ABI Innovation: Identifying phylogenetically informative data from next-generation sequencing

物种之间进化关系的知识构成了从农业到生态学到医学等领域研究的基本基础。该项目旨在开发新的方法和软件，以快速处理大型基因组数据集，并提取有助于提高我们对这些关系的理解的信息。 该项目将为3- 60名本科生提供广泛的生物信息学培训;将开发计算机练习，以吸引数百名额外的本科生参加大学课程。将在年度会议上向科学界提供研究更新和解释，并通过科普博客《熊猫？这项研究的目的是开发软件，以促进进化领域的许多项目，包括对非模式生物和新出现的传染病的研究。首先，这项研究将产生一种方法来识别基因组中足够相似的区域，以便在不同的群体中进行比较。第二，将开发新的统计模型，以准确地确定这些地区的每个物种的序列，因为目前的基因组测序方法存在错误。第三，将开发自动识别数据的方法，这些数据对确定最近和古代的进化事件都有用。由此产生的数据可以在已建立的分析软件平台中使用。该项目开发的方法将在https://github.com/rachelss/SISRS上作为强大的、易于使用的软件提供给科学界。

Knowledge of evolutionary relationships among species forms the fundamental basis for research in fields ranging from agriculture to ecology to medicine. This project aims to develop new methods and software to rapidly process large genomic datasets and extract information that helps improve our understanding of these relationships. This project will provide extensive bioinformatics training for 3-6undergraduates; computer exercises will be developed to engage hundreds of additional undergraduates in college courses. Research updates and explanations will be provided to the scientific community at annual conferences, and to the general public through the popular science weblog, The Panda?s Thumb (http://www.pandasthumb.org/).This research seeks to develop software that will facilitate numerous projects in the field of evolution, including research on non-model organisms and emerging infectious diseases. First, this research will produce a method to identify regions of the genome that are similar enough that they can be compared among diverse groups. Second, new statistical models will be developed to accurately determine the sequence for each species for these regions given the error in current methods of genome sequencing. Third, methods will be developed to automate the identification of data that is useful to determine both recent and ancient evolutionary events. The resulting data can be used in established analytical software platforms. The methods developed by this project will be made available to the scientific community as robust, easy-to-use software at https://github.com/rachelss/SISRS.