Sequence Alignment and Phylogenetic Prediction using Map Reduce Programming

Model in Hadoop DFS

Presented by

C. Geetha Jini (07MW03)

D. Komagal Meenakshi (07MW05)

Guided by

Dr. G. Sudha Sadasivam

Asst. Professor

Dept. of CSE

What is Sequence Alignment?

The procedure of comparing two or more sequences by searching for a series of individual characters or character patterns that are in the same order in the sequences.

Types of Sequence Alignment Pair-wise Alignment

Alignment of two sequencesGlobal –using Needleman Wunsch algorithm.

L G P S S K Q T G K G S _ S R A W D N | | | | | | |

L N _ A T K S A G K G A I M R L G D A

Local – using Smith Waterman algorithm._ _ _ _ _ _ _ _ _ T G K G _ _ _ _ _ _ _ _ _ _

| | |_ _ _ _ _ _ _ _ _ A G K G _ _ _ _ _ _ _ _ _ _

Multiple Sequence AlignmentAlignment of more than two sequences

Initialization

F(0, 0) = 0

F(0, i) = −i * d

F(j, 0) = −j* d

Main Iteration

For each i=1…M and j=1….N 

F(i-1,j-1+s(xi,yj), case 1 F(i,j) = max F(i-1,j)-d, case 2

F(I,j-1)-d, case 3 

DIAG, if case 1 Ptr(i,j) = UP, if case 2 LEFT, if case 3

Case 1: xi aligns to yi Case 2: xi aligns to gapCase 3: yi aligns to gap

NEEDLEMAN WUNSCH ALGORITHM

s(xi,yj ) = +1, match -1, mismatch

Needleman Wunsch Algorithm

A G T A

0 -1 -2 -3 -4

A -1 1 0 -1 -2

T -2 0 0 1 0

A -3 -1 -1 0 2

F(i,j) i=0 1 2 3 4

j=0

1

2

3

f(0,0)+s(1,1) =1F(1,1)=max f(0,1)-1 = -2 f(1,0)-1 = -2 = 1 (case 1)

OptimalAlignment A_TA AGTAScore:1+0+1+2 = 4

Case 1: xi aligns to yi Case 2: xi aligns to gapCase 3: yi aligns to gap

s(xi,yj ) = +1, match -1, mismatch d=1

PTR =DIAG, if case 1UP, if case 2LEFT, if case 3

f(0,1)+s(1,2) =-2f(0,2)-1 = -3f(1,1)-1 = 0Max = 0 (case 3)

Smith Waterman Algorithm

Initialization:F(0, j) = F(i, 0) = 0

Iteration: 0

F(i, j) = max F(i – 1, j – 1) + s(xi, yj), case 1

F(i – 1, j) – d, case 2 F(i, j – 1) – d, case 3

Smith Waterman Algorithm

A G T A

0 0 0 0 0

A 0 1 0 0 0

T 0 0 0 1 0

A 0 0 0 0 2

F(i,j) i=0 1 2 3 4

j=0

1

2

3

f(0,0)+s(1,1) =1F(1,1)=max f(0,1)-1 = -1

f(1,0)-1 = -1 0

= 1 (case 1)

OptimalAlignment A_TA _ _TAScore: 1+2 = 4

Case 1: xi aligns to yi Case 2: xi aligns to gapCase 3: yi aligns to gap

s(xi,yj ) = +1, match -1,mismatch d=1

PTR =DIAG, if case 1UP, if case 2LEFT, if case 3

f(0,2)+s(1,3) =-1F(1,3)=max f(0,3)-1 = -1

f(1,2)-1 = -10

= 0

Input: one query file and a set of sequence files

Put all files in DFS

Map

Reduce

Combine all the (K,V) pairs

Output: (Filename, Score)

Set File Name as KeyPass Entire File contents as Value

Do Sequence alignment of query file with the target files in DFS

Return (Filename as key, Score as Value).

Proposed system

A multiple sequence alignment is a sequence alignment of three or more biological sequences, generally protein, DNA, or RNA.

In general, the input is a set of query sequences that are assumed to have an evolutionary relationship by which they share a lineage and are descended from a common ancestor.

From the resulting multiple sequence alignment , phylogenetic analysis can be conducted to assess the sequences shared evolutionary origins.

Dynamic programming

Progressive alignment construction

Methods for producing MSA

most direct method for producing an MSA to identify the globally optimal alignment solution .

computational complexity◦ For n individual sequences, the naive method requires

constructing the n-dimensional equivalent of the matrix formed in standard pairwise sequence alignment.

◦ The search space thus increases exponentially with increasing n and is also strongly dependent on sequence length.

uses a heuristic search . builds up a final MSA by combining pair wise

alignments beginning with the most similar pair and progressing to the most distantly related.

The most popular progressive alignment method has been the ClustalW.

All progressive alignment methods require two stages: ◦ a first stage in which the relationships between the sequences

are represented as a tree, called a guide tree.◦ second step in which the MSA is built by adding the sequences

sequentially to the growing MSA according to the guide tree.

◦ first step: computation of guide tree from pair-wise alignment scores by an efficient clustering method such as neighbor-joining method.

◦ Second step: The two most similar sequences are aligned first, additional sequences (or groups of sequences) are added later following the guide tree

◦ requires a method to optimally align a sequence with an alignment or an alignment with an alignment

sequence 1sequence 2sequence 3Sequence4

Example: According to guide tree, alignfirst sequences 1 and 2, then align sequence 3 to alignment of sequence 1 and 2, then sequence 4 to alignment of sequences 1, 2, and 3.

Neighbor-joining is a bottom-up clustering method used for the construction of phylogenetic trees.

Neighbor-joining is an iterative algorithm. Each iteration consists of the following steps:

Based on the current distance matrix calculate the matrix Q .

For example, if we have four taxa (A, B, C, D) and the following distance matrix:

We obtain the following values for the Q matrix:

Find the pair of taxa in Q with the lowest value. Create a node on the tree that joins these two taxa (i.e. join the closest neighbors, as the algorithm name implies).

Calculate the distance of each of the taxa in the pair to this new node.

Calculate the distance of all taxa outside of this pair to the new node.

Start the algorithm again, considering the pair of joined neighbors as a single taxon and using the distances calculated in the previous step.

The primary problem is that when errors are made at any stage in growing the MSA, these errors are then propagated through to the final result.

Performance is also particularly bad when all of the sequences in the set are rather distantly related.

Phylogenetic Analysis

An investigation of evolutionary relationships among a group of related sequences by producing a tree representation of relationships.

Significant use-to make prediction concerning tree of life.

Structure

outer branches ->SequencesInner part -> Reflect the degree to which sequences are relatedAlike sequences -> located at neighboring outside branchesLess related sequences -> more distant from each other

Proposed System

Implementation of Sequence alignment and phylogenetic prediction using map-reduce programming model in hadoop

Algorithms used for AlignmentGlobal-Needleman Wunsch AlgorithmLocal-Smith Waterman Algorithm

Input: set of sequence files

Put all files in DFS

Map

Reduce

Combine all the (K,V) pairs

Output: (Filename, Score)

Phylogenetic Analysis

Set File Name as KeyPass Entire File contents as Value

Do Sequence alignment of all the files with all possible combinations and find the alignment scores

Return (Filename as key, Score as Value).

Proposed system

The mapreduce algorithm for pairwise sequence alignment both local and global was completed using the Needleman wunsch and Smith waterman algorithm in Hadoop.

This can be extended to do multiple sequence alignment and to perform phylogenetic analysis in Hadoop for predicting possible evolutionary relationships among a group of related sequences.

Bibliography

• David W. Mount, Bioinformatics Sequence and Genome Analysis, second edition

• http://apache.org/hadoop• http://wiki.apache.org/hadoop• Map reduce: Simplified data processing on Large Clusters, Jeffrey Dean and

Sanjay Ghemawat • www.biojava.org• www.biojava.org/wiki/Biojava:CookBook• Biojava in Anger, A Tutorial and Recipe for Those in a Hurry.

www.di.unito.it/~botta/didattica/biojavaHowTo.pdf• http://www-sop.inria.fr/oasis/Stages/04-05/BioProActive-Caromel.html• http://hpc.pnl.gov/projects/scalablast/• http://www.ebi.ac.uk/Tools/clustalw2/

Thank you