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1.
Bioinformatics ; 38(5): 1463-1464, 2022 02 07.
Artigo em Inglês | MEDLINE | ID: mdl-34864914

RESUMO

MOTIVATION: Dendrogram is a classical diagram for visualizing binary trees. Although efficient to represent hierarchical relations, it provides limited space for displaying information on the leaf elements, especially for large trees. RESULTS: Here, we present TreeAndLeaf, an R/Bioconductor package that implements a hybrid layout strategy to represent tree diagrams with focus on the leaves. The TreeAndLeaf package combines force-directed graph and tree layout algorithms using a single visualization system, allowing projection of multiple layers of information onto a graph-tree diagram. The Supplementary Information provides two case studies that use breast cancer data from epidemiological and experimental studies. AVAILABILITY AND IMPLEMENTATION: TreeAndLeaf is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/TreeAndLeaf/ (version≥1.4.2). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Neoplasias da Mama , Software , Humanos , Feminino , Algoritmos , Idioma
2.
Bioinformatics ; 35(24): 5357-5358, 2019 12 15.
Artigo em Inglês | MEDLINE | ID: mdl-31250887

RESUMO

MOTIVATION: Transcription factors (TFs) are key regulators of gene expression, and can activate or repress multiple target genes, forming regulatory units, or regulons. Understanding downstream effects of these regulators includes evaluating how TFs cooperate or compete within regulatory networks. Here we present RTNduals, an R/Bioconductor package that implements a general method for analyzing pairs of regulons. RESULTS: RTNduals identifies a dual regulon when the number of targets shared between a pair of regulators is statistically significant. The package extends the RTN (Reconstruction of Transcriptional Networks) package, and uses RTN transcriptional networks to identify significant co-regulatory associations between regulons. The Supplementary Information reports two case studies for TFs using the METABRIC and TCGA breast cancer cohorts. AVAILABILITY AND IMPLEMENTATION: RTNduals is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNduals/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Software , Expressão Gênica , Redes Reguladoras de Genes , Regulon , Fatores de Transcrição
3.
Bioinformatics ; 35(21): 4488-4489, 2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-30923832

RESUMO

MOTIVATION: Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes. RESULTS: We describe RTNsurvival, an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis. AVAILABILITY AND IMPLEMENTATION: RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Software , Expressão Gênica , Redes Reguladoras de Genes , Probabilidade , Análise de Sobrevida
4.
Nat Genet ; 48(1): 12-21, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26618344

RESUMO

Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.


Assuntos
Neoplasias da Mama/genética , Redes Reguladoras de Genes , Locos de Características Quantitativas , Fatores de Transcrição/genética , Neoplasias da Mama/mortalidade , Linhagem Celular Tumoral , Imunoprecipitação da Cromatina/métodos , Análise por Conglomerados , Receptor alfa de Estrogênio/genética , Feminino , Fator 10 de Crescimento de Fibroblastos/genética , Fator 10 de Crescimento de Fibroblastos/metabolismo , Perfilação da Expressão Gênica/métodos , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Mutação , Reprodutibilidade dos Testes
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