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RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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Towards automated cellular image segmentation for RNAi genome-wide screening.

Xiaobo Zhou1, K Y Liu, P Bradley

  • 1Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, 3rd floor, 1249 Boylston, Boston, MA 02215, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|May 12, 2006
PubMed
Summary
This summary is machine-generated.

This study introduces an automated image analysis method to identify cell phenotypes from genome-wide RNA interference (RNAi) screens. The approach overcomes the bottleneck of manual analysis, enabling efficient discovery of genes affecting cell morphology.

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Area of Science:

  • Cell Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Rho GTPases are crucial for cell morphology, development, migration, and cancer.
  • Genome-wide RNA interference (RNAi) screening is a powerful tool for discovering genes involved in cellular processes.
  • Manual analysis of high-content imaging data from RNAi screens is a significant bottleneck.

Purpose of the Study:

  • To develop an automated image analysis method for high-throughput cell phenotype classification in genome-wide RNAi screens.
  • To identify novel genes and pathways regulating Rho protein activity and cell morphology.

Main Methods:

  • A two-step cell segmentation approach using DNA-channel information, dissimilarity functions, and polygon estimation.
  • Fuzzy c-means based multi-threshold segmentation, sharpening, and marker-controlled watershed for cell isolation.
  • Voronoi diagrams for correcting segmentation errors, followed by feature extraction and K-nearest neighbor (KNN) classification.

Main Results:

  • The proposed automated image analysis pipeline successfully segments and classifies cell phenotypes from complex imaging data.
  • The method effectively handles challenges like over-segmentation and touching/overlapping cells.

Conclusions:

  • The developed image analysis approach significantly accelerates the discovery of genes affecting cell phenotypes in large-scale RNAi screens.
  • This method facilitates the identification of novel Rho effector genes and enhances our understanding of cell morphology regulation.