Algorithms FDK Center of Excellence

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Algorithms FDK Center of Excellence

• Esko Ukkonen

Department of Computer Science
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Mission

• Sequences Are Everywhere
• combinatorial pattern matching
• pattern discovery in sequences
• dynamic programming, automata theory, advanced
  data structures, probabilistic modeling
• algorithms on strings and biological sequence
  analysis studied since 1980 many our results
  appear in textbooks

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J Kärkkäinen, P Sanders S Burkhardt Linear
work suffix array construction. J ACM 53 (2006),
918-936

• direct construction of a suffix array in linear
  time
• immediately included in teaching materials
  internationally

abaab baab aab ab b
SuffixTree(abaab)
a

baab
sort
ab
baab
aab ab abaab b baab
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V Mäkinen, G Navarro E Ukkonen Transposition
invariant string matching. J Algorithms 56
(2005)E Ukkonen, K Lemström V Mäkinen
Sweepline the music! LNCS 2598 (2003), 330-342.

• Transposition invariant variants of string
  matching algorithms
• Music retrieval

Transposition by -2
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P Rastas, M Koivisto, H Mannila E Ukkonen A
hidden Markov technique for haplotype
reconstruction. WABI 2005, 140-151
founder
SNP
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O Hallikas al Genome-wide prediction of
mammalian enhancers based on analysis of
transcription-factor binding affinity. Cell 124
(2006), 47-59.
enhancer module
gene1
gene2
gene3
gene4
DNA
transcription
transcription factors
RNA
translation
Proteins
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Computational identification of enhancer elements

• Preserved in evolution
• Affinities of functional cis-elements.
• Spatial arrangement of elements within a module.

Human
Mouse
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Enhancer prediction for N-myc (Cell 2006, Nat.
Protocols 2006)
200 kb Mouse N-Myc genomic region
200 kb Human N-Myc genomic region
Conserved GLI binding sites in two predicted
enhancer elements, CM5 and CM7
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A Rantanen Algorithms for 13C Metabolic Flux
Analysis. PhD Thesis 2006.
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Future goals

• Indexing sequential data for approximate searches
• Distance functions for sequences
• Theoretical framework, efficient evaluation,
  complexity bounds, relations between distances
• Application specific distances (XML, images,
  music, )
• Finding structure and signals in sequences
• Supervised and unsupervised learning of signals
• Statistical significance of the findings
• Systems biology How works the program encoded in
  genomes?