AF: Medium: Parallel Algorithms and Software for High-Throughput Sequence Assembly
AF:中:用于高通量序列组装的并行算法和软件
基本信息
- 批准号:1360593
- 负责人:
- 金额:$ 92.52万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-08-12 至 2018-04-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
High-throughput next-generation DNA sequencing technologies (NGS) are causing a major revolution in life sciences research by allowing rapid and cost-effective sampling of genomes and transcriptomes (expressed genomic sequences). Assembly of genomes and transcriptomes from billions of such randomly sampled sequences is an important problem in computational biology. While significant strides have been made, much work remains in addressing the diverse and rapidly emerging platforms, improving assembly quality, and scaling to both large-scale data sizes and large genomes. This project will harness the power of high performance computing to develop effective solutions for sequence assembly. It will lead to the development of scalable, efficient parallel algorithms and a parallel integrated software framework for genome and transcriptome assembly. The project seeks to advance the state of the art by targeting important unsolved problems such as hybrid assembly of sequences from multiple NGS platforms, making fundamental algorithmic advances to improve assembly quality, and conducting an in-depth effort at parallel algorithms development for the entire gamut of problems that arise in connection with assembly. It will be carried out by an interdisciplinary team of investigators, in partnership with leading NGS manufacturers and academicians involved in large plant genome sequencing projects. The project will lead to the release of a scalable parallel software package for sequence assembly that will be made available to the scientific community. Postdoctoral and graduate students will be trained in computer science driven interdisciplinary research and in writing efficient high performance computing software. The project will influence curriculum development and will lead to educational materials in bioinformatics for next-generation sequencing.
高通量下一代DNA测序技术(NGS)通过允许快速且具有成本效益的基因组和转录组(表达的基因组序列)采样,正在引发生命科学研究的重大革命。从数十亿个这样的随机采样序列中组装基因组和转录组是计算生物学中的一个重要问题。虽然已经取得了重大进展,但在解决多样化和快速新兴的平台,提高组装质量以及扩展到大规模数据大小和大基因组方面仍有许多工作要做。这个项目将利用高性能计算的力量来开发有效的序列组装解决方案。它将导致可扩展的,高效的并行算法和基因组和转录组组装的并行集成软件框架的发展。该项目旨在通过针对重要的未解决的问题(如来自多个NGS平台的序列的混合组装)来推进最先进的技术水平,进行基本的算法进步以提高组装质量,并对与组装有关的所有问题进行深入的并行算法开发。它将由一个跨学科的研究人员团队与领先的NGS制造商和参与大型植物基因组测序项目的学者合作进行。该项目将导致一个可扩展的并行软件包的序列组装,将提供给科学界。博士后和研究生将接受计算机科学驱动的跨学科研究和编写高效高性能计算软件的培训。该项目将影响课程的编制,并将导致下一代测序的生物信息学教材。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Srinivas Aluru其他文献
Reply to: “Re-evaluating the evidence for a universal genetic boundary among microbial species”
回复:“重新评估微生物物种间通用遗传边界的证据”
- DOI:
10.1038/s41467-021-24129-1 - 发表时间:
2021-07-07 - 期刊:
- 影响因子:15.700
- 作者:
Luis M. Rodriguez-R;Chirag Jain;Roth E. Conrad;Srinivas Aluru;Konstantinos T. Konstantinidis - 通讯作者:
Konstantinos T. Konstantinidis
Distribution-Independent Hierarchical Algorithms for the N-body Problem
- DOI:
10.1023/a:1008047806690 - 发表时间:
1998-01-01 - 期刊:
- 影响因子:2.700
- 作者:
Srinivas Aluru;John Gustafson;G.M. Prabhu;Fatih E. Sevilgen - 通讯作者:
Fatih E. Sevilgen
A Parallel Monte Carlo Algorithm for Protein Accessible Surface Area Computation
蛋白质可及表面积计算的并行蒙特卡罗算法
- DOI:
10.1007/978-3-540-46642-0_49 - 发表时间:
1999 - 期刊:
- 影响因子:0
- 作者:
Srinivas Aluru;D. Ranjan;N. Futamura - 通讯作者:
N. Futamura
Srinivas Aluru的其他文献
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{{ truncateString('Srinivas Aluru', 18)}}的其他基金
A scalable integrated multi-modal single cell analysis framework for gene regulatory and cell-cell interaction networks
用于基因调控和细胞间相互作用网络的可扩展集成多模式单细胞分析框架
- 批准号:
2233887 - 财政年份:2023
- 资助金额:
$ 92.52万 - 项目类别:
Continuing Grant
BD Hubs: Collaborative Proposal: SOUTH:The South Big Data Innovation Hub
BD Hubs:合作提案:SOUTH:南方大数据创新中心
- 批准号:
1916589 - 财政年份:2019
- 资助金额:
$ 92.52万 - 项目类别:
Cooperative Agreement
AF: Small: Algorithmic Techniques for High-throughput Analysis of Long Reads
AF:小:长读长高通量分析的算法技术
- 批准号:
1816027 - 财政年份:2018
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
EAGER: A Framework for Learning Graph Algorithms with Applications to Social and Gene Networks
EAGER:学习图算法及其在社交和基因网络中的应用的框架
- 批准号:
1841351 - 财政年份:2018
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
MRI: Acquisition of an HPC System for Data-Driven Discovery in Computational Astrophysics, Biology, Chemistry, and Materials Science
MRI:获取 HPC 系统,用于计算天体物理学、生物学、化学和材料科学中的数据驱动发现
- 批准号:
1828187 - 财政年份:2018
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
Big Data Regional Innovation Hubs and Spokes Workshop
大数据区域创新中心和辐射研讨会
- 批准号:
1736154 - 财政年份:2017
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
SHF:Small: Reproducibility and Comprehensive Assessment of Next Generation Sequencing Bioinformatics Software
SHF:Small:下一代测序生物信息学软件的重现性和综合评估
- 批准号:
1718479 - 财政年份:2017
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
AF: Medium: Collaborative Research: Sequential and Parallel Algorithms for Approximate Sequence Matching with Applications to Computational Biology
AF:媒介:协作研究:近似序列匹配的顺序和并行算法及其在计算生物学中的应用
- 批准号:
1704552 - 财政年份:2017
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
BD Hubs: Collaborative Proposal: SOUTH: A Big Data Innovation Hub for the South Region
BD 中心:合作提案:SOUTH:南部地区的大数据创新中心
- 批准号:
1550305 - 财政年份:2015
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
EAGER: Exploratory Research on the Micron Automata Processor
EAGER:微米自动机处理器的探索性研究
- 批准号:
1448333 - 财政年份:2014
- 资助金额:
$ 92.52万 - 项目类别:
Standard Grant
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