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Updated: Jun 19, 2026

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data.

Chris D Greenman¹, Graham Bignell, Adam Butler

¹Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. cdg@sanger.ac.uk

Biostatistics (Oxford, England)

|October 20, 2009

Summary

This summary is machine-generated.

We developed a new algorithm to accurately analyze cancer genomes, which are typically aneuploid. This method improves genotype classification and copy number variation analysis for cancer research.

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Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Array Comparative Genomic Hybridization (Array CGH) for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

High-throughput oligonucleotide microarrays are standard for genetic disease investigation, including cancer.
Current algorithms are optimized for diploid genomes, leading to errors in aneuploid cancer genomes.

Purpose of the Study:

To introduce a novel preprocessing transformation and hidden Markov model algorithm tailored for cancer genome analysis.
To address systematic errors in genotype and copy number variation analysis of aneuploid cancer genomes.

Main Methods:

Development of a bespoke preprocessing transformation and hidden Markov model algorithm.
Application to Affymetrix Genome-Wide SNP6.0 data from 755 cancer cell lines.
Validation using independent experimental techniques for accurate prediction.

Main Results:

The algorithm achieves accurate genotype classification.
It specifies regions of loss of heterozygosity.
It provides absolute allelic copy number segmentation for cancer genomes.

Conclusions:

The novel algorithm effectively analyzes aneuploid cancer genomes, overcoming limitations of existing methods.
This advancement enables more accurate inference of biological features from cancer genomic data.
The algorithm and associated data are publicly available for further research.