A segmental maximum a posteriori approach to genome-wide copy number profiling

Robin Andersson; Carl E G Bruder; Arkadiusz Piotrowski; Uwe Menzel; Helena Nord; Johanna Sandgren; Torgeir R Hvidsten; Teresita Diaz de Ståhl; Jan P Dumanski; Jan Komorowski

doi:10.1093/bioinformatics/btn003

A segmental maximum a posteriori approach to genome-wide copy number profiling

Robin Andersson, Carl E G Bruder, Arkadiusz Piotrowski, Uwe Menzel, Helena Nord, Johanna Sandgren, Torgeir R Hvidsten, Teresita Diaz de Ståhl, Jan P Dumanski, Jan Komorowski

Research output: Contribution to journal › Journal article › Research › peer-review

27 Citations (Scopus)

Abstract

Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis.

Original language	English
Journal	Bioinformatics (Online)
Volume	24
Issue number	6
Pages (from-to)	751-8
Number of pages	8
ISSN	1367-4811
DOIs	https://doi.org/10.1093/bioinformatics/btn003
Publication status	Published - 15 Mar 2008
Externally published	Yes

Keywords

Algorithms
Artificial Intelligence
Base Sequence
Chromosome Mapping
Gene Dosage
Gene Expression Profiling
Markov Chains
Molecular Sequence Data
Oligonucleotide Array Sequence Analysis
Pattern Recognition, Automated

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Cite this

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title = "A segmental maximum a posteriori approach to genome-wide copy number profiling",

abstract = "Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis.",

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AU - Andersson, Robin

AU - Bruder, Carl E G

AU - Piotrowski, Arkadiusz

AU - Menzel, Uwe

AU - Nord, Helena

AU - Sandgren, Johanna

AU - Hvidsten, Torgeir R

AU - Diaz de Ståhl, Teresita

AU - Dumanski, Jan P

AU - Komorowski, Jan

PY - 2008/3/15

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AB - Copy number profiling methods aim at assigning DNA copy numbers to chromosomal regions using measurements from microarray-based comparative genomic hybridizations. Among the proposed methods to this end, Hidden Markov Model (HMM)-based approaches seem promising since DNA copy number transitions are naturally captured in the model. Current discrete-index HMM-based approaches do not, however, take into account heterogeneous information regarding the genomic overlap between clones. Moreover, the majority of existing methods are restricted to chromosome-wise analysis.

KW - Algorithms

KW - Artificial Intelligence

KW - Base Sequence

KW - Chromosome Mapping

KW - Gene Dosage

KW - Gene Expression Profiling

KW - Markov Chains

KW - Molecular Sequence Data

KW - Oligonucleotide Array Sequence Analysis

KW - Pattern Recognition, Automated

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DO - 10.1093/bioinformatics/btn003

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EP - 758

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

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