Machine learning approaches for improved accuracy and speed in sequence annotation

摘要/摘要 生物序列的比对是理解它们的进化、功能和模式的关键一步 活动。在这里，我们描述了机器学习方法，以提高精度和速度高度- 敏感的序列比对。为了提高准确率，我们开发了减少错误标注的方法 这是由于(1)低复杂度和重复序列的存在以及(2)过度扩展造成的 将真同系物比对成不相关的序列。我们描述了基于这两种隐藏的方法 马尔可夫模型和人工神经网络大大减少了这类序列标注 错误。我们还解决了标注速度的问题，并开发了一个自定义的深度学习 体系结构旨在非常快速地过滤掉大部分候选序列比较 相对较慢的序列比对步骤。这些努力的成果将纳入 开源序列比对工具HMMER、MMSeqs和(适当时)BLAST；我们还将 与注释管道的社区开发人员(如RepeatMasker和IMG/M)合作，将 这些方法。这些广泛使用的生物信息学工具的开发和整合将导致 对序列注释工作的广泛影响。

Summary/Abstract Alignment of biological sequences is a key step in understanding their evolution, function, and patterns of activity. Here, we describe Machine Learning approaches to improve both accuracy and speed of highly- sensitive sequence alignment. To improve accuracy, we develop methods to reduce erroneous annotation caused by (1) the existence of low complexity and repetitive sequence and (2) the overextension of alignments of true homologs into unrelated sequence. We describe approaches based on both hidden Markov models and Artificial Neural Networks to dramatically reduce these sorts of sequence annotation error. We also address the issue of annotation speed, with development of a custom Deep Learning architecture designed to very quickly filter away large portions of candidate sequence comparisons prior to the relatively-slow sequence-alignment step. The results of these efforts will be incorporated into forks of the open source sequence alignment tools HMMER, MMSeqs, and (where appropriate) BLAST; we will also work with community developers of annotation pipelines, such as RepeatMasker and IMG/M, to incorporate these approaches. The development and incorporation into these widely used bioinformatics tools will lead to widespread impact on sequence annotation efforts.