Effective normalization for copy number variation in Hi-C data.
BMC Bioinformatics
; 19(1): 313, 2018 Sep 06.
Article
em En
| MEDLINE
| ID: mdl-30189838
ABSTRACT
BACKGROUND:
Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other.RESULTS:
In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations. We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes.CONCLUSIONS:
Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs.Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Genoma Humano
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Aberrações Cromossômicas
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Mapeamento Cromossômico
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Biologia Computacional
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Genômica
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Variações do Número de Cópias de DNA
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Neoplasias
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
França