Poster: A Parallel Framework for Ab Initio Transcript-Clustering
Poster: A Parallel Framework for Ab Initio Transcript-Clustering
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海报：从头开始转录聚类的并行框架
DOI：
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发表时间：
2018
期刊：
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通讯作者：
D. Rao
中科院分区：
文献类型：
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作者：
D. Rao
聚类用于将基因组数据划分为不相交的子集，以简化进一步的处理。由于输入可能包含数十亿个核苷酸，因此性能至关重要。因此，集群软件通常被开发为紧密耦合的单片系统，这阻碍了软件的可重用性，可扩展性和新算法以及数据结构的引入。在我们自己的集群软件中经历了类似的问题后，我们开发了一个灵活且可扩展的并行框架，称为PEACE。该框架的目标是在不影响性能的情况下简化各种集群方法的设计、实现和使用。本文介绍了PEACE框架，它的软件体系结构，并行基础设施，分布式数据结构沿着与开发一个聚类算法的案例研究。还讨论了开发过滤器，算法和比较算法的案例研究，以说明PEACE的模块化和可扩展性，使软件重用的独特的方式，可能没有预见到当个别组件的开发。
Clustering is used to partition genomic data into disjoint subsets to streamline further processing. Since inputs can contain billions of nucleotides, performance is paramount. Consequently, clustering software is typically developed as a tightly coupled monolithic system which hinders software reusability, extensibility and introduction of new algorithms as well as data structures. Having experienced similar issues in our own clustering software, we have developed a flexible and extensible parallel framework called PEACE. The objective of the framework is to ease design, implementation, and use of various clustering methods without compromising performance. This paper presents the PEACE framework, its software architecture, parallel infrastructure, and distributed data structures along with a case study of developing a clustering algorithm. Case studies of developing filters, heuristics, and comparison algorithms are also discussed to illustrate modularity and extensibility of PEACE which enables software reuse in unique ways that may not have been foreseen when individual components were developed.