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

描述(由申请人提供)：我们建议为高通量序列分析的两个核心问题研究新的计算方法：(1)RNAseq和元基因组数据中转录本和物种丰度的量化，以及(2)改进测序读取的纠错。提出的解决这两个问题的新方法源于快速计算巨大的序列数据集合中每个k-mer(长度为k的串)的每个实例的能力。在项目人员出版的k-mer计数软件(称为水母)中显示的关于这一问题的广泛的初步工作将得到落实和推广。现有的用于定量转录本丰度的基于映射的计算技术已经发现了广泛的适用性，但由于例如剪接连接、微外显子和参考序列的变异，读取映射容易出错。目标1试图开发一种替代的、无图谱的方法来从测序数据中定量转录，该方法依赖于对标准化的k-mer计数向量进行聚类来识别指示转录或基因丰度的k-MERs。这些k-MERS形成的图谱可用于在随后的实验中快速量化给定转录本或基因的表达，而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通过开发更准确的k-mer过滤方法和更紧凑的De Bruijn图表示来解决基因组错误纠正的问题，更具推测性地，RNAseq读取。新的过滤程序试图通过同时考虑它们在读数中的位置和它们的质量分数在读数之间的分布来更好地区分正确和错误的k-MERS。改进的纠错和De Bruijn图形表示将用于更有效的算法，用于超级读取和单元创建，这是组装的初始阶段。为这两个目标开发的方法和软件将显著提高在广泛可用的商用计算机上完成的高通量序列分析和组装的能力。

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.