Read-to-contig alignments for de novo genome assembly and annotation

用于从头基因组组装和注释的读取到重叠群比对

• 批准号: RGPIN-2014-05112
• 负责人: Birol, Inanc
• 金额: $ 2.57万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566960
• 关键词: Read; contig; alignments; de; novo

The proposed research is about building computational technologies to analyze DNA. DNA is composed of sequences of four possible nucleotides (nt): A, C, G and T. The last decade witnessed a revolution in technologies that “read” DNA sequences, with applications in many areas of life sciences. Data from high throughput sequencing (HTS) platforms reach hundreds of millions of “reads”, where each read represents 75-300 nt of DNA (the human genome – the sum total of our DNA – is around 3 billion nt long). Interpreting these massive volumes of short reads is an ongoing challenge as sequencing technologies evolve. There are two popular analysis methods that process HTS reads: alignment-based and assembly-based approaches. The first uses a reference genome, whose DNA sequence is known from previous studies of the same or a closely related species. In this approach, reads are aligned to the reference genome through a process that searches for sequence similarities between the reads and the reference. The second approach is a data-driven method that does not assume similarity to any given genome. Instead, it reconstructs the genome represented by the DNA de novo (from scratch). This is a less biased approach that gives a truer representation of the genome, especially if there have been rearrangements compared to the reference genome sequence, or if no reference is available. The Birol lab has developed de novo assembly algorithms and downstream analysis tools and has applied them in a number of highly visible projects in human health and other fields. In the proposed work, the team will concentrate on alignment technologies as a way to support this highly successful assembly based analysis platform. The read alignment problem has been addressed several times, to match changes in read lengths and data volumes as HTS technology evolved. However, efficient and accurate alignment of reads to newly assembled genomes is an un-answered need. General purpose read alignment algorithms assume the target sequence to be composed of a small number of long stretches of sequence, essentially, chromosomes. The results of draft de novo assembly processes, in contrast, are typically in hundreds of thousands of pieces. This creates problems for general-purpose aligners, which we will address by developing an algorithm for this specific need. We will pay special attention to the scalability of our algorithm to accommodate the growing volume of data, and we will achieve this by building parallel processing algorithms similar to those used in Internet search engines, such as Google. When the genome of a new species is sequenced and assembled, one important task is to “annotate” its genes – i.e. mark where they are in the genome, and how they are structured. We also note an important gap in this area, as current alignment technologies were developed for previous generations of sequencing platforms, and have exceeded their limits to support data from new sequencing projects. (One such popular tool, exonerate, is still being heavily used, yet it is no longer being maintained by the developer lab.) We propose to build an alternative to these tools, and provide sustained support for the community. As the use of sequencing technologies further penetrates life sciences, there is an urgent need for high-quality computational tools to analyze large volumes of data in a timely manner. Development of the described alignment technologies will improve the efficiency and the accuracy of de novo assemblies and their annotation.

拟议的研究是关于建立计算技术来分析DNA。DNA由四种可能的核苷酸（nt）序列组成：A、C、G和T。过去十年见证了“读取”DNA序列的技术革命，并在生命科学的许多领域得到应用。来自高通量测序（HTS）平台的数据达到数亿个“读段”，其中每个读段代表75-300 nt的DNA（人类基因组-我们DNA的总和-约为30亿nt长）。随着测序技术的发展，解释这些大量的短读段是一个持续的挑战。有两种流行的分析方法处理HTS读取：基于汇编的方法和基于汇编的方法。第一种方法使用参考基因组，其DNA序列是从以前对相同或密切相关物种的研究中得知的。在该方法中，通过搜索读段和参考之间的序列相似性的过程将读段与参考基因组比对。第二种方法是数据驱动的方法，不假设与任何给定基因组的相似性。相反，它重新构建由DNA从头（从头开始）代表的基因组。这是一种偏差较小的方法，可以更真实地表示基因组，特别是如果与参考基因组序列相比存在重排，或者如果没有参考可用。Birol实验室开发了从头组装算法和下游分析工具，并将其应用于人类健康和其他领域的许多高度可见的项目。在拟议的工作中，该团队将专注于对准技术，以支持这一非常成功的基于组装的分析平台。读段对齐问题已经被多次解决，以匹配随着HTS技术的发展而发生的读段长度和数据量的变化。然而，读段与新组装的基因组的有效且准确的比对是一个未回答的需求。通用读段比对算法假设靶序列由少量长序列段组成，基本上是染色体。相比之下，草图从头组装过程的结果通常是数十万件。这给通用校准器带来了问题，我们将通过开发一种算法来解决这一特定需求。我们将特别注意算法的可扩展性，以适应不断增长的数据量，我们将通过构建类似于互联网搜索引擎（如Google）中使用的并行处理算法来实现这一目标。当一个新物种的基因组被测序和组装时，一个重要的任务是“注释”它的基因-即标记它们在基因组中的位置，以及它们是如何结构的。我们还注意到这一领域的一个重要差距，因为目前的比对技术是为前几代测序平台开发的，并且已经超过了它们的限制，以支持来自新测序项目的数据。(One这种流行的工具，exonerate，仍然被大量使用，但它不再由开发人员实验室维护。）我们建议建立一个替代这些工具，并为社区提供持续的支持。随着测序技术的使用进一步渗透到生命科学中，迫切需要高质量的计算工具来及时分析大量数据。所描述的比对技术的发展将提高从头组装及其注释的效率和准确性。