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Related Experiment Video

Updated: Jul 3, 2026

Quantitation and Analysis of the Formation of HO-Endonuclease Stimulated Chromosomal Translocations by Single-Strand Annealing in Saccharomyces cerevisiae

Quantitation and Analysis of the Formation of HO-Endonuclease Stimulated Chromosomal Translocations by Single-Strand Annealing in Saccharomyces cerevisiae

Published on: September 23, 2011

Translocation detection from Hi-C data via scan statistics.

Anthony Cheng^1,2,3, Disheng Mao⁴, Yuping Zhang⁴

¹Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, Massachusetts, USA.

|July 21, 2022

Summary

This summary is machine-generated.

TranScan, a new method using scan statistics, effectively detects interchromosomal translocations from Hi-C data. It outperforms existing methods in simulations and real data analyses, offering high power and low error rates for chromosomal variation detection.

Keywords:

anomaly detection false discovery exceedance hot-spot(s) detection scan clustering

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Quantitation and Analysis of the Formation of HO-Endonuclease Stimulated Chromosomal Translocations by Single-Strand Annealing in Saccharomyces cerevisiae

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Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C

Published on: October 14, 2022

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Hi-C technology provides insights into chromosomal conformation.
Detecting structural variations, particularly translocations, is crucial for understanding genome stability.
Existing methods for translocation detection using Hi-C data have limitations.

Purpose of the Study:

To develop a novel computational method for detecting interchromosomal translocations using Hi-C data.
To formulate translocation detection as a spatial point process scan clustering problem.
To evaluate the performance of the new method against existing approaches.

Main Methods:

Developed TranScan, a method utilizing scan statistics for translocation detection.
Incorporated false discovery rate control within the TranScan framework.
Utilized spatial point process modeling for interchromosomal translocation analysis.

Main Results:

TranScan demonstrated superior power compared to existing scan clustering methods in simulations, especially under strong signals.
Evaluation on realistic breakpoint simulations showed TranScan has better discriminative power (ROC curve analysis).
Power analysis indicated consistent outperformance across varying sequencing depths and heterozygosity rates, with the lowest Type I error rate on normal cell lines.

Conclusions:

TranScan is a powerful and robust method for interchromosomal translocation detection using Hi-C data.
The method shows significant potential for improving the analysis of structural variations in genomic research.
TranScan offers enhanced accuracy and reliability in identifying chromosomal abnormalities from Hi-C datasets.