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、功能和生物学模式的关键步骤。 活动在这里，我们描述了机器学习方法，以提高准确性和速度的高度- 灵敏的序列比对为了提高准确性，我们开发了减少错误注释的方法 这是由于（1）存在低复杂性和重复序列，以及（2）过度延伸， 将真正的同源物排列成不相关的序列。我们描述的方法基于两个隐藏的 马尔可夫模型和人工神经网络，以显着减少这些类型的序列注释 错误.我们还解决了注释速度的问题，开发了一个自定义的深度学习 该架构被设计为在比较之前非常快速地过滤掉大部分候选序列比较。 相对缓慢的序列比对步骤。这些努力的成果将纳入《联合国宪章》的各分支， 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， 这些方法。开发和纳入这些广泛使用的生物信息学工具将导致 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.