A tool for identifying causative mutations from sequencing data without a reference genome

无需参考基因组即可从测序数据中识别致病突变的工具

• 批准号: BB/M019896/1
• 负责人: Dan MacLean
• 金额: $ 16.31万
• 依托单位: University of East Anglia
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FM019896%2F1
• 关键词: tool; identifying; causative; mutations; sequencing

Forward genetic screens are essential to identify target genes behind desirable traits and their beneficial application. Traditional map-based cloning approaches are extremely labour intensive and years can elapse between the mutagenesis and the detection of the polymorphism responsible for the phenotype. The arrival of high throughput sequencing (HTS) technologies has raised the importance of genomics and offers a number of ways to accelerate discoveries using forward genetic screens. A primary application of HTS for genetics is the detection of DNA sequence polymorphisms among different genotypes within a species, as these polymorphisms can be directly associated with phenotypic variation. HTS approaches have accelerated forward genetic screens through the rate at which mutations are mapped. An important advance includes mapping-by-sequencing (MBS), which enables mapping and identification of causal mutation in a single step by providing allele frequency from pools and the identification of causal mutations at single-nucleotide resolution. MBS requires a complete genome assembly and cannot be used in non-sequenced species or those with draft genomes. Hence, there is a need for computational tools to identify mutations directly from a general, whole genome HTS datasets for organisms with a draft or pre-draft genome assembly. Even though the ability to cause mutations and manipulate non-model genomes to test and characterise the candidate mutations are available, lack of or limited genetic and genomic resources are restricting the application of HTS methods to forward genetic screens of non-model organisms. Therefore new methods are necessary that can provide fast and cost-effective ways to order genome assemblies for causative mutation mapping using sequencing data from forward genetics screen on non-model plants and animals. We have exciting preliminary data from an algorithm we have developed that can order contigs based on the expected density distribution of SNPs from forward genetic mutant data. We have devised a genetic algorithm that can effectively traverse the space to find an optimum arrangement that maximises the SNP density distribution according to the expected distribution from the initial genetic screen. We have implemented and tested Genetic Algorithm To Re-order Contigs (GATROC) using a small simulated dataset generated from Arabidopsis. The major objective of the work proposed here is to develop our proof-of-principle fragment arrangement algorithm to be applicable to sequence data from genomes of any size and using data generated from different sequencing technologies. We also would evaluate the algorithms performance using various published studies to provide benchmarks and opportunities to extend to various other systems. We will include a variant call pipeline to deal with a range of sequencing technologies. Additionally we will implement various extensions of the algorithms that would analyse data from backcrossed populations as well as variant data from polyploids such as wheat. We aim to provide visualization tools that would help design markers to verify the candidate mutation. Our algorithm will be provided in various implementations such as Galaxy pipelines, binaries for use in various operating systems as well open source release of the source code for developers to ensure the software is as widely used as possible.

正向遗传筛选对于识别理想性状背后的目标基因及其有益应用至关重要。传统的基于图谱的克隆方法是非常劳动密集型的，从诱变到检测导致表型的多态之间可能需要数年的时间。高通量测序(HTS)技术的到来提高了基因组学的重要性，并提供了许多使用正向基因筛查来加速发现的方法。HTS在遗传学中的一个主要应用是检测一个物种内不同基因型之间的DNA序列多态，因为这些多态可以直接与表型变异相关。HTS方法通过绘制突变图谱的速度加快了前向基因筛查的速度。一项重要的进展包括测序作图(MBS)，它通过提供池中的等位基因频率和单核苷酸分辨率的因果突变识别，使得能够在单一步骤中定位和识别因果突变。MBS需要完整的基因组组装，不能用于未测序的物种或具有草稿基因组的物种。因此，需要计算工具来直接从具有草稿或预草稿基因组组装的生物体的通用、全基因组HTS数据集中识别突变。尽管有能力导致突变并操纵非模式基因组来测试和表征候选突变，但遗传和基因组资源的缺乏或有限限制了HTS方法在非模式生物遗传筛选中的应用。因此，新的方法是必要的，可以提供快速和经济有效的方法，使用来自非模式动植物的正向遗传学筛查的测序数据来对基因组组件进行排序，以用于病因突变图谱。我们从我们开发的一种算法中获得了令人兴奋的初步数据，该算法可以根据正向遗传突变数据中SNPs的预期密度分布对重叠群进行排序。我们设计了一种遗传算法，它可以有效地遍历空间，根据初始遗传筛选的预期分布找到最大化SNP密度分布的最优排列。我们已经使用从拟南芥产生的一个小的模拟数据集实现并测试了遗传算法对重叠群进行重新排序(GATROC)。这项工作的主要目标是开发我们的原则证明片段排列算法，以适用于来自任何大小的基因组的序列数据，并使用不同测序技术产生的数据。我们还将使用各种已发表的研究来评估算法的性能，以提供基准和扩展到各种其他系统的机会。我们将包括一个变量调用管道来处理一系列测序技术。此外，我们将实现算法的各种扩展，以分析回交群体的数据以及多倍体(如小麦)的变异数据。我们的目标是提供可视化工具，帮助设计标记来验证候选突变。我们的算法将在各种实现中提供，如Galaxy管道，用于各种操作系统的二进制文件，以及开源源代码的发布，供开发人员使用，以确保软件尽可能广泛地使用。

项目成果

Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence.

从一组无序的、重叠群大小的基因组序列片段中快速精细地绘制致病突变。

• DOI: 10.1186/s12859-018-2515-5
• 发表时间: 2019
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Rallapalli G

Additional file 1 of Rapid fine mapping of causative mutations from sets of unordered, contig-sized fragments of genome sequence

从基因组序列的无序重叠群大小片段中快速精细绘制致病突变的附加文件 1

• DOI: 10.6084/m9.figshare.7558292
• 发表时间: 2019
• 作者: Ghanasyam Rallapalli