PMID- 29183987
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231005
IS  - 1943-4456 (Electronic)
IS  - 0091-7451 (Print)
IS  - 0091-7451 (Linking)
VI  - 82
DP  - 2017
TI  - SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ 
      Hybridization (FISH) Signals in High-Throughput Imaging Approaches.
PG  - 57-70
LID - 10.1101/sqb.2017.82.033761 [doi]
AB  - 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.
CI  - Published by Cold Spring Harbor Laboratory Press.
FAU - Gudla, Prabhakar R
AU  - Gudla PR
AD  - High-Throughput Imaging Facility, National Cancer Institute, National Institutes 
      of Health, Bethesda, Maryland 20892.
AD  - Cell Biology of Genomes Group, National Cancer Institute, National Institutes of 
      Health, Bethesda, Maryland 20892.
FAU - Nakayama, Koh
AU  - Nakayama K
AD  - Cell Biology of Genomes Group, National Cancer Institute, National Institutes of 
      Health, Bethesda, Maryland 20892.
AD  - Oxygen Biology Laboratory, Medical Research Institute, Tokyo Medical and Dental 
      University, Tokyo, Japan 1138510.
FAU - Pegoraro, Gianluca
AU  - Pegoraro G
AD  - High-Throughput Imaging Facility, National Cancer Institute, National Institutes 
      of Health, Bethesda, Maryland 20892.
AD  - Cell Biology of Genomes Group, National Cancer Institute, National Institutes of 
      Health, Bethesda, Maryland 20892.
FAU - Misteli, Tom
AU  - Misteli T
AD  - Cell Biology of Genomes Group, National Cancer Institute, National Institutes of 
      Health, Bethesda, Maryland 20892.
LA  - eng
GR  - ZIA BC010309-16/Intramural NIH HHS/United States
GR  - ZIA BC010309-17/Intramural NIH HHS/United States
PT  - Journal Article
DEP - 20171128
PL  - United States
TA  - Cold Spring Harb Symp Quant Biol
JT  - Cold Spring Harbor symposia on quantitative biology
JID - 1256107
PMC - PMC6350914
MID - NIHMS999233
EDAT- 2017/12/01 06:00
MHDA- 2017/12/01 06:01
PMCR- 2019/01/29
CRDT- 2017/11/30 06:00
PHST- 2017/12/01 06:00 [pubmed]
PHST- 2017/12/01 06:01 [medline]
PHST- 2017/11/30 06:00 [entrez]
PHST- 2019/01/29 00:00 [pmc-release]
AID - sqb.2017.82.033761 [pii]
AID - 10.1101/sqb.2017.82.033761 [doi]
PST - ppublish
SO  - Cold Spring Harb Symp Quant Biol. 2017;82:57-70. doi: 10.1101/sqb.2017.82.033761. 
      Epub 2017 Nov 28.