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:10465048
- 负责人:
- 金额:$ 5.17万
- 依托单位:
- 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 2022-09-23
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsArchitectureBioinformaticsBiologicalClassificationCollectionCommunitiesComplexComputer Vision SystemsComputer softwareConsumptionCustomDNA Transposable ElementsData SetDeletion MutationDescriptorDevelopmentError SourcesEvolutionFoundationsGenomeGenomicsHourHumanHuman GenomeIndustry StandardInsertion MutationInstitutesInterventionJointsLabelLettersLicensingMachine LearningManualsMasksMethodsModelingModernizationMolecular BiologyNetwork-basedNucleotidesPatternPilot ProjectsProteinsRepetitive SequenceSequence AlignmentSequence AnalysisSourceSpeedStatistical ModelsTakifuguWorkannotation systemartificial neural networkbasebioinformatics toolcomputing resourcesconvolutional neural networkdeep learningdensitydesigngenomic dataimprovedmarkov modelneural network architecturenovelnovel strategiesopen sourcesoftware developmentstatisticssuccesstool
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.
总结/摘要
生物序列的比对是理解其进化、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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Travis John Wheeler其他文献
Travis John Wheeler的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Travis John Wheeler', 18)}}的其他基金
Building Knowledge About Alternatively-spliced Dual-Coding Exons
建立关于选择性剪接双编码外显子的知识
- 批准号:
10363514 - 财政年份:2022
- 资助金额:
$ 5.17万 - 项目类别:
Building Knowledge About Alternatively-spliced Dual-Coding Exons
建立关于选择性剪接双编码外显子的知识
- 批准号:
10701663 - 财政年份:2022
- 资助金额:
$ 5.17万 - 项目类别:
Machine learning approaches for improved accuracy and speed in sequence annotation: supplement for software enhancement
提高序列注释准确性和速度的机器学习方法:软件增强的补充
- 批准号:
10406630 - 财政年份:2019
- 资助金额:
$ 5.17万 - 项目类别:
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
- 批准号:
10838066 - 财政年份:2019
- 资助金额:
$ 5.17万 - 项目类别:
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
- 批准号:
10020995 - 财政年份:2019
- 资助金额:
$ 5.17万 - 项目类别:
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
- 批准号:
10231149 - 财政年份:2019
- 资助金额:
$ 5.17万 - 项目类别:
相似海外基金
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 5.17万 - 项目类别:
Continuing Grant
Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms
合作研究:SHF:小型:物联网人工智能 (AIoT):理论、架构和算法
- 批准号:
2221742 - 财政年份:2022
- 资助金额:
$ 5.17万 - 项目类别:
Standard Grant
Collaborative Research: SHF: Small: Artificial Intelligence of Things (AIoT): Theory, Architecture, and Algorithms
合作研究:SHF:小型:物联网人工智能 (AIoT):理论、架构和算法
- 批准号:
2221741 - 财政年份:2022
- 资助金额:
$ 5.17万 - 项目类别:
Standard Grant
Algorithms and Architecture for Super Terabit Flexible Multicarrier Coherent Optical Transmission
超太比特灵活多载波相干光传输的算法和架构
- 批准号:
533529-2018 - 财政年份:2020
- 资助金额:
$ 5.17万 - 项目类别:
Collaborative Research and Development Grants
OAC Core: Small: Architecture and Network-aware Partitioning Algorithms for Scalable PDE Solvers
OAC 核心:小型:可扩展 PDE 求解器的架构和网络感知分区算法
- 批准号:
2008772 - 财政年份:2020
- 资助金额:
$ 5.17万 - 项目类别:
Standard Grant
Algorithms and Architecture for Super Terabit Flexible Multicarrier Coherent Optical Transmission
超太比特灵活多载波相干光传输的算法和架构
- 批准号:
533529-2018 - 财政年份:2019
- 资助金额:
$ 5.17万 - 项目类别:
Collaborative Research and Development Grants
Visualization of FPGA CAD Algorithms and Target Architecture
FPGA CAD 算法和目标架构的可视化
- 批准号:
541812-2019 - 财政年份:2019
- 资助金额:
$ 5.17万 - 项目类别:
University Undergraduate Student Research Awards
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
合作研究:ABI 创新:从 3D 成像恢复根结构的算法
- 批准号:
1759836 - 财政年份:2018
- 资助金额:
$ 5.17万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
合作研究:ABI 创新:从 3D 成像恢复根结构的算法
- 批准号:
1759796 - 财政年份:2018
- 资助金额:
$ 5.17万 - 项目类别:
Standard Grant
Collaborative Research: ABI Innovation: Algorithms for recovering root architecture from 3D imaging
合作研究:ABI 创新:从 3D 成像恢复根结构的算法
- 批准号:
1759807 - 财政年份:2018
- 资助金额:
$ 5.17万 - 项目类别:
Standard Grant