Geometric Crossovers for Supervised Motif Discovery Rolv Seehuus NTNU.
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Transcript of Geometric Crossovers for Supervised Motif Discovery Rolv Seehuus NTNU.
Geometric Crossovers for Supervised Motif DiscoveryRolv Seehuus
NTNU
Motivation and Scope
Try out the applicability of the geometric framework, on a supervised motif discovery problem Compare its merits to a previously used operator.
In practice, we test on a very easy problem that existing software can solve easily
Value as test case Building block for more complex motif discovery
problems, that current algorithms can not solve satisfactory
Motif Discovery
Has become a standard problem in bioinformatics Given a set of sequences, figure out what is
special with it …by eliciting motifs in the dataset Differing by…
Motif model Learning algorithms Scoring functions
The Standard Approach
Do analysis of the positive set of sequences …background distribution… …information content… …statistical significance…
Report motif
Motif discovery as a classification problem Always at least two datasets: The positive, and “the rest” Choose a negative dataset Report motifs best suited to discriminate No need to learn a background model The statistical significance of the motif can be given Discriminative motif discovery has received increased
attention lately
Classification problem
Protein sequences, from the SwissProt database Classified according to protein family (as
specified in the Prosite database) Selected six families, that previously have been
shown to be hard to classify under similar circumstances.
Some of the families can be said to have an overrepresented motif as the ones we can train on
The Potential Negative Data Set
Huge, compared to the negative Quite common in bioinformatics, and an interesting
problem to cope with in its own right In field:
randomly generated sequences one set of randomly selected sequences random rearrangement of the positive sequences (data not
shown)
The “best practice” was to select the samples randomly from the negative set each generation, so that their size matches the positive set.
Motif Model
Twenty amino acids Wildcard
C...C.C..CDMEGACGGSCACSTCHVIVDP
Motif match, positive sequence
Operators on Motifs
Unit edit move as mutation Mut(A) = {Insert, Delete or Replace a token}
Substring Swapping Crossover (for comparison) Two-point Geometric Crossover
Geometric Crossover
Search space have a metric Mutation is a move in search space Crossover yield children found on the shortest
path between the parents in search space Successfully applied to other problems
Geometric Crossover for Motifs
View motifs as sequences Basic assumption: The edit distance is a good way
to move around in motif space A crossover based on the edit distance, should
yield a good crossover for motif discovery We (arbitrarily) choose unit costs for insertions,
deletions and substitutions
Sequence Alignment
Alignment: put spaces (-) in both sequences such as they become of the same length
Seq1’= agcacac-a Seq2’= a-cacacta Score: 2 An Optimal alignment is an alignment with minimal score The score of the optimal alignment of two sequences
equals their edit distance There often are multiple optimal alignments
Homologous Crossover
1. Pick an optimal alignment for two parent sequences
2. Generate a crossover mask as long as the alignment
3. Recombine as traditional crossover
4. Remove dashes from offspring
Mask = 1101100Seq1’= BANANA-Seq2’= -ANANASSeqA’= BANANASSeqB’= -ANANA-
Child1’= BANANASChild2’= ANANA
Experiments
Two crossovers with same parameters, and mutation only Ten fold cross validation:
Partitioned datasets in ten pieces Trained on 9/10ths Tested the best motif on the remaining test set
Trained on randomly selected subset of SwissProt Tested on entire SwissProt Fitness: Scaled Pearson correlation of confusion matrix
Dynamic behavior during evolution
Maximum Values
Max
Cytochrome
Include the following fragment of a highly conserved motif: C…CH
Which geometric crossover find While substring swapping finds: CH Conservation of length keeps us in the correct
ballpark CH representa local maximum for substring swap
Ferredoxin Contains the following motif: C..C..C...C[PH] Which Substring Swap finds While Geometric Crossover don’t Conservation of length keeps us from finding the correct motif
Population Means
Means
Classification Performance
SubSwap Geometric MutationName Train Test Train Test Train Test
Carbamoyl 0.99 0.57 0.99 0.56 0.99 0.56Dna J 0.9 0.23 0.91 0.23 0.92 0.23Cytochrome 0.8 0.1 0.98 0.36 0.97 0.36Ferredoxin 1 0.87 0.81 0.13 0.96 0.39HLH 0.5 0.07 0.52 0.06 0.59 0.08EF Hand 0.62 0.15 0.67 0.19 0.66 0.19
Medians, of 10 experiments, for each family
Classification Performance - II
Similar for all operators Maybe a slight advantage, for the geometric
crossover if we have A highly conserved motif exist A “ballpark guess” on motif length
Surprisingly, mutation frequently outperforms the other operators
Concluding remarks
The geometric operator is promising - need work It is more length preserving than substring swap The geometric operator need a good guess on motif
length Edit move might not be optimal for motif discovery?
even though, it for some problems shows merit.
Our initial assumption imply an insertion/deletion equally often as replacement in sequence data we are WAY off on that parameter
Future Work
Synthetic data with known parameters Include character classes and within motif gaps in
representation Modules (composite motifs) Expand to position weight matrixes