page: a framework for easy parallelization of genomic applications

PAGE: A Framework for Easy Parallelization of Genomic

Applications

1

Mucahid Kutlu Gagan AgrawalDepartment of Computer Science and Engineering

The Ohio State University

IPDPS 2014, Phoenix, Arizona

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Motivation

• The sequencing costs are decreasing

*Adapted from genome.gov/sequencingcosts

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• Big data problem– 1000 Human Genome Project already produced 200 TB data

– Parallel processing is inevitable!*Adapted from https://www.nlm.nih.gov/about/2015CJ.html

Motivation

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Typical Analysis on Genomic Data

• Single Nucleotide Polymorphism (SNP) calling

Sequences 1 2 3 4 5 6 7 8Read-1 A G C GRead-2 G C G GRead-3 G C G T ARead-4 C G T T C C

Alig

nmen

t File

-1

Reference A G C G T A C C

Sequences 1 2 3 4 5 6 7 8Read-1 A G A G

Read-2 A G A G T

Read-3 G A G T

Read-4 G T T C CAlig

nmen

t File

-2

*Adapted from Wikipedia

A single SNP may cause Mendelian disease!

✖ ✓✖

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Outline• Motivation• Existing Solutions for Implementation• Our Work• Experimental Evaluation• Conclusion

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Existing Solutions for Implementation

• Serial tools– SamTools, VCFTools, BedTools – File merging, sorting etc.– VarScan – SNP calling

• Parallel implementations– Turboblast, searching local alignments, – SEAL, read mapping and duplicate removal– Biodoop, statistical analysis

• Middleware Systems– Hadoop

• Not designed for specific needs of genetic data• Limited programmability

– Genome Analysis Tool Kit (GATK)• Designed for genetic data processing• Provides special data traversal patterns• Limited parallelization for some of its tools

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Outline• Motivation• Existing Solutions for Implementation• Our Work• Experimental Evaluation• Conclusion

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Our Goal• We want to develop a middleware system

– Specific for parallel genetic data processing– Allow parallelization of a variety of genetic algorithms– Be able to work with different popular genetic data

formats – Allows use of existing programs

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Challenges• Load Imbalance due to

nature of genomic data– It is not just an array of

A, G, C and T characters

• High overhead of tasks

• I/O contention

9

1 3 4

Coverage Variance

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Our Work• PAGE: A Map-Reduce-like middleware for easy

parallelization of genomic applications

• Mappers and reducers are executable programs– Allows us to exploit existing applications– No restriction on programming language

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File-mFile-2File-1

Map

Reduce

Region-1

MapRegion-n

Intra-dependent Processing

O-11

O-1n

Output-1

Map

Reduce

Region-1

MapRegion-n

O-m1

O-mn

Output-m

• Each file is processed independently

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Map O1

Ok

On

Reduce Output

Region-1

Input Files

MapRegion-k

Map

Region-n

Inter-dependent Processing• Each map task processes a particular region of ALL files

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What Can PAGE Parallelize?• PAGE can parallelize all applications that have the

following property• M - Map task• R, R1 and R2 are three regions such that

R = concatenation of R1 and R2

• M (R) = M(R1) M(R⊕ 2) where is the reduction ⊕function

R1 R2

R

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Data Partitioning• Data is NOT packaged into equal-size data blocks as in

Hadoop– Each application has a different way of reading the data– Equal-size data block packaging ignores nucleotide base

location information• Genome structure is divided into regions and each map

task is assigned for a region.– Takes account location information– The map task is responsible of accessing particular region of

the input files• It is a common feature for many genomic tools (GATK, SamTools)

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Genome Partition

• PAGE provides two data partitioning methods– By-locus partitioning: Chromosomes are divided into

regions

– By-chromosome partitioning: Chromosomes preserve their unity

Chr-1 Chr-2 Chr-3 Chr-4 Chr-5 Chr-6

Chr-1 Chr-2 Chr-3 Chr-4 Chr-5 Chr-6

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Task Scheduling

Static • Each processor is responsible of regions with equal length.• All map tasks should finish before the execution of reduce

tasks.

Dynamic• Map & reduce tasks are assigned by a master process• Reduce tasks can start if there are enough available

intermediate results.

PAGE provides two types of scheduling schemes.

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Applications Developed Using PAGE

• We parallelized 4 applications– VarScan: SNP detection– Realigner Target Creator: Detects insertion/deletions in

alignment files– Indel Realigner: Applies local realignment to improve

quality of alignment files– Unified Genotyper: SNP detection

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Sample Application Development with PAGE

• Serial execution command of VarScan Software– samtools mpileup –b file_list -f reference | java -jar VarScan.jar mpileup2snp

• To parallelize VarScan with PAGE, user needs to define:– Genome Partition: By-Locus– Scheduling Scheme: Dynamic (or Static)– Execution Model: Inter-dependent– Map command: samtools mpileup –b file_list -r regionloc -f

reference | java -jar VarScan.jar mpileup2snp >outputloc– Reduction : cat bash shell command

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Outline• Motivation• Existing Solutions for Implementation• Our Work• Experimental Evaluation• Conclusion

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Experiments• Experimental Setup

– In our cluster • Each node has 12 GB memory• 8 cores (2.53 GHz)

– We obtained the data from 1000 Human Genome Project– We evaluated PAGE with 4 applications– We compared PAGE with Hadoop Streaming and GATK

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Comparison with GATK

Scalability Data Size Impact

- Indel Realigner tool of GATK

Data Size: 11 GB # of cores: 128

3.3x

9x

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Comparison with GATK

Scalability Data Size Impact

- Unified Genotyper tool of GATK

10.9x 12.8x

Data Size: 34 GB # of cores: 128

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Scalability Data Size Impact

- VarScan Application

6.9x 12.7x

Comparison with Hadoop Streaming

Data Size: 52 GB # of cores: 128

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Summary of Experimental Results

When the computing power increased by 16 times

Indel Realigner

Unified Genotyper

VarScan Realigner Target Creator

PAGE 9x 12.8x 12.7x 14.1x

GATK 3.3x 10.9x - -

Hadoop Streaming

- - 6.9x -

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Conclusion• We developed a middleware

– Easily parallelizes genomic applications– High applicability

• No restriction on programming language or data format• Allows to use existing applications

– Provides user to control the parallel execution while hiding the details

• Alternative scheduling schemes, execution models and data partitioning types

– Good Scalability

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Thank you for listening …

Questions