FPGA Acceleration of Sequence Analysis Tools in Bioinformatics

180 Pages·2014·1.95 MB·English

Boston University
College of Engineering

Dissertation 
FPGA Acceleration of Sequence Analysis Tools in
Bioinformatics

by 
Atabak Mahram

Submitted in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
2013
© Copyright by
Atabak Mahram
2013
Approved by

First Reader _______________________________________________________
Martin C. Herbordt, PhD
Professor of Electrical and Computer Engineering

Second Reader _______________________________________________________
Ayse Coskun , PhD
Professor of Electrical and Computer Engineering

Third Reader _______________________________________________________
Douglas Densmore , PhD
Professor of Electrical and Computer Engineering

Forth Reader _______________________________________________________
Allyn Hubbard , PhD
Professor of Electrical and Computer Engineering
Contents
1  Introduction .......................................................................................................................................... 1 
1.1  The Problem .................................................................................................................................. 1 
1.2  Sequence Analysis Algorithms ...................................................................................................... 4 
1.3  High‐Performance Computing with Accelerators ......................................................................... 5 
1.4  FPGA‐Based Accelerators .............................................................................................................. 7 
1.4.1  Programmability .................................................................................................................... 8 
1.4.2  FPGAs for High‐Performance Computing ........................................................................... 10 
1.5  High‐Performance Reconfigurable Computing for Sequence Analysis ....................................... 11 
1.6  Summary of Contributions .......................................................................................................... 12 
1.6.1  Acceleration of NCBI BLAST ................................................................................................ 13 
1.6.2  Acceleration of CLUSTALW ................................................................................................. 14 
1.7  Organization of the Rest of the Thesis ........................................................................................ 16 
2  High‐Performance Computing ............................................................................................................ 18 
2.1  Overview ..................................................................................................................................... 18 
2.2  Background ................................................................................................................................. 19 
2.3  Multicore Processors .................................................................................................................. 22 
2.4  GPU Computing ........................................................................................................................... 24 
2.5  FPGAs .......................................................................................................................................... 27 
2.6  FPGA‐Based Systems ................................................................................................................... 34 
2.6.1  Convey System .................................................................................................................... 34 
2.6.2  Gidel Board .......................................................................................................................... 36 
2.7  Summary ..................................................................................................................................... 37 
3  Sequence Analysis: Methods and Algorithms ..................................................................................... 38 
3.1  Overview ..................................................................................................................................... 38 
3.2  The Basic Biology of Cell ............................................................................................................. 39 
3.3  Fundamentals of Biosequence Analysis ...................................................................................... 40 
3.4  Scoring Models ............................................................................................................................ 42 
3.5  Pairwise Sequence Alignment with Dynamic Programming ....................................................... 44 
3.6  BLAST ........................................................................................................................................... 49 
3.6.1  Overview ............................................................................................................................. 49 
3.6.2  Word Matching ................................................................................................................... 50
3.6.3  Ungapped Extension ........................................................................................................... 51 
3.6.4  Gapped Extension ............................................................................................................... 51 
3.6.5  Statistical Evaluation in BLAST ............................................................................................ 52 
3.7  CLUSTAL‐W: Multiple‐Sequence Alignment ............................................................................... 53 
3.7.1  Dynamic Programming ....................................................................................................... 53 
3.7.2  Progressive Multiple‐Sequence Alignment: ClustalW ......................................................... 53 
4  Previous Attempts to Accelerate Sequence Analysis ......................................................................... 55 
4.1  Overview ..................................................................................................................................... 55 
4.2  Software Acceleration of Smith‐Waterman ................................................................................ 58 
4.3  Hardware Acceleration of Smith‐Waterman .............................................................................. 60 
4.4  Cluster Computing and NCBI BLAST ............................................................................................ 66 
4.5  GPU accelerated NCBI BLASTp .................................................................................................... 67 
4.6  FPGA Accelerators and NCBI BLASTp .......................................................................................... 68 
4.6.1  Tree BLAST ........................................................................................................................... 68 
4.6.2  Mercury BLASTp .................................................................................................................. 74 
4.7  Acceleration of Multiple Sequence Alignment ........................................................................... 79 
5  CAAD BLAST ........................................................................................................................................ 82 
5.1  Overview ..................................................................................................................................... 82 
5.2  Filter Basics ................................................................................................................................. 84 
5.3  Two‐Hit Filter .............................................................................................................................. 85 
5.4  EUA Filter .................................................................................................................................... 98 
5.4.1  Theoretical General Skipping .............................................................................................. 99 
5.4.2  Skip‐Fold Mechanism ........................................................................................................ 100 
5.4.3  Seed Lookup mechanism .................................................................................................. 102 
5.5  CAAD BLAST Architectures ........................................................................................................ 103 
5.6  Multiple Phase System on a Gidel Board .................................................................................. 104 
5.6.1  Results ............................................................................................................................... 107 
5.6.2  Scalability Analysis ............................................................................................................ 112 
5.6.3  Terminology ........................................................................... Error! Bookmark not defined. 
5.7  The Pipelined System on a Convey Machine ............................................................................ 124 
5.7.1  System Configuration and Operation ............................................................................... 125 
5.7.2  Pipelined Filters ................................................................................................................. 126
5.7.3  Jump FIFO Interface .......................................................................................................... 128 
5.7.4  Glue Logic Modules ........................................................................................................... 129 
5.7.5  RTL Optimizations ............................................................................................................. 130 
5.7.6  Replicating and Balancing the Components ..................................................................... 132 
5.7.7  Floor Planning ................................................................................................................... 134 
5.7.8  Integration and Results ..................................................................................................... 136 
6  CLUSTALW ......................................................................................................................................... 143 
6.1  Overview ................................................................................................................................... 143 
6.2  BACKGROUND ........................................................................................................................... 146 
6.2.1  Basics of MSA for Biological Sequences ............................................................................ 146 
6.2.2  CLUSTALW Overview ......................................................................................................... 148 
6.3  DESIGN AND IMPLEMENTATION ............................................................................................... 149 
6.3.1  Design Overview ............................................................................................................... 149 
6.3.2  FMSA Scoring .................................................................................................................... 150 
6.3.3  Filter Details ...................................................................................................................... 153 
6.3.4  RESULTS ............................................................................................................................. 154 
7  Conclusion and Future Work ............................................................................................................ 158 
7.1  Summary ................................................................................................................................... 159 
7.2  Future Directions ...................................................................................................................... 162 
8  References ........................................................................................................................................ 165
FPGA Acceleration of Sequence Analysis Tools in
Bioinformatics

ATABAK MAHRAM

Boston University, College of Engineering, 2013
Major Professor: Martin C. Herbordt, PhD,
Professor of Electrical and Computer Engineering

Abstract

With advances in biotechnology and computing power, biological data are being
produced at an exceptional rate. The purpose of this study is to analyze the application
of FPGAs to accelerate high impact production biosequence analysis tools. Compared
with other alternatives, FPGAs offer huge compute power, lower power consumption,
and reasonable flexibility.
BLAST has become the de facto standard in bioinformatic approximate string matching
and so its acceleration is of fundamental importance. It is a complex highly-optimized
system, consisting of tens of thousands of lines of code and a large number of
heuristics. Our idea is to emulate the main phases of its algorithm on FPGA. Utilizing
our FPGA engine, we quickly reduce the size of the database to a small fraction, and
then use the original code to process the query. Using a standard FPGA-based system,
we achieved 12x speedup over a highly optimized multithread reference code.
Multiple Sequence Alignment (MSA)--the extension of pairwise Sequence Alignment to
multiple Sequences--is critical to solve many biological problems. Previous attempts to
accelerate Clustal-W, the most commonly used MSA code, have directly mapped a
portion of the code to the FPGA. We use a new approach: we apply prefiltering of the
kind commonly used in BLAST to perform the initial all-pairs alignments. This results in
a speedup of from 80x to 190x over the CPU code (8 cores). The quality is comparable
to the original according to a commonly used benchmark suite evaluated with respect to
multiple distance metrics.
The challenge in FPGA-based acceleration is finding a suitable application mapping.
Unfortunately many software heuristics do not fall into this category and so other
methods must be applied. One is restructuring: an entirely new algorithm is applied.
Another is to analyze application utilization and develop accuracy/performance
tradeoffs. Using our prefiltering approach and novel FPGA programming models we
have achieved significant speedup over reference programs. We have applied
approximation, seeding, and filtering to this end. The bulk of this study is to introduce
the pros and cons of these acceleration models for biosequence analysis tools.
1 Introduction
1.1 The Problem

Bioinformatics refers to the analysis and management of scientific data and to the
development of tools and applications that help us organize, retrieve, and process
biological knowledge bases [Dur98][Jon04][Ewe05]. The application of mathematics and
computer science for the modeling of biological processes has been essential to the use
of biotic information for fundamental applications such as understanding life processes
and in high impact applied domains such as drug discovery [Ach07][Jon04].
The key insight in bioinformatics is that biologically significant polymers, such as
proteins and DNA, can be abstracted into character strings of a finite alphabet [Dur98].
Another fundamental observation is that all living cells pass a massive amount of
hereditary features onto their offspring through a process of replication and cell division
[Alb02]. In other words, nature adapts new sequences from pre-existing sequences.
This opens the door for understanding the functionality of newly discovered sequences:
by comparing a new sequence with known sequences, we can usually detect similarities
that will help us learn about the structure and infer the functionality of that sequence.
This mechanism allows biologists to use approximate string matching (AM) to
determine, for example, how a newly identified protein is related to previously analyzed
proteins, and how it has diverged through mutation [Mah10].
While AM is critical in diverse fields, e.g., text analysis, certain properties of biological
sequences have required creation of biology-specific algorithms. Here the canonical
AM task is Sequence Alignment (SA). For example, Hamming distance, the number of
differing characters, is one way to measure differences between two strings, but does
1
not tolerate insertions or deletions (indels). As discussed later, more generalized
scoring is necessary and is most often based on the probability of particular character
mutations and includes indels; it can be handled using dynamic programming (DP)
techniques. These have complexity O(mn) for two strings of size m and n, respectively.
With the exploding size of biological databases, however, DP algorithms have often
proven to be impractical. This has spawned heuristic O(n) algorithms, the most famous
and widely used of these is BLAST [Alt90].
Since the completion of the human genome project, the scientific community has seen a
sharp and rapid growth in the size of publicly available genomic and biotic information. 
Due to advances in technology and computing power, biological data are being
produced at an exponential rate; genomic databases now double in size every 15
months [Ben12a]. The complexity of bioinformatic tasks to which sequence alignment is
being applied is increasing just as dramatically. A typical query, say, of a protein with
respect to a database of all other known proteins, requires millions of pairwise SAs. In
Multiple Sequence Alignment (MSA), algorithms often begin with all-to-all pairwise
sequence alignment. And Phylogenetic Analysis can require millions of MSAs. As a
result, the development of faster SA tools and methods continues to be one of the
fundamental challenges in Computational Biology.
Since its invention, BLAST has been based on heuristics [Alt90][Tho94] and algorithmic
development remains an active area of research [Hen10][Hom09][Ken02]. On the other
hand, the acceleration and parallelization of these applications are as important as
algorithmic improvements. For example, the National Center for Biotechnology
Information(NCBI) maintains a BLAST server, that consists of thousands of nodes that
2