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A platform for pattern discovery in sets of
biological sequences
C. Alland, J. Nicolas
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Framework bioinformatics platform of Genopole
Ouest
http//www.sb-roscoff.fr/ BioInfo-GPO/
Sequencing Genotyping
O. Collin H. Leroy
Bioinformatics
Functional exploration
Biochips

• Coordination
• Data Bases
• Bioinformatics Software
• High Performance
  Computing
• Teaching

PCIO SunFire 6800 56 UltraSparc III 56 Go RAM
Proteomics
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Welcome Page of the bioinformatics platform
service
http//idefix.univ-rennes1.fr8080/ Serveur-GPO/
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Software Page of the bioinformatics platform
service
http//idefix.univ-rennes1.fr8080/
Serveur-GPO/services.php
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Aims of the project
Set of biological sequences
Common characteristic or discriminant pattern

• Annotation of genomes Discovery of new
  genes/proteins
• Characterization of functional families
• Experimental comparison of methods Choice of
  complexities and representations of patterns
  Copy/Implementation of several algorithms
• Practical tool Parameter tuning Filtering

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Architecture of the platform
Visualization of results
Pattern Discovery Algorithms
Interface
Supervisor
Statistical Analysis of inter-motif regions
Refinement
Search of patterns
Practical Use
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Welcome page of the pattern discovery service
Regular languages inferring methods
Jonassen
Marsan
Pevzner
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Brazma hierarchy for (generalized) regular
patterns

• J full regular languages (finite automata)

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Example of the discovery of candidates in the
defensin family
Collaboration with GERM (C. Pineau, F.
Bourgeon) directed by B. Jégou, staffed with 40
people and specialized in researches on male
reproduction in mammals.

• Defensins are a major family of antimicrobial
  peptides found in mammals, cationic peptides of
  28-42 amino acids length containing 3
  intramolecular disulfide bonds.
• Starting point a set of 30 sequences (including
  all organisms), 4 for human.
• Aim discovery of new candidates

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Pratt principle of the algorithm

1. One starts from a pattern graph containing all
   the most specific allowed patterns covering at
   least k of the n sequences in the training set
2. A pattern search tree is explored starting from
   the most general one (empty pattern) and
   specializing it by adding allowed components
   (belonging to the pattern graph generalization
   operators) while patterns obtain a better score.
   Several scores and search strategies are
   available
3. The most significant patterns are filtered and a
   refinement phase may be applied to specialize
   flexible wild card with ambiguous letters

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Pratt three levels of use

1. Simple most parameters are fixed or simplified
2. Expert all parameters available
3. Meta Pratt is applied to sequences of patterns.

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Simple Pratt parameters
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Simple Pratt results
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Advanced Pratt parameters
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Advanced Pratt results
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Visualization of selected results
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Meta Pratt
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Search pattern in a databank
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Results of the search in a databank
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View of the search in a databank
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Statistical Analysis of inter-motif regions
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Results for refinment of patterns
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Reverse Search in a Genome
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Reverse Search in a Genome principle

• From the patterns and knowledge of exons/introns
  splicing, a formal grammar may be inferred.
• Genomes are translated in the six frames and
  compiled in a suffix tree data structure.
• Syntactical analysis is done with the help of
  operations on suffix trees and results in
  potential new candidates.

To jnicolas_at_irisa.fr Pattern
C-x(2,4)-G-x(1,3)-C-x(3,4)-C-x(7)-AG-HKNRST-C-
x(5,6)-C-C Organisme Chromosome Phase
Position LengthOcc Length Ch preOcc
Occ postOcc No match No match No match No
match No match No match
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Conclusion / Perspectives

• 10 new potential defensins discovered
• Importance of a complete environment coupling
  highly expressive patterns with syntactical
  search in banks
• Current research  meta level  using
  grammatical inference. Infer any regular language
  from a set of positive AND negative instances.
• Open questions Better filtering of patterns,
  introduction of probabilities, long distance
  interaction.

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