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Author: Nathaniel McVicar Publisher: ISBN: Category : Languages : en Pages :
Book Description
Advances in next-generation sequencing technology have led to increases in genomic data production by sequencing machines that outpace Moore’s law. This trend has reached the point where the time and money spent processing human and other genome sequence data could be greater than that spent producing it. Field-Programmable Gate Arrays (FPGAs) can provide a solution to this problem. Bioinformatics accelerators running on FPGAs achieve order of magnitude speedups across a variety of genomics applications important in both biological research and clinical medicine. This dissertation presents three accelerators. The first addresses the short read alignment problem, where millions of short DNA or RNA reads, with lengths on the order of 100 base pairs, are aligned to an index built from a reference genome. Our aligner combines an FPGA accelerator with the greater memory bandwidth of our host system to produce a fast and flexible short read aligner. Using this aligner, we developed a classifier to determine which of two possible species each read originated from. In a case study with RNA-Seq reads from mouse and human retinal cultures our aligner produced more accurate classification results and better performance than software-based aligners. Our second accelerator tackles the problem of non-coding RNA (ncRNA) homology search. The biologically important functions these ncRNAs perform are determined by their two- or three-dimensional structure, and ncRNAs with different sequences can perform the same functions if they share a similar structure. Homology search scores sequences using models of ncRNA families in an effort to find previously unknown members. Our accelerator greatly improves the speed of filters that identify candidate sequences using the Viterbi and CYK algorithms. The final accelerator uses the Hybrid Memory Cube (HMC), a stacked DRAM, for K-mer counting. In many areas of bioinformatics, including de novo assembly, K-mer counting is a filter with important roles including removing read errors. Our approach stores K-mer counts in a Bloom filter on the HMC leveraging the greater random access rate for increased performance over both the host and FPGA-attached DRAM. Throughout this dissertation we demonstrate that FPGA accelerators can achieve excellent speedups across a variety of genomics applications.
Author: Nathaniel McVicar Publisher: ISBN: Category : Languages : en Pages :
Book Description
Advances in next-generation sequencing technology have led to increases in genomic data production by sequencing machines that outpace Moore’s law. This trend has reached the point where the time and money spent processing human and other genome sequence data could be greater than that spent producing it. Field-Programmable Gate Arrays (FPGAs) can provide a solution to this problem. Bioinformatics accelerators running on FPGAs achieve order of magnitude speedups across a variety of genomics applications important in both biological research and clinical medicine. This dissertation presents three accelerators. The first addresses the short read alignment problem, where millions of short DNA or RNA reads, with lengths on the order of 100 base pairs, are aligned to an index built from a reference genome. Our aligner combines an FPGA accelerator with the greater memory bandwidth of our host system to produce a fast and flexible short read aligner. Using this aligner, we developed a classifier to determine which of two possible species each read originated from. In a case study with RNA-Seq reads from mouse and human retinal cultures our aligner produced more accurate classification results and better performance than software-based aligners. Our second accelerator tackles the problem of non-coding RNA (ncRNA) homology search. The biologically important functions these ncRNAs perform are determined by their two- or three-dimensional structure, and ncRNAs with different sequences can perform the same functions if they share a similar structure. Homology search scores sequences using models of ncRNA families in an effort to find previously unknown members. Our accelerator greatly improves the speed of filters that identify candidate sequences using the Viterbi and CYK algorithms. The final accelerator uses the Hybrid Memory Cube (HMC), a stacked DRAM, for K-mer counting. In many areas of bioinformatics, including de novo assembly, K-mer counting is a filter with important roles including removing read errors. Our approach stores K-mer counts in a Bloom filter on the HMC leveraging the greater random access rate for increased performance over both the host and FPGA-attached DRAM. Throughout this dissertation we demonstrate that FPGA accelerators can achieve excellent speedups across a variety of genomics applications.
Author: B. Sharat Chandra Varma Publisher: Springer ISBN: 9811005915 Category : Technology & Engineering Languages : en Pages : 133
Book Description
This book presents an evaluation methodology to design future FPGA fabrics incorporating hard embedded blocks (HEBs) to accelerate applications. This methodology will be useful for selection of blocks to be embedded into the fabric and for evaluating the performance gain that can be achieved by such an embedding. The authors illustrate the use of their methodology by studying the impact of HEBs on two important bioinformatics applications: protein docking and genome assembly. The book also explains how the respective HEBs are designed and how hardware implementation of the application is done using these HEBs. It shows that significant speedups can be achieved over pure software implementations by using such FPGA-based accelerators. The methodology presented in this book may also be used for designing HEBs for accelerating software implementations in other domains besides bioinformatics. This book will prove useful to students, researchers, and practicing engineers alike.
Author: Harisankar Sadasivan Publisher: Harisankar Sadasivan ISBN: Category : Computers Languages : en Pages : 132
Book Description
The MinION is a revolutionary handheld DNA sequencer that is inexpensive, portable, and can perform real-time sequencing. MinION is increasingly used in Precision Medicine applications. However, the MinION lacks portable compute power. This thesis introduces two clinical applications of the MinION and identifies and solves performance bottlenecks through hardware and software solutions to enable portable microbial diagnostics. Finally, we discuss how our accelerated solutions will fit on a laptop with a GPU. More than 99% of DNA fragments in a typical human sample are non-target (human), which may be skipped in real-time using the MinION’s Read Until feature. We analyze the performance of the Read Until pipeline in detecting target microbial species for targeted viral pathogen detection and microbiome abundance estimation. We find new sources of performance bottlenecks (basecaller in the classification of a fragment) that are not addressed by past genomics accelerators. While SquiggleFilter and DTWax are our solutions for targeted viral pathogen detection, RawMap is for untargeted microbiome analysis. We also discuss accelerating the bottleneck step in the DNA mapping software (Minimap2) used in all of MinION’s sequencing workflows. For targeted virus detection, we discuss SquiggleFilter which is a portable and programmable hardware-software solution that directly analyzes MinION’s raw squiggles and filters everything except target viral DNA fragments. SquiggleFilter avoids the expensive basecalling step and uses hardware accelerated subsequence Dynamic Time Warping (sDTW). We show that our 14.3W 13.25 sq. mm accelerator has 274× greater throughput and 3481× lower latency than existing GPU-based solutions while consuming half the power. DTWax overcomes the on-chip memory limitations of SquiggleFilter by optimizing its high-accuracy sDTW algorithm for GPUs resulting in a ∼1.92X sequencing speedup and ∼3.64X compute speedup: costup. For the untargeted classification and analysis of microbiome, we discuss RawMap which is a machine-learning-based smart and efficient solution that reduces sequencing time and cost by ∼24% and computing cost by ∼22%. We also discuss how RawMap may be used as an alternative to the RT-PCR test for viral load quantification of SARS-CoV-2. Minimap2 is a software used in all MinION workflows. minimap2-accelerated (mm2-ax) is a heterogeneous design for sequence mapping where minimap2’s bottleneck step is sped up on the GPU with bit-exact output. mm2-ax on an NVIDIA A100 GPU improves the bottleneck step (chaining) with 4.07 - 1.93X speedup: costup over a SIMD baseline. Finally, we envision a portable solution to microbial diagnostics with a laptop connected to the MinION. DTWax can perform targeted virus detection on the GPU and RawMap does untargeted microbiome classification on the CPU. Post-sequencing tasks like basecalling, alignment (using mm2-ax) and variant calling use the GPU.
Author: Wim Vanderbauwhede Publisher: Springer Science & Business Media ISBN: 1461417910 Category : Technology & Engineering Languages : en Pages : 798
Book Description
High-Performance Computing using FPGA covers the area of high performance reconfigurable computing (HPRC). This book provides an overview of architectures, tools and applications for High-Performance Reconfigurable Computing (HPRC). FPGAs offer very high I/O bandwidth and fine-grained, custom and flexible parallelism and with the ever-increasing computational needs coupled with the frequency/power wall, the increasing maturity and capabilities of FPGAs, and the advent of multicore processors which has caused the acceptance of parallel computational models. The Part on architectures will introduce different FPGA-based HPC platforms: attached co-processor HPRC architectures such as the CHREC’s Novo-G and EPCC’s Maxwell systems; tightly coupled HRPC architectures, e.g. the Convey hybrid-core computer; reconfigurably networked HPRC architectures, e.g. the QPACE system, and standalone HPRC architectures such as EPFL’s CONFETTI system. The Part on Tools will focus on high-level programming approaches for HPRC, with chapters on C-to-Gate tools (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical tools (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for example OpenCL, Microsoft’s Kiwi and Alchemy projects). The part on Applications will present case from several application domains where HPRC has been used successfully, such as Bioinformatics and Computational Biology; Financial Computing; Stencil computations; Information retrieval; Lattice QCD; Astrophysics simulations; Weather and climate modeling.
Author: Horacio Pérez-Sánchez Publisher: BoD – Books on Demand ISBN: 9535108786 Category : Computers Languages : en Pages : 340
Book Description
This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here.
Author: Martin Gollery Publisher: CRC Press ISBN: 1420011804 Category : Computers Languages : en Pages : 178
Book Description
Demonstrating that many useful resources, such as databases, can benefit most bioinformatics projects, the Handbook of Hidden Markov Models in Bioinformatics focuses on how to choose and use various methods and programs available for hidden Markov models (HMMs). The book begins with discussions on key HMM and related profile methods, incl
Author: Bertil Schmidt Publisher: CRC Press ISBN: 1439814899 Category : Computers Languages : en Pages : 370
Book Description
New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological
Author: Naiara Rodríguez-Ezpeleta Publisher: Springer Science & Business Media ISBN: 1461407826 Category : Science Languages : en Pages : 258
Book Description
Next generation sequencing is revolutionizing molecular biology. Owing to this new technology it is now possible to carry out a panoply of experiments at an unprecedented low cost and high speed. These go from sequencing whole genomes, transcriptomes and small non-coding RNAs to description of methylated regions, identification protein – DNA interaction sites and detection of structural variation. The generation of gigabases of sequence information for each of this huge bandwidth of applications in just a few days makes the development of bioinformatics applications for next generation sequencing data analysis as urgent as challenging.
Author: Bertil Schmidt Publisher: CRC Press ISBN: 1439858365 Category : Computers Languages : en Pages : 425
Book Description
New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological
Author: Nathaniel McVicar
Author: Nathaniel McVicar
Author: B. Sharat Chandra Varma
Author: Harisankar Sadasivan
Author: Wim Vanderbauwhede
Author: 
Author: Horacio Pérez-Sánchez
Author: Martin Gollery
Author: Bertil Schmidt
Author: Naiara Rodríguez-Ezpeleta
Author: Bertil Schmidt