Fast k-mer Counting to Quantify Gene Expression and Improve Genome Assembly

快速 k-mer 计数可量化基因表达并改善基因组组装

• 批准号: 8642468
• 负责人: Carleton Lee Kingsford
• 金额: $ 24.06万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8642468
• 关键词: Fast; mer; Counting; Quantify; Gene

DESCRIPTION (provided by applicant): We propose to investigate new computational approaches to two central problems of high-throughput se- quence analysis: (1) quantification of transcript and species abundance in RNAseq and metagenomic data, and (2) improved error correction of sequencing reads. The proposed novel approaches to both of these problems derive from the ability to quickly count every instance of every k-mer (string of length k) within huge collections of sequence data. Extensive preliminary work on this problem, manifest in the k-mer counting software (called Jellyfish) published by the project personnel, will be brought to bear and extended. Existing mapping-based computational techniques for quantifying transcript abundance have found wide applicability but read mapping is error prone due to, e.g., splice junctions, microexons, and variation from the reference sequence. Aim 1 seeks to develop an alternative, mapping-free approach to transcript quantification from sequencing data that relies on clustering normalized k-mer count vectors to identify k-mers that are indicative of transcript or gene abundance. These k-mers form profiles that can be used to rapidly quantify expression of the given transcript or gene in subsequent experiments with limited computational effort and avoiding the challenging read mapping step. Aim 2 tackles the problem of error correction of genomic, and, more speculatively, RNAseq reads by developing more accurate k-mer filtering methods and more compact de Bruijn graph representations. The new filtering proce- dures try to make a better distinction between correct and erroneous k-mers by simultaneously considering their position within the reads and the distribution of their quality scores across reads. Improved error correction and de Bruijn graph representations will be used for more efficient algorithms for super-read and unitig creation, the initial stages of assembly. The methods and software developed for both aims will significantly increase the ability of high-throughput sequence analysis and assembly to be completed on widely available commodity computers.

描述（由申请人提供）：我们建议研究新的计算方法来解决高通量序列分析的两个核心问题：(1)RNAseq和宏基因组数据中转录物和物种丰度的量化，以及(2)改进测序reads的纠错。提出的解决这两个问题的新方法源于在庞大的序列数据集合中快速计数每个k-mer（长度为k的字符串）的每个实例的能力。在这个问题上广泛的初步工作，体现在k-mer计数软件（称为水母）由项目人员发布，将带来承担和扩展。现有的基于定位的转录本丰度定量计算技术已经发现了广泛的适用性，但由于剪接、微外显子和参考序列的变化等原因，读取定位容易出错。Aim 1旨在开发一种替代的、无图谱的方法来从测序数据中进行转录物定量，该方法依赖于聚类归一化k-mer计数载体来鉴定转录物或基因丰度的k-mers。这些k-mers形成的谱可以用于在随后的实验中快速量化给定转录物或基因的表达，只需有限的计算工作量，并避免具有挑战性的读取映射步骤。目标2通过开发更精确的k-mer过滤方法和更紧凑的de Bruijn图表示来解决基因组错误校正问题，并且更有推测性地解决RNAseq读取问题。新的过滤程序通过同时考虑k-mers在reads中的位置和它们在reads中的质量分数分布，试图更好地区分正确和错误的k-mers。改进的纠错和de Bruijn图表示将用于更有效的算法，用于超级读取和统一创建，组装的初始阶段。为这两个目标开发的方法和软件将大大提高在广泛可用的商用计算机上完成高通量序列分析和组装的能力。