An Efficient Parallel Approachfor Identifying Protein Families from

Large-scale Metagenomics Data

Changjun Wu, Ananth Kalyanaraman

School of Electrical Engineering and Computer Science

Washington State University

Outline Problem Introduction Related Work Our Parallel Approach for Protein Family

Identification Experimental Results Conclusions & Future Work Acknowledgments

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Outline Problem Introduction Related Work Our Parallel Approach for Protein Family

Identification Experimental Results Conclusions & Future Work Acknowledgments

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Metagenomics Application of genomics techniques to the

study of microbial communities in their natural environments. Without isolation and lab cultivation of individual

species.

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Protein Family Identification Problem Motivation

Family identification Functional annotation Diversity of protein family universe

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………

family1 family2

knownproteins

newmetagenomic

proteins

familyi new protein family

functionalannotatio

n

What is a Protein Family? A protein family is a group of evolutionarily (thus

functionally) related proteins.

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sequence similarity domain similarity structure similarity

Outline Problem Introduction Related Work Our Parallel Approach for Protein Family

Identification Experimental Results Conclusions & Future Work Acknowledgments

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Related Work General approach

Perform all-against-all sequence comparison (BLAST)

Group proteins based on pair-wise similarity

Related work Kriventseva et al. (2001) Enright et al. (2002) Pipenbacher et al. (2002) Kelil et al. (2007) Yooseph et al. (2007) … 11/19/2008SC08, Austin, TX7

sequential

approach

GOS Approach Yooseph et al. (2007)

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……… ………

Redundancy removal

………

Graph generation Dense subgraph detection

1 2 3

Θ(n2) spaceΩ(n2) time

Limitations of Current Approaches Constructing large graphs can be time-

consuming ~106 CPU hours for ~28.6 million proteins – GOS

approach

Quadratic space requirement

Brute-force parallel approach

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Outline Problem Introduction Related Work Our Parallel Approach for Protein Family

Identification Experimental Results Conclusions & Future Work Acknowledgments

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Main Ideas of Our Approach Idea#1: A dense subgraph cannot span two

connected components

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DSCC

CC

CCDS

CCDS

use divide and conquer to drastically reduce problem size!

Challenge: find connected components without generating the whole graph

Main Ideas of Our Approach Idea#2: Exact-match based filtering technique

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100 bp

98% sequence similarity

>= 33 bp

eliminate unnecessary all-against-all comparisons!

Main Ideas of Our Approach Idea#3: High overlap of outlinks dense

subgraph

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……

u

v

v

u

web community

outlinks

use outlinks comparison to group vertices into dense subgraph!

Our Parallel Approach for Protein Family Identification

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connected componen

t detection

redundancy

removal

……

densesubgrap

hdetectio

n

input protein sequences

…

connected components

protein sequence pairwise sequence homology

……

densesubgraph

densesubgraph

bipartite graph

generation

4 3

21

Redundancy Removal Criteria

similarity of the match is >= 98% >= 95% of the shorter sequence is covered by the

match

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|||||| ||||||||||||||

>=95%

generalized suffix tree (GST)

p1 p2 p3 p4 p5

cut off>=98%

idea#2

Connected Component Detection

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M

GST1 GST2 GSTp

………

1) manage CC using union-find data structure2) distribute work in a load-balancing way

1) generate pairs2) sequence alignmentW W W

pairs

pairspairs

workwork

work

M – Master nodeW – Worker node

+ alignment results+ alignment results

+ alignment results

Bipartite Graph Generation

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…

connected component G(V,E)

B(V,V,E)

…

Dense Subgraph Detection Shingle algorithm

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outlinks(u)

s elems shingle shingle…………

permutation

permutation

s elems

comparison

c times

outlinks(v)

u

v

s, c: parameters

…… ……

Dense Subgraph Detection

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……

……

……

……

……

……

shingle

shingle

densesubgraph

densesubgraph

1 2 31st pass 2nd pass A~B

B(V, V, E) B(V, V, E) B(V, V, E)

AB

€

A∩ B

A∪B

Outline Problem Introduction Related Work Our Parallel Approach for Protein Family

Identification Experimental Results Conclusions & Future Work Acknowledgments

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Qualitative Validation with GOS Data

160k data set Our results vs. GOS results

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#inputseq #NR #CC #DS

mean degre

e

mean density

size of largest

DS

160,000 138,633

1,861

850 26 76% 13,263

22,186 21,348 1 134 20 78% 6,828

Precision Rate (PR) = 95.75% Sensitivity (SE) = 56.89%

Overlap Quality (OQ) = 55.49%

Drastical Work Reduction 40k input data

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~800 million

~8 million

all-against-allBLAST

our parallelapproach

#(sequence alignment work)

Run Time as Function of Input Size

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Performance Evaluation

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Conclusions & Future Work Presented a parallel approach for protein

family identification

Quality testing – better “benchmark” Parallelization of Shingle algorithm – potential

memory problem Large-scale application – 28.6 million

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Acknowledgments Prof. Srinivas Aluru at Iowa State University for

BlueGene/L access Anonymous reviewers Funding: Washington State University

Foundation and the Office of Research

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Thanks!Questions?

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