DNA Barcode Data Analysis Boosting Accuracy by Combining Simple Classification Methods

1. DNA Barcode Data AnalysisBoosting Accuracy by Combining Simple Classification Methods CSE 377 – Bioinformatics - Spring 2006

2. Outline • Motivation • Problem Definition • The Methods • Hamming Distance and Minimum Hamming Distance • Aminoacid Similarity and Minimum Aminoacid Similarity • Dinucleotide Distance • Trinucleotide Distance • Nucleotide Frequency Similarity • Combining the Methods • Results • Specie Classification • New Specie Recognition • Conclusion • Future Work

3. Motivation • “DNA barcoding” was proposed as a tool for differentiating biological species • Goal: To make a “finger print” for species, using a short sequence of DNA • Assumption: mitochondrial DNA evolve at a lower rate than regular DNA • Mitochondrial DNA: High interspecie variability while retaining low intraspecie sequence variability • Choice was cytochrome c oxidase subunit 1 mitochondrial region ("COI", 648 base pairs long).

4. Problem definition The scope of our project was to explore if by combining simple classification methods one can increase the classification accuracy. We address two problems: • Classification of individuals given a training set of species. • Identification of individuals that belong in new species. All the sequences are aligned

5. Problem definition • Specie differentiation: • INPUT: a set S of aligned DNA sequences for which the specie is known and x a new sequence • OUTPUT: find the specie of x, given that there are sequences in S that have the same specie as x

6. Problem definition • Specie differentiation&New Specie Determination: • INPUT: a set S of aligned DNA sequences for which the specie is known and x a new sequence • OUTPUT: find the specie of x, if there is at least a sequence in S with the same specie or determine if it is a new specie.

7. Methods Used • Hamming Distance and Minimum Hamming Distance • Aminoacid Similarity and Minimum Aminoacid Similarity • Dinucleotide Distance • Trinucleotide Distance • Nucleotide Frequency Similarity

8. Methods d(x,S1) d(x,Sn) Specie Sn Specie S1 x d(x,S2) … Specie S2 • d(x,Si) = Minimum{ d(x,y) | sequence y belongs to specie Si } • Notation: Minimum “Method” Classifier • d(x,Si) = Average{ d(x,y) | sequence y belongs to specie Si } • Notation: “Method” Classifier

9. Hamming Distance • Average: • Given new sequence x find specie S such that the minimum hamming distances on the average from x to y (y in S) is minimized • Assign to S to y • Minimum • Given new sequence x find y such that the minimum hamming distance from x to y is minimized • Assign specie(y) to x

10. Aminoacid Similarity • Genetic code: • rules that map DNA sequences to proteins • Codon: tri-nucleotide unit that encodes for one aminoacid • Divide DNA seq. into codons and substitute each one by its corresp. aminoacid • Blosum62 (BLOck SUbstitution Matrix) • 20x20 matrix that gives score for each two aminoacids based on aminoacid properties • The higher the score the more likely no functional change in the protein

11. Aminoacid Similarity • Distance(x,y) • DNA sequences x, y ->Aminoacid sequences x’ , y’ (using codon to aminoacid transf.) • Using the Blosum aminoacid substitution matrix get the score of the alignment • Average: • Find the specie with maximum average similarity • Minimum: • Find the sequence with max. similarity

12. Dinucleotide Distance • For each specie find the frequency with which each Dinucleotide appears. • Compute the frequency of each Dinucleotide in the unclassified sequence. • Find the specie with the minimum Mean Square distance to the new unclassified sequence • For New Species, after classifying the individual find the Average Intraspecie Mean Square distance for the candidate specie. If the individual is close enough, assign him at the specie, otherwise he belongs in a New Specie. in/dels are ignored

13. Trinucleotide Distance • For each specie find the frequency with which each Trinucleotide appears. • Compute the frequency of Trinucleotide appearance of the unclassified sequence. • Find the specie with the minimum Mean Square distance to the new unclassified sequence • For New Species, after classifying the individual find the Average Intraspecie Mean Square distance for the candidate specie. If the individual is close enough, assign him at the specie, otherwise he belongs in a New Specie. in/dels are ignored

14. Nucleotide Frequency Similarity • For each position in the DNA find the frequency with which the Nucleotides appear in the specie individuals. We include the frequency of in/dels appearing. • For unclassified individuals compute the log of the probability that the individual belongs to the specie and assign it to the specie for which the probability is maximum. • For new species, we compute the minimum probability for the individuals belonging in the specie and compare it with the one of the candidate specie in order to determine whether it belongs to the specie or not.

15. Combining the Methods • The specie on which most classifiers agreed is returned • Simple Voting: • Every classifier’s returned specie has a weight of 1 • Output the specie with the most votes • Weighted Voting • Every classifier has a different weight based on the accuracy of each independent method • Output the specie with largest total • As expected weighted voting yields higher accuracy and thus in our results the combined method uses weighted voting

16. Datasets(1) • We used the dataset provided at http://dimacs.rutgers.edu/workshops/BarcodeResearchchallanges consisting of 1623 aligned sequences classified into 150 species with each sequence consisting of 590 nucleotides on the average. • We randomly deleted from each specie 10 to 50 percent of the sequences • Deleted seq -> test • Remaining seq -> train • We made sure that in every specie has a least one sequence

17. Specie Recovering Accuracy(in %)(no new specie)

18. Datasets(2) • In order to test the accuracy of new specie detection and classification we devised a regular leave one out procedure. • delete a whole specie • randomly delete from each remaining specie 0 to 50 percent of the sequences • Deleted seq -> test • Remaining seq -> train • The following table gives accuracy results on average for 150x6 different testcases

19. Leave one out Accuracy(in %)

20. Conclusions(1) • Every method show a tradeoff between new specie detection and classification accuracy • Hamming distance performs very good when no new species are present but the accuracy results are low for new specie detection • The combined method yields better accuracy results both on new specie detection and seq. classification. • The runtime of all methods is within same order of magnitude

21. Conclusions(2) • By combining simple classification methods, we managed to boost the accuracy both for classifying individuals in known species and for detecting new species • As expected the results imply a tradeoff between classification and new specie detection • the higher the classification accuracy the lower the detection • Hamming Distance is a very good metric for the training dataset provided

22. Future Work • New specie clustering: determining the different new species present • Further investigate threshold selection and weighting schemes. • Possible ignoring parts of the given sequences could improve accuracy. Are there redundant/noisy regions? • Use independent weighting schemes for new specie detection and classification into known species.

23. Questions Thank you.