Machine learning approaches for improved accuracy and speed in sequence annotation

用于提高序列注释的准确性和速度的机器学习方法

基本信息

批准号：
10838066
负责人：
Travis John Wheeler
金额：
$ 25.21万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-20 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10838066
关键词：
Machine learning approaches improved accuracy

项目摘要

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.

摘要/摘要生物序列的比对是理解其进化、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，以提高高度的准确性和速度。敏感的序列比对。为了提高准确性，我们开发了减少错误注释的方法由（1）低复杂性和重复序列的存在以及（2）过度扩展引起将真正的同源物与不相关的序列进行比对。我们描述了基于隐藏的方法马尔可夫模型和人工神经网络可显着减少此类序列注释错误。我们还通过开发定制的深度学习来解决注释速度的问题旨在快速过滤掉大部分候选序列比较的架构相对较慢的序列比对步骤。这些努力的结果将被纳入到分叉中开源序列比对工具 HMMER、MMSeqs 和（如果适用）BLAST；我们也会与注释管道的社区开发人员（例如 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产生广泛影响。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

nail: software for high-speed, high-sensitivity protein sequence annotation.

nail：用于高速、高灵敏度蛋白质序列注释的软件。

DOI：
发表时间：
2024-01-30
期刊：
影响因子：
0
作者：
Roddy, Jack W;Rich, David H;Wheeler, Travis J
通讯作者：
Wheeler, Travis J

Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.

Drugsniffer：一种开源工作流程，用于虚拟筛选数十亿个分子与蛋白质靶点的结合亲和力。

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Venkatraman, Vishwesh;Colligan, Thomas H;Lesica, George T;Olson, Daniel R;Gaiser, Jeremiah;Copeland, Conner J;Wheeler, Travis J;Roy, Amitava
通讯作者：
Roy, Amitava

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.

DISCO：一种深度学习集成，用于对声学信号进行不确定性感知分割。