to participate in the
  definition of an inron boundary ranges from zero
  to one, and it is conditionated to very many
  different factors (which could be other sequences)