HMSC: a Hybrid Metagenomic Sequence Classification Algorithm
HMSC: a Hybrid Metagenomic Sequence Classification Algorithm
复制标题
HMSC：混合宏基因组序列分类算法
DOI：
10.1145/3388440.3412468
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Rajasekaran, Sanguthevar
中科院分区：
文献类型：
--
作者：
Saha, Subrata;Wang, Zigeng;Rajasekaran, Sanguthevar
新一代测序（NGS）技术的广泛应用最近引起了人们对微生物组的兴趣激增。因此，宏基因组学在生物信息学和计算生物学中是一个快速发展的领域。分析宏基因组测序数据的一个重要问题是鉴定样品中存在的微生物并计算出它们的相对丰度。RefSeq和GenBank等基因组数据库为描述宏基因组测序数据集提供了越来越多的资源。然而，这些数据库的规模和相关基因组之间可能存在的高度序列同源性意味着精确分析宏基因组读数在计算上具有挑战性。在本文中，我们提出了一种高效的算法，称为“混合宏基因组序列分类器”（HMSC），以准确检测宏基因组样本中的微生物及其相对丰度。该算法方法与目前在该领域存在的其他最先进的算法有根本的不同。HMSC明智地利用无比对和基于比对的方法来准确表征宏基因组测序数据。在真实和合成数据集上进行的严格实验评估表明，与其他最先进的方法相比，HMSC在准确性、内存和运行时间方面确实是一种有效、可扩展和高效的算法。
Widespread availability of next-generation sequencing (NGS) technologies has prompted a recent surge in interest in the microbiome. As a consequence, metagenomics is a fast growing field in bioinformatics and computational biology. An important problem in analyzing metagenomic sequenced data is to identify the microbes present in the sample and figure out their relative abundances. Genome databases such as RefSeq and GenBank provide a growing resource to characterize metagenomic sequenced datasets. However, both the size of these databases and the high degree of sequence homology that can exist between related genomes mean that accurate analysis of metagenomic reads is computationally challenging. In this article we propose a highly efficient algorithm dubbed as "Hybrid Metagenomic Sequence Classifier" (HMSC) to accurately detect microbes and their relative abundances in a metagenomic sample. The algorithmic approach is fundamentally different from other state-of-the-art algorithms currently existing in this domain. HMSC judiciously exploits both alignment-free and alignment-based approaches to accurately characterize metagenomic sequenced data. Rigorous experimental evaluations on both real and synthetic datasets show that HMSC is indeed an effective, scalable, and efficient algorithm compared to the other state-of-the-art methods in terms of accuracy, memory, and runtime.
影响因子：
3.7
作者：
Diaz PI;Dupuy AK;Abusleme L;Reese B;Obergfell C;Choquette L;Dongari-Bagtzoglou A;Peterson DE;Terzi E;Strausbaugh LD
通讯作者：
Strausbaugh LD
影响因子：
48
作者：
Sunagawa, Shinichi;Mende, Daniel R.;Bork, Peer
通讯作者：
Bork, Peer