LanceOtron: a deep learning peak caller for genome sequencing experiments.
LanceOtron: a deep learning peak caller for genome sequencing experiments.
复制标题
DOI：
10.1093/bioinformatics/btac525
复制
发表时间：
2022-09-15
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
基因组测序实验通过允许研究人员在基因组范围内识别重要的DNA编码元件而彻底改变了分子生物学。发现这些元素的区域在检测覆盖轨迹的模拟信号中表现为峰值，尽管人类可以很容易地对这些模式进行视觉分类，但许多基因组的大小需要算法实现。常用的方法侧重于对峰进行分类的统计测试，忽视背景信号不完全遵循任何已知的概率分布，并将信息密集的峰形状简化为最大高度。深度学习已被证明对许多模式识别任务具有高度准确性，与人类能力相当甚至超过人类能力，为重新想象和改进峰值呼叫提供了机会。我们提出了峰值调用框架LanceOtron，它将用于识别峰形的深度学习与用于评估显著性的多方面富集计算相结合。在对ATAC-seq、ChIP-seq和DNase-seq进行基准测试时，LanceOtron通过其改进的选择性和近乎完美的灵敏度，超越了长期以来的黄金标准峰调用器。一个功能齐全的Web应用程序可以从LanceOtron.molbiol.ox.ac.uk免费获得，通过Python的命令行界面可以从PyPI在https：//pypi.org/project/lanceotron/上pip安装，源代码和基准测试可以在https://github.com/LHentges/LanceOtron上获得。 补充数据可在Bioinformatics在线获得。
Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay’s coverage track, and despite the ease with which humans can visually categorize these patterns, the size of many genomes necessitates algorithmic implementations. Commonly used methods focus on statistical tests to classify peaks, discounting that the background signal does not completely follow any known probability distribution and reducing the information-dense peak shapes to simply maximum height. Deep learning has been shown to be highly accurate for many pattern recognition tasks, on par or even exceeding human capabilities, providing an opportunity to reimagine and improve peak calling. We present the peak calling framework LanceOtron, which combines deep learning for recognizing peak shape with multifaceted enrichment calculations for assessing significance. In benchmarking ATAC-seq, ChIP-seq and DNase-seq, LanceOtron outperforms long-standing, gold-standard peak callers through its improved selectivity and near-perfect sensitivity. A fully featured web application is freely available from LanceOtron.molbiol.ox.ac.uk, command line interface via python is pip installable from PyPI at https://pypi.org/project/lanceotron/, and source code and benchmarking tests are available at https://github.com/LHentges/LanceOtron. Supplementary data are available at Bioinformatics online.
登录
查看更多内容
DOI：
10.1093/bioinformatics/btq033
发表时间：
2010-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Quinlan AR;Hall IM
通讯作者：
Hall IM
DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
影响因子：
2.6
作者：
Klein, David C.;Hainer, Sarah J.
通讯作者：
Hainer, Sarah J.
影响因子：
14.9
作者：
Bailey TL;Johnson J;Grant CE;Noble WS
通讯作者：
Noble WS