DNA Barcode Data AnalysisBoosting Accuracy by Combining Simple

Classification Methods

CSE 377 – Bioinformatics - Spring 2006

Sotirios Kentros Univ. of Connecticut

Bogdan Paşaniuc

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

Specie S1 xd(x,S1)

Specie S2

d(x,S2) …

Specie Snd(x,Sn)

1. d(x,Si) = Minimum{ d(x,y) | sequence y belongs to specie Si }• Notation: Minimum “Method” Classifier

2. 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

Methods 

Percent missing from each specie(%)

10 20 30 40 50

Aminoacid Similarity

95.1 94.8 94.7 94.3 93

Min. Aminoacid Similarity

99.3 99.2 98.7 98.1 97.3

Hamming Dist. 97.9 97.4 96.7 96.5 96.5

Min. Hamming Dist.

98.8 98.2 97.5 97.1 96.4

Nucleotide Freq Sim.

56.2 49.6 44.2 44.6 38.2

Dinucleotide Freq. Dist.

44.9 42.2 41.6 41.5 39.3

Trinucleotide Freq. Dist

70.9 68.1 68 66.7 64.2

Combination 99.2 99.2 98.8 98.3 97.7

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

Methods 

Percent missing from each remaining specie(%)

0 10 20 30 40 50

Aminoacid Similarity 65.1 49.2 43.6 42.0 41.0 37.4

Min. Aminoacid Similarity 72.6 61.0 56.2 56.4 52.6 51.0

Hamming Dist. 55.0 91.4 90.2 90.4 88.0 88.6Min. Hamming

Dist. 73.1 85.4 79.6 78.6 75.0 74.4

Dinucleotide Freq. Dist. 51.0 50.4 48.2 48.2 45.2 43.4

Trinucleotide Freq. Dist 56.5 63.6 61.8 63.0 59.2 57.4

Nucleotide Freq Sim. 73.0 56.2 49.6 44.2 44.0 38.2

Combination 80.5 93.2 91.6 91.6 88.4 88.6

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.