SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches.
SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches.
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DOI：
10.1101/sqb.2017.82.033761
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Misteli T
中科院分区：
文献类型：
--
作者：
Gudla PR;Nakayama K;Pegoraro G;Misteli T
DNA荧光原位杂交（FISH）是在细胞核中单个等位基因水平上绘制基因组位点在三维空间（3D）位置的首选技术。高通量DNA FISH方法最近被开发出来，使用复杂的荧光标记合成寡核苷酸文库和自动荧光显微镜，能够大规模地研究基因组组织。虽然高通量方法产生的FISH信号原则上可以通过传统的点检测算法进行分析，但这些方法需要用户干预以优化每个被询问的基因组位点，使得在单个实验中分析数十或数百个基因组位点难以实现。我们在这里报告了两个独立的基于机器学习的工作流程的设计和测试，用于高通量格式的FISH信号检测。这两种方法分别依赖于随机森林（RF）分类或卷积神经网络（cnn）。这两个工作流程都可以在三个独立的荧光显微镜通道中对数十个独立的基因组位点进行高精度检测DNA FISH信号，而无需在每个位点的基础上手动设置参数值。特别是我们命名为SpotLearn的CNN工作流程，在检测DNA FISH信号方面效率高，准确性高，信噪比（SNR）低。我们建议SpotLearn将有助于以高通量的方式准确和健壮地检测各种DNA FISH信号，从而在单个实验中实现数百个基因组位点的可视化和定位。
DNA fluorescence in situ hybridization (FISH) is the technique of choice to map the position of genomic loci in three-dimensional (3D) space at the single allele level in the cell nucleus. High-throughput DNA FISH methods have recently been developed using complex libraries of fluorescently labeled synthetic oligonucleotides and automated fluorescence microscopy, enabling large-scale interrogation of genomic organization. Although the FISH signals generated by high-throughput methods can, in principle, be analyzed by traditional spot-detection algorithms, these approaches require user intervention to optimize each interrogated genomic locus, making analysis of tens or hundreds of genomic loci in a single experiment prohibitive. We report here the design and testing of two separate machine learning–based workflows for FISH signal detection in a high-throughput format. The two methods rely on random forest (RF) classification or convolutional neural networks (CNNs), respectively. Both workflows detect DNA FISH signals with high accuracy in three separate fluorescence microscopy channels for tens of independent genomic loci, without the need for manual parameter value setting on a per locus basis. In particular, the CNN workflow, which we named SpotLearn, is highly efficient and accurate in the detection of DNA FISH signals with low signal-to-noise ratio (SNR). We suggest that SpotLearn will be useful to accurately and robustly detect diverse DNA FISH signals in a high-throughput fashion, enabling the visualization and positioning of hundreds of genomic loci in a single experiment.
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DOI：
10.1126/science.1202142
发表时间：
2011-04-22
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
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通讯作者：
Singer RH
影响因子：
10.5
作者：
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影响因子：
4.8
作者：
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通讯作者：
DICE, LR
DOI：
10.1109/34.709601
发表时间：
1998-08-01
影响因子：
23.6
作者：
Ho, TK
通讯作者：
Ho, TK
影响因子：
4.5
作者：
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Wu CT