Cargando…

Inference of 3D genome architecture by modeling overdispersion of Hi-C data

MOTIVATION: We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to i...

Descripción completa

Detalles Bibliográficos
Autores principales: Varoquaux, Nelle, Noble, William S, Vert, Jean-Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857972/
https://www.ncbi.nlm.nih.gov/pubmed/36594573
http://dx.doi.org/10.1093/bioinformatics/btac838
_version_ 1784873982247305216
author Varoquaux, Nelle
Noble, William S
Vert, Jean-Philippe
author_facet Varoquaux, Nelle
Noble, William S
Vert, Jean-Philippe
author_sort Varoquaux, Nelle
collection PubMed
description MOTIVATION: We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. RESULTS: We first confirm the presence of overdispersion in several real Hi-C datasets, and we show that the overdispersion arises even in simulated datasets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms, both MDS-based and statistical methods. We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. AVAILABILITY AND IMPLEMENTATION: A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
format Online
Article
Text
id pubmed-9857972
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-98579722023-01-23 Inference of 3D genome architecture by modeling overdispersion of Hi-C data Varoquaux, Nelle Noble, William S Vert, Jean-Philippe Bioinformatics Original Paper MOTIVATION: We address the challenge of inferring a consensus 3D model of genome architecture from Hi-C data. Existing approaches most often rely on a two-step algorithm: first, convert the contact counts into distances, then optimize an objective function akin to multidimensional scaling (MDS) to infer a 3D model. Other approaches use a maximum likelihood approach, modeling the contact counts between two loci as a Poisson random variable whose intensity is a decreasing function of the distance between them. However, a Poisson model of contact counts implies that the variance of the data is equal to the mean, a relationship that is often too restrictive to properly model count data. RESULTS: We first confirm the presence of overdispersion in several real Hi-C datasets, and we show that the overdispersion arises even in simulated datasets. We then propose a new model, called Pastis-NB, where we replace the Poisson model of contact counts by a negative binomial one, which is parametrized by a mean and a separate dispersion parameter. The dispersion parameter allows the variance to be adjusted independently from the mean, thus better modeling overdispersed data. We compare the results of Pastis-NB to those of several previously published algorithms, both MDS-based and statistical methods. We show that the negative binomial inference yields more accurate structures on simulated data, and more robust structures than other models across real Hi-C replicates and across different resolutions. AVAILABILITY AND IMPLEMENTATION: A Python implementation of Pastis-NB is available at https://github.com/hiclib/pastis under the BSD license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2023-01-03 /pmc/articles/PMC9857972/ /pubmed/36594573 http://dx.doi.org/10.1093/bioinformatics/btac838 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Varoquaux, Nelle
Noble, William S
Vert, Jean-Philippe
Inference of 3D genome architecture by modeling overdispersion of Hi-C data
title Inference of 3D genome architecture by modeling overdispersion of Hi-C data
title_full Inference of 3D genome architecture by modeling overdispersion of Hi-C data
title_fullStr Inference of 3D genome architecture by modeling overdispersion of Hi-C data
title_full_unstemmed Inference of 3D genome architecture by modeling overdispersion of Hi-C data
title_short Inference of 3D genome architecture by modeling overdispersion of Hi-C data
title_sort inference of 3d genome architecture by modeling overdispersion of hi-c data
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9857972/
https://www.ncbi.nlm.nih.gov/pubmed/36594573
http://dx.doi.org/10.1093/bioinformatics/btac838
work_keys_str_mv AT varoquauxnelle inferenceof3dgenomearchitecturebymodelingoverdispersionofhicdata
AT noblewilliams inferenceof3dgenomearchitecturebymodelingoverdispersionofhicdata
AT vertjeanphilippe inferenceof3dgenomearchitecturebymodelingoverdispersionofhicdata