論文

査読有り
2011年4月

FISH Finder: a high-throughput tool for analyzing FISH images

BIOINFORMATICS
  • James W. Shirley
  • ,
  • Sereyvathana Ty
  • ,
  • Shin-ichiro Takebayashi
  • ,
  • Xiuwen Liu
  • ,
  • David M. Gilbert

27
7
開始ページ
933
終了ページ
938
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1093/bioinformatics/btr053
出版者・発行元
OXFORD UNIV PRESS

Motivation: Fluorescence in situ hybridization (FISH) is used to study the organization and the positioning of specific DNA sequences within the cell nucleus. Analyzing the data from FISH images is a tedious process that invokes an element of subjectivity. Automated FISH image analysis offers savings in time as well as gaining the benefit of objective data analysis. While several FISH image analysis software tools have been developed, they often use a threshold- based segmentation algorithm for nucleus segmentation. As fluorescence signal intensities can vary significantly from experiment to experiment, from cell to cell, and within a cell, threshold- based segmentation is inflexible and often insufficient for automatic image analysis, leading to additional manual segmentation and potential subjective bias. To overcome these problems, we developed a graphical software tool called FISH Finder to automatically analyze FISH images that vary significantly. By posing the nucleus segmentation as a classification problem, compound Bayesian classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters. Additionally, FISH Finder was designed to analyze the distances between differentially stained FISH probes.

Web of Science ® 被引用回数 : 12

リンク情報
DOI
https://doi.org/10.1093/bioinformatics/btr053
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000289162000007&DestApp=WOS_CPL
ID情報
  • DOI : 10.1093/bioinformatics/btr053
  • ISSN : 1367-4803
  • Web of Science ID : WOS:000289162000007

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