1. A Hybrid Algorithm for Multiple DNA Sequence Alignment
9/18/2018
A Hybrid Algorithm for Multiple DNA Sequence Alignment
Dr. C. Thusangi Wannige and G. Kokila K. Perera
Department of Computer Science,
University of Ruhuna,
Sri Lanka.

2. AGENDA Introduction Problem Definition Algorithm Evaluation
9/18/2018
AGENDA
Introduction
Problem Definition
Algorithm
Evaluation
Conclusion & Future Improvements

3. 9/18/2018
Introduction
Bioinformatics is defined as the application of computational techniques to organize and understand the information associated with biomolecules
(N. M. Luscombe et al, 2001)

4. Center Star Method for Multiple Sequence Alignment
9/18/2018
Center Star Method for Multiple Sequence Alignment
Multiple Sequence Alignment: Infer the homologous regions within the bimolecular sequences
Step 1: Identifying the center sequence (Sc)
Step 2: Merge the pairwise alignments between the center sequence and the remaining sequences Sc S1 S2 S3
...
Si (i≠c) Sn
Star Tree
Pairwise Alignments
Sum up Gaps in Sc
S’c1
S’c2
S’c3 …
Sci (i≠c)
S’c(n-1)
S’c
S’1
S’2
S’3
S’n
Update
Alignment

5. 9/18/2018
Research Problem

6. Research Problem Existence of more mutually related sequences
9/18/2018
Research Problem
Existence of more mutually related sequences
When the sequences exist as subsets of mutually related sequences, the sequences in the subset have partially conserved regions among them
When Center Star method is used in these scenarios, the partially conserved regions are hidden in the alignment
Subset of
sequence
Sequence

7. 9/18/2018
Algorithm
The main output of this research is a more accurate algorithm for MSA

8. Step 1: Identify subsets of sequences
9/18/2018
Step 1: Identify subsets of sequences
Quantify distance between sequences: K-mers
Use statistics of k length substrings in the sequences to measure the distance between them
Clustering method: Bisecting K-means Algorithms
A divisive hierarchical clustering method
Initial cluster centroids : the two sequences with minimum similarity
Terminating criteria : intra-cluster similarity > threshold (0.25)

9. Step 2: Obtain alignments in each subset
9/18/2018
Step 2: Obtain alignments in each subset
The sequences within the each subsets are aligned separately using the Center Star method
Step 3: Merge the alignments form each subset
Align pairs of subsets together using profile alignment
Measure distance between 2 subsets using the alignment
Apply UPGMA - hierarchical clustering method to build a guide tree that represent relationships between the subset
Align the output of the Step 2 for each cluster according to the order of guide tree

10. 9/18/2018
Evaluation
A comparative approach is used for evaluation

11. Evaluation Data set: Method
9/18/2018
Evaluation
Data set:
13 sequences corresponding to envelope glycoprotein (env) gene, V3 region of the HIV-1 genome (Accession no: U U68508)
The transmission history of the patients from whom these sequences are obtained is known (Leitner T, Escanilla D, Franzén C, et al.)
Method
Alignment output of the MSA algorithm is used for a phylogenetic analysis
Resulting phylogeny is compared with the true transmission history using Split Distance measure

12. Evaluation Results Algorithm Split Distance New algorithm 0.3
9/18/2018
Evaluation Results
Algorithm
Split Distance
New algorithm
0.3
Center Star
0.4
HAlign
ClustalW2
0.5
MUSCLE
MAFFT

13. From Alignment of the New Algorithm
9/18/2018
Evaluation Results
Actual Phylogeny
From Alignment of the New Algorithm
Split Distance = 0.3

14. 9/18/2018
Conclusion
The new algorithm has the main focus on preserving the partially conserved regions in the sequences.
Alignment reflect the relationships between sequences more accurately
The algorithm is more useful for phylogenetic studies
We can improve the algorithm to use lesser the memory and processor requirements
This algorithm employs dynamic programming in several occasions.

15. Questions and Suggestions
9/18/2018
Questions and Suggestions