Machine learning approaches for improved accuracy and speed in sequence annotation: supplement for software enhancement

摘要 这项补充申请的家长拨款的目标是开发机器学习方法来 提高高灵敏度序列数据库搜索和比对的准确性和速度。我们有 开发了与正确注释基因组的努力相关的三个软件工具：(I)ULTRA，它 标签重复序列，(Ii)Polya，它将这样的标签与其他序列注释集成在 概率框架，计算不确定性和提高精确度，以及(3)SODA，它有助于 注解和支持证据的可视化。在这里，我们描述了重构这些软件的计划 工具及其文档，以提高健壮性和可靠性，并通过以下方式提高可用性 包管理系统并整合到基于云的分析框架中。

Summary The goal of this parent grant for this supplement request is to develop Machine Learning approaches to improve both accuracy and speed of highly-sensitive sequence database search and alignment. We have developed three software tools associated with this effort of correctly annotating genomes: (i) ULTRA, which labels repetitive sequence, (ii) PolyA which integrates such labels with other sequence annotations in a probabilistic framework, computing uncertainty and improving accuracy, and (iii) SODA, which aids in visualization of annotations and supporting evidence. Here, we describe a plan to refactor these software tools and their documentation to improve robustness and reliability, and to improve their availability through package management systems and incorporation into cloud-based analysis frameworks.