Massively parallel read mapping on graphics cards Johannes K oster - - PowerPoint PPT Presentation

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Massively parallel read mapping on graphics cards

Johannes K¨

• ster

May 15, 2014

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Outline

1 Next-Generation-Sequencing of DNA 2 Read Mapping 3 Algorithm 4 Results

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Outline

1 Next-Generation-Sequencing of DNA 2 Read Mapping 3 Algorithm 4 Results

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Next-Generation-Sequencing

1 Chop DNA/RNA into small

fragments.

2 Ligate adapters to both ends. 3 Spread fragment solution across

a flowcell with beads.

4 Amplify fragments into clusters

(PCR).

5 Sequence fragments by adding

fluorescent complementary bases ◮ reads.

Illumina, 2013

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Outline

1 Next-Generation-Sequencing of DNA 2 Read Mapping 3 Algorithm 4 Results

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Read Mapping

For each read... find position in the known reference genome.

? ? ?

• A DNA sequence is a word over Σ = {A, C, G, T}.
• string matching, but with error tolerance

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Read Mapping

For each read... find position(s) with optimal alignment(s) to either strand of the reference: ACTGTGGACTATCAATGGAC GGTACTGT CTATCTATGGACCGTTAG

◮ Smith Waterman Algorithm

Too slow, therefore heuristics to find anchor positions:

• suffixarray/Burrows-Wheeler-Transformation (BWA, bowtie2)
• q-gram indices (RazerS3)

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Read mapping on GPUs

Challenges:

• limited and slow memory

q-gram index

• branching interrupts parallelism

BWT Idea:

• Use a special q-gram index with small memory footprint.
• Use parallelism to hide memory latency.
• Export branching into bitvector operations.

◮ PEANUT – the ParallEl AligNment UTility

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Outline

1 Next-Generation-Sequencing of DNA 2 Read Mapping 3 Algorithm 4 Results

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Algorithm

Main steps:

• Filtration

find potential hits between reads and reference using a special q-gram index

• Validation

validate hits using a bit-parallel alignment algorithm

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Algorithm

Main steps:

• Filtration

find potential hits between reads and reference using a special q-gram index

• Validation

validate hits using a bit-parallel alignment algorithm

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Q-Gram Index

For a given DNA sequence T:

• consider q-grams (substrings of length q)

GGTACTGACGTTCTATGGACCGTTAG

• encode them as integers

ACGT = 11 10 01 00 = 228

• array P with concatenation of q-gram positions
• array Q with address in P for each q-gram

◮ size 4q + |T|

P[Q[228]] . . . P[Q[229]]

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Q-Group Index

• assign each q-gram to a q-group

⌊g/w⌋

• store occurence of q-gram in a

bit-vector

• two address arrays guide from

q-group to positions of the q-gram in the text

◮ size 2/w ·4q +min{4q, |T|}+|T|

228 0 1 2 3 4 5 6 7... 1 1 228 / 32 228 % 32 found

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Q-Group Index

0000 0010 2 2 3 5 8 15 3 52 31 GAAA 1 11 17 308 22 0101 I S S' O

less memory, because we consider only. . .

• q-groups at the top level
• occuring q-grams at the bottom

calculate adress ranges in parallel by

• population counts
• prefix-sums

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Algorithm

Main steps:

• Filtration

find potential hits between reads and reference using a special q-gram index

• Validation

validate hits using a bit-parallel alignment algorithm

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Validation

T G T C T A T G T A +1 +1

Observations:

• calculating column j needs only column j − 1
• each transition changes edit distance by at most 1

Myers bit-parallel algorithm1:

• process graph column-wise
• maintain distance deltas in bitvectors

1 1 1 1 1 1 1 + << & ^

1Myers, 1999. J. ACM 46.

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Workflow

• load reads into buffer
• build q-group index of reads
• filtration of hits
• validation of hits
• postprocessing
• writing

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d r e a d s e q u e n c e s w r i t e h i t s start

• IO
• GPU
• CPU

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Outline

1 Next-Generation-Sequencing of DNA 2 Read Mapping 3 Algorithm 4 Results

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Results

100 200 300 400 500 600

block size

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

• ccupancy

filter_reference create_queries_index validate_hits

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Sensitivity

• assessed with Rabema2 benchmark on S. cerevisiae genome
• 100% for reads with error rate less than 7%
• 99.77% for error rates up to 10%
• 98.97% for error rates up to 20%

2Holtgrewe et al. 2011. BMC Bioinformatics

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Performance

Output types: all all alignments of a read best one of the best alignments best-stratum all best alignments 5 million simulated human reads:

mapper type time [min:sec]

• sens. [%]

PEANUT best-stratum 1:55 98.62 BWA-MEM best 3:16 96.99 Bowtie 2 best 5:21 96.85 PEANUT all 18:29 98.74 RazerS 3 all 199:55 98.83 Intel Core i7, 16GB RAM NVIDIA Geforce 780, 3GB RAM

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Performance

5 million real human exome reads:

mapper type time [min:sec] PEANUT best-stratum 1:33 BWA-MEM best 1:58 Bowtie 2 best 3:12 PEANUT all 10:52 RazerS 3 all 89:38 Intel Core i7, 16GB RAM NVIDIA Geforce 780, 3GB RAM

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Performance

10 million human exome paired end reads:

mapper type time [min:sec] PEANUT best-stratum 3:08 BWA-MEM best 4:44 Bowtie 2 best 8:18 PEANUT all 21:54 RazerS 3 all 150:59 Intel Core i7, 16GB RAM NVIDIA Geforce 780, 3GB RAM

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Summary

PEANUT is a GPU based read mapper that outperforms other state-of-the-art mappers in terms of

• sensitivity
• speed

by introducing the q-group index with small memory footprint and exploiting

• bit-vector operations
• prefix sums
• population counts

http://peanut.readthedocs.org