The role of high performance, grid and cloud computing in high-throughput sequencing

Lightbody, Gaye; Browne, Fiona; Zheng, Huiru; Haberland, Valeriia; Blayney, Jaine K.

doi:10.1109/bibm.2016.7822643

Cited by 3 publications

(2 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cloud computing cluster is similar to an HPC except, rather than connected via a local area network (LAN), computing nodes are connected remotely usually over the internet ( Jackson et al , 2010 ; Lightbody et al , 2019 ; Schatz, 2009 ). One of the significant advantages of cloud computing is that they are highly scalable, on-demand and without the barrier of the in-house deployment of fixed computational resources ( Lightbody et al ., 2016 ). As such, recent years has seen a surge in online vendors offering high-performance cloud computing as a service, providing accessibility of high-performance computing to researchers ( Jackson et al , 2010 ; Lightbody et al , 2019 ).…”

Section: Computational Challenges Of Short Read Alignmentmentioning

confidence: 99%

Hardware acceleration of genomics data analysis: challenges and opportunities

2021

View full text Add to dashboard Cite

The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimising memory latency and global memory access. This paper provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilising NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.

show abstract

Section: Computational Challenges Of Short Read Alignmentmentioning

confidence: 99%

Hardware acceleration of genomics data analysis: challenges and opportunities

2021

View full text Add to dashboard Cite

show abstract

“…Against this background, we consider common HTS bottlenecks that can be encountered at different workflow stages. We then present potential in silico and computational solutions, extending on the review in [17], and examine further rate-limiting issues that may in turn be raised. We therefore conclude with a discussion on the future role of HTS in facilitating biomedical research and its potential translation to clinical decision-making tools.…”

Section: Introductionmentioning

confidence: 99%

Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

Lightbody

Haberland

Browne

et al. 2019

Briefings in Bioinformatics

Self Cite

128

100

View full text Add to dashboard Cite

There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

show abstract

Custom Hardware Versus Cloud Computing in Big Data

Lightbody

Browne

Haberland

2017

Advanced Information and Knowledge Processing

Self Cite

View full text Add to dashboard Cite

The computational and data handling challenges in big data are immense yet a market is steadily growing traditionally supported by technologies such as Hadoop for management and processing of huge and unstructured datasets. With this ever increasing deluge of data we now need the algorithms, tools and computing infrastructure to handle the extremely computationally intense data analytics, looking for patterns and information pertinent to creating a market edge for a range of applications. Cloud computing has provided opportunities for scalable high-performance solutions without the initial outlay of developing and creating the core infrastructure. One vendor in particular, Amazon Web Services, has been leading this field. However, other solutions exist to take on the computational load of big data analytics. This chapter provides an overview of the extent of applications in which big data analytics is used. Then an overview is given of some of the high-performance computing options that are available, ranging from multiple Central Processing Unit (CPU) setups, Graphical Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and cloud solutions. The chapter concludes by looking at some of the state of the art solutions for deep learning platforms in which custom hardware such as FPGAs and Application Specific Integrated Circuits (ASICs) are used within a cloud platform for key computational bottlenecks.

show abstract

The role of high performance, grid and cloud computing in high-throughput sequencing

Cited by 3 publications

References 54 publications

Hardware acceleration of genomics data analysis: challenges and opportunities

Hardware acceleration of genomics data analysis: challenges and opportunities

Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

Custom Hardware Versus Cloud Computing in Big Data

Contact Info

Product

Resources

About