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计算机系统应用英文版:2013,22(11):165-170,142

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基于仿射聚类的宏基因组序列物种聚类

聂鹏宇^1,2, 潘玮华^1,2, 徐云^1,2

(1.安徽省高性能计算重点实验室, 安徽合肥 230027;2.中国科学技术大学计算机科学与技术学院, 安徽合肥 230027)

Metagenomic DNA Sequence Binning based on Affinity Propagation

NIE Peng-Yu^1,2, PAN Wei-Hua^1,2, XU Yun^1,2

(1.Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China;2.School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China)

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本文已被：浏览 1738次下载 2400次
Received:April 22, 2013 Revised:May 13, 2013

中文摘要: 随着下一代测序技术的迅猛发展, 宏基因组学已经成为新的研究热点, 宏基因组学序列聚类问题使用无参考的方法, 对包含多个物种的宏基因组序列进行有效分离. 为此, 提出一种结合相似度信息和结构信息的宏基因组物种聚类算法, 并引入仿射聚类来进行序列物种聚类. 实验数据表明该方法聚类精度高、执行速度快. 我们也开发了基于该方法的宏基因组序列物种聚类软件.

中文关键词: 宏基因组学 DNA序列物种聚类仿射聚类倒排索引

Abstract:Nowadays, with the rapid development of the next generation sequencing technologies, metagenomics have become a new hotspot,However research in metagenomics faces the issue of binning --- identification and taxonomic characterization of the NGS short reads. To solve this problem, this paper first analyzes the next generation sequencing technology characteristics, statistical characteristics of metagenomic sequence, then proposes a new clustering method for DNA sequence binning. Test results show that this method has a very good clustering accuracy. In the same time, we developed an software for metagenomic binning based on this algorithm MetaBinning.

keywords: metagenomics DNA sequence binning affinity propagation inverted index

文章编号： 中图分类号： 文献标志码：

基金项目:国家自然科学基金面上项目(60970085)

引用文本：
聂鹏宇,潘玮华,徐云.基于仿射聚类的宏基因组序列物种聚类.计算机系统应用,2013,22(11):165-170,142
NIE Peng-Yu,PAN Wei-Hua,XU Yun.Metagenomic DNA Sequence Binning based on Affinity Propagation.COMPUTER SYSTEMS APPLICATIONS,2013,22(11):165-170,142

Author Name	Affiliation
NIE Peng-Yu	Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China
PAN Wei-Hua	Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China
XU Yun	Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China

Author Name	Affiliation
NIE Peng-Yu	Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China
PAN Wei-Hua	Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China
XU Yun	Key Laboratory of High Performance Computing of Anhui Province, Anhui University, Hefei Anhui 230027, China School of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China