Design, analysis, development and experimental validation of algorithms for high throughput sequencing mass data using the SeqAn library for biological sequence analysis

使用 SeqAn 库进行生物序列分析的高通量测序海量数据算法的设计、分析、开发和实验验证

• 批准号: 192954395
• 负责人: Professor Dr. Knut Reinert
• 金额: --
• 依托单位: Arbeitsgruppe Algorithmische Bioinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2010
• 资助国家: 德国
• 起止时间: 2009-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/192954395?language=en
• 关键词: Design; analysis; development; experimental; validation

During the last five years modern sequencing technologies have brought a super-exponential growth of sequencing capacities. At the time of writing this proposal it is possible to sequence about 30 billion nucleotides per day using one sequencing machine. This proposal aims to respond to the described increase of genomic sequence data with algorithmic approaches that benefit from redundancies across multiple datasets. More specifically we aim at: 1) Developing a data structure representing one or more genomic sequences by storing only the differences to a similar reference sequence while maintaining the ability to navigate quickly in all sequences. We then us this data structure for developing algorithms to transform the substring index data structure of a reference to the substring index of a new genome without rebuilding it from scratch and by only storing the differences to the reference index. 2) Developing algorithms that efficiently process multiple genomes in parallel based on the representation developed in 1). 3) Bridging the gap between algorithm theory and practical implementations by extending SeqAn as a library providing the core algorithmic components required to analyze large-scale genomic data and as an experimental platform to design, analyze, and implement state-of-the-art bioinformatics algorithms.

在过去的五年中，现代测序技术带来了测序能力的超指数增长。在撰写本提案时，使用一台测序机每天可以测序约300亿个核苷酸。该提案旨在通过受益于多个数据集之间的冗余的算法方法来响应所描述的基因组序列数据的增加。更具体地说，我们的目标是：1）通过仅存储与相似参考序列的差异，同时保持在所有序列中快速导航的能力，开发表示一个或多个基因组序列的数据结构。然后，我们使用该数据结构来开发算法，以将参考的子串索引数据结构转换为新基因组的子串索引，而无需从头开始重建它，并且仅将差异存储到参考索引。2)开发算法，有效地处理多个基因组在1）的基础上开发的表示并行。3)通过扩展SeqAn作为一个库，提供分析大规模基因组数据所需的核心算法组件，并作为一个实验平台，设计，分析和实现最先进的生物信息学算法，弥合算法理论和实际实现之间的差距。