Computational Methods for Microbial and Microbiome Sequence Analysis

微生物和微生物组序列分析的计算方法

• 批准号: 10331733
• 负责人: Steven L. Salzberg
• 金额: $ 40.34万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10331733
• 关键词: Address; Adopted; Algorithms; Anthrax disease; Archaea; Area; Bacteria; Bacterial Genome; Cholera; Computer software; Computing Methodologies; Custom; Data; Data Set; Development; Diagnosis; Environment; Eukaryota; Experimental Designs; Explosion; Genes; Genome; Human Microbiome; Infection; Licensing; Lyme Disease; Metagenomics; Methods; Organism; Pathogenicity; Performance; Problem Solving; Publications; Publishing; Scientist; Sequence Analysis; Shotgun Sequencing; Syphilis; System; Technology; Third Generation Sequencing; Time; Tuberculosis; Update; Virus; Work; bacterial genome sequencing; cost effective; experimental study; genome database; improved; microbial; microbiome; microorganism; next generation sequencing; open source; tool; whole genome

Project Summary This project will support our work on computational methods for microbial sequence analysis, including gene finding, whole-genome alignment, genome assembly, and metagenomic sequence analysis. Over the years we have developed multiple systems to solve problems in these areas, some of which are very widely used. These tools need continued updates and improvements to keep pace with changes in sequencing technology, changes in experimental design, and the ever-growing number of sequenced genomes. One of these systems is Glimmer, a computational method for finding genes in bacteria, viruses, archaea, and simple eukaryotes. Glimmer is highly accurate, finding over 99% of the genes in most prokaryotic genomes. It has been used by thousands of scientists around the world and in the majority of published bacterial genome sequencing projects over the past decade. Collectively the three main publications describing Glimmer have been cited over 4,700 times, including >700 citations in 2016-17 alone. Usage of Glimmer has been increased in recent years due to the explosion in next-generation sequencing projects, which are particularly cost-effective for bacterial genomes. A second system, MUMmer, is an efficient whole-genome aligner that is used to compare genomes to one another and to compare genome assemblies to detect changes, both large and small. MUMmer and its components, especially Nucmer, have been widely used and incorporated in other systems, including multi-genome aligners and several genome assembly packages. The three main publications describing MUMmer have been cited over 3,600 times including >750 citations in 2016-17. In recent years we have focused our efforts on developing methods for the analysis of metagenomics data, producing several newer tools, including Kraken and Centrifuge. Both of these systems attempt to assign a species identifier to every read in a metagenomics data set. Because the Kraken algorithm is not only accurate but far faster than earlier methods, it was rapidly adopted by many labs soon after its release, and its usage continues to grow. The even newer and more space- efficient Centrifuge system has also been highly successful and was recently incorporated into the analysis package of one of the new third-generation sequencing companies. We continue to work on improving the performance of both algorithms, and this project will allow us to extend them to handle the newest long-read data that is increasingly being used for metagenomics experiments. Finally, a new direction of the lab is the use of metagenomic shotgun sequencing to diagnose infections, for which we are not only modifying our algorithms, but also building customized genome databases where we rigorously screen the genomes to identify and remove contaminants and low-complexity sequences that create false positives. As we have done for many years, we will release all of the software and data generated by this project for free under an open source license, allowing other scientists to use, modify, and redistribute them without restrictions of any kind.

项目概要 该项目将支持我们在微生物序列分析计算方法方面的工作，包括基因 发现、全基因组比对、基因组组装和宏基因组序列分析。这些年来我们 已经开发了多种系统来解决这些领域的问题，其中一些系统应用非常广泛。这些 工具需要不断更新和改进，以跟上测序技术的变化、变化 实验设计以及不断增长的基因组测序数量。 Glimmer 就是其中之一， 一种在细菌、病毒、古细菌和简单真核生物中寻找基因的计算方法。微光是 高度准确，可找到大多数原核生物基因组中 99% 以上的基因。它已被数千人使用 世界各地的科学家以及过去大多数已发表的细菌基因组测序项目 十年。描述 Glimmer 的三本主要出版物总共被引用超过 4,700 次， 仅 2016-17 年就被引用超过 700 次。近年来，Glimmer 的使用量有所增加，原因是 下一代测序项目的爆炸式增长，对于细菌基因组来说尤其具有成本效益。一个 第二个系统 MUMmer 是一种高效的全基因组比对器，用于将基因组相互比较 并比较基因组组装以检测大大小小的变化。 MUMmer 及其组件， 特别是 Nucmer，已被广泛使用并纳入其他系统，包括多基因组对准器 和几个基因组组装包。描述 MUMmer 的三篇主要出版物已被引用 2016-17 年引用次数超过 3,600 次，其中引用次数超过 750 次。近年来我们主要致力于 开发宏基因组数据分析方法，生产几种更新的工具，包括 Kraken 和离心机。这两个系统都试图为宏基因组中的每个读数分配一个物种标识符 数据集。由于 Kraken 算法不仅准确，而且比早期的方法快得多，因此它很快就得到了广泛应用。 它发布后很快就被许多实验室采用，并且其使用量持续增长。更新更多的空间- 高效的离心机系统也非常成功，最近被纳入分析中 新第三代测序公司之一的包装。我们继续致力于改进 两种算法的性能，这个项目将使我们能够扩展它们以处理最新的长读 数据越来越多地用于宏基因组学实验。最后，实验室的一个新方向是使用 宏基因组鸟枪测序来诊断感染，为此我们不仅修改了我们的 算法，同时还构建定制的基因组数据库，我们在其中严格筛选基因组以识别 并去除造成误报的污染物和低复杂性序列。正如我们为许多人所做的那样 年，我们将在开源下免费发布该项目生成的所有软件和数据 许可证，允许其他科学家不受任何限制地使用、修改和重新分发它们。