FPGA Acceleration of DNA Sequencing Analysis and Storage

139 Pages·2017·1.16 MB·English

Imperial College London
Department of Computing
FPGA Acceleration of DNA Sequencing
Analysis and Storage
James Arram
July 2017
Submitted in part ful(cid:12)lment of the requirements for the degree of
Doctor of Philosophy in Computing of Imperial College London
and the Diploma of Imperial College London
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Abstract
In this work we explore how Field-Programmable Gate Arrays (FPGAs) can be
used to alleviate the data processing bottlenecks in DNA sequencing. We focus our
e(cid:11)orts on accelerating the FM-index, a data structure used to solve the computationally
intensivestringmatchingproblemsfoundinDNAsequencinganalysissuchasshortread
alignment. The main contributions of this work are:
1. We accelerate the FM-index using FPGAs and develop several novel methods for
reducing the memory bottleneck of the search algorithm. These methods include
customising the FM-index structure according to the memory architecture of the
FPGA platform and minimising the number of memory accesses through both
architectural and algorithmic optimisations.
2. We present a new approach for accelerating approximate string matching using
the backtracking FM-index. This approach makes use of specialised approximate
string matching modules and a run-time recon(cid:12)gurable architecture in order to
achieve both high sensitivity and high performance.
3. We extend the FM-index search algorithm for reference-based compression and
accelerate it using FPGAs. This accelerated design is integrated into fastqZip and
fastaZip, two new tools that we have developed for the fast and e(cid:11)ective compres-
sion of sequence data stored in the FASTQ and FASTA formats respectively.
We implement our designs on the Maxeler Max4 Platform and show that they are
able to outperform state-of-the-art DNA sequencing analysis software. For instance,
our hardware-accelerated compression tool for FASTQ data is able to achieve a higher
compression ratio than the best performing tool, fastqz, whilst the average compression
and decompression speeds are 25 and 43 times faster respectively.
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Declaration of Originality
I hereby declare that I am the sole author of this thesis and that the material within
hasnotbeensubmittedforadegreeinanyotheruniversityorinstitution. Thematerials
and information used or derived from other published sources has been clearly cited and
appropriately acknowledged.
3
Copyright Declaration
The copyright of this thesis rests with the author and is made available under a
Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers
are free to copy, distribute or transmit the thesis on the condition that they attribute it,
that they do not use it for commercial purposes and that they do not alter, transform
or build upon it. For any reuse or redistribution, researchers must make clear to others
the licence terms of this work
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Acknowledgements
First and foremost I would like to thank my supervisor Wayne Luk for all the advice
and encouragement he has given me over the past four years. His forming of the col-
laboration between myself and the Department of Chemical Pathology in the Chinese
UniversityofHongKong(CUHK)gavethisprojectrealpurposeandallowedmetowork
on truly exciting applications. From CUHK I would like to thank Peiyong Jiang, Rossa
Chiu and Dennis Lo for their advice on DNA methylation and for their hospitality when
I visited Hong Kong. I would like to thank my colleagues, in particular Paul Grigoras,
Xinyu Niu, Pavel Burovskiy and Kuen Hung Tsoi, for the technical support they have
given me throughout this project and for the many hours of co(cid:11)ee breaks shared. Last
but not least, I would like to thank my friends and family for (cid:12)lling my life with love
and support. Thank you all.
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Contents
1 Introduction 13
2 Background and related work 20
2.1 Indexed String Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.1 Notation and basic de(cid:12)nitions . . . . . . . . . . . . . . . . . . . . . 21
2.1.2 Su(cid:14)x Tries, Trees and Arrays . . . . . . . . . . . . . . . . . . . . . 21
2.1.3 Burrows-Wheeler Transform . . . . . . . . . . . . . . . . . . . . . . 24
2.1.4 FM-index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2 Application Acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.1 FPGA acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2.2 FPGA accelerator platforms . . . . . . . . . . . . . . . . . . . . . . 33
2.3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3 Exact string matching 40
3.1 Base design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.1.1 FM-index structure. . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.1.2 Hardware architecture . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.1.3 Performance model . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.2 Algorithmic optimisations . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.1 k-step FM-index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.2.2 Precomputed intervals . . . . . . . . . . . . . . . . . . . . . . . . . 55
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3.2.3 Oversampled Index . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.4 Optimisation procedure . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.1 Experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.3.2 Base design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.3 k-step FM-index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.3.4 Precomputed intervals . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.3.5 Oversampled index . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.3.6 Short read alignment . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4 Approximate string matching 72
4.1 Backtracking design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.1.1 Backtracking strategy . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.1.2 Hardware architecture . . . . . . . . . . . . . . . . . . . . . . . . . 78
4.1.3 Seed-and-compare . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2 Design-space exploration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.2.1 Static architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.2.2 Run-time recon(cid:12)gurable architecture . . . . . . . . . . . . . . . . . 88
4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.3.1 Experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.3.2 Backtracking design . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.3 Seed-and-compare . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.4 Short read alignment . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5 Sequence data compression 103
5.1 Reference-based compression . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.1.1 Algorithm overview . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.1.2 Hardware architecture . . . . . . . . . . . . . . . . . . . . . . . . . 107
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5.2 FASTQ and FASTA compression . . . . . . . . . . . . . . . . . . . . . . . 109
5.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.3.1 Experimental setup. . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.3.2 Reference-based compression . . . . . . . . . . . . . . . . . . . . . 117
5.3.3 FASTQ compression . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.3.4 FASTA compression . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
6 Conclusion 125
Bibliography 131
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List of Figures
1.1 Sequencing throughput of Illumina NGS platforms . . . . . . . . . . . . . 14
1.2 NGS data analysis pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1 Trie of the strings: B, AN, and ANA . . . . . . . . . . . . . . . . . . . . . 21
2.2 Su(cid:14)x trie of the text BANANA . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3 Su(cid:14)x tree of the text BANANA . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Su(cid:14)x array of the text BANANA . . . . . . . . . . . . . . . . . . . . . . . 24
2.5 Construction of the BWT . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.6 FM-index of the text BANANA . . . . . . . . . . . . . . . . . . . . . . . . 27
2.7 FM-index search algorithm example . . . . . . . . . . . . . . . . . . . . . 28
2.8 Maxeler MPC-X2000 architecture . . . . . . . . . . . . . . . . . . . . . . . 33
2.9 Benchmarking system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.1 Modi(cid:12)ed FM-index structure . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.2 The FM-index size and buckets size for the Human genome . . . . . . . . 44
3.3 Hardware achitecture for exact string matching . . . . . . . . . . . . . . . 46
3.4 Count operation steps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.5 Base design performance estimate for the Maxeler Max4 DFE . . . . . . . 50
3.6 Construction of k-step FM-index with step size of 2 . . . . . . . . . . . . 52
3.7 The k-step FM-index size and buckets size for the Human genome . . . . 53
3.8 Precomputed intervals optimisation . . . . . . . . . . . . . . . . . . . . . . 55
3.9 Oversampled FM-index construction . . . . . . . . . . . . . . . . . . . . . 57
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3.10 Optimisation procedure for the base design . . . . . . . . . . . . . . . . . 58
3.11 Performance estimates for algorithmic optimisations . . . . . . . . . . . . 59
3.12 Memory channel bandwidth for the Max4 DFE . . . . . . . . . . . . . . . 61
3.13 Performance of D1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.14 Strong-scaling and weak-scaling performance of D1 . . . . . . . . . . . . . 64
3.15 Performance of D2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.16 Performance of D3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.17 Performance of D4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.1 Approximate matching using greedy and exhaustive searching . . . . . . . 74
4.2 Search phases for approximate matching with one edit . . . . . . . . . . . 75
4.3 Search phases for approximate matching with two edits . . . . . . . . . . 76
4.4 Hardware architecture for approximate string matching . . . . . . . . . . 79
4.5 Seed-and-compare optimisation . . . . . . . . . . . . . . . . . . . . . . . . 82
4.6 Frequency distribution of CMLs . . . . . . . . . . . . . . . . . . . . . . . . 83
4.7 Static architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.8 Alignment breakdown for NGS dataset . . . . . . . . . . . . . . . . . . . . 87
4.9 Run-time recon(cid:12)gurable architecture . . . . . . . . . . . . . . . . . . . . . 89
4.10 Approximate matching performance . . . . . . . . . . . . . . . . . . . . . 94
4.11 Pipeline of modules for the seed-and-compare optimisation . . . . . . . . 95
4.12 Performance of seed-and-compare optimisation (one mismatch) . . . . . . 96
4.13 Performance of seed-and-compare optimisation (two mismatches) . . . . . 96
4.14 Pipeline of modules for up to two mismatches . . . . . . . . . . . . . . . . 98
5.1 Reference-based compression example . . . . . . . . . . . . . . . . . . . . 105
5.2 Hardware architecture for reference-based compression . . . . . . . . . . . 108
5.3 FASTQ format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.4 FASTA format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.5 Reverse complement optimisation for FASTQ (cid:12)les . . . . . . . . . . . . . 111
5.6 Merge tuples optimisation for FASTA (cid:12)les . . . . . . . . . . . . . . . . . . 112
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