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1.
Nat Commun ; 11(1): 731, 2020 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-32024834

RESUMO

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.


Assuntos
Biologia Computacional/métodos , Mutação , Neoplasias/genética , Simulação por Computador , Evolução Molecular , Frequência do Gene , Genoma Humano , Humanos , Neoplasias/patologia , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Genoma
2.
Plant Cell ; 29(8): 1806-1821, 2017 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28808136

RESUMO

A big challenge in current systems biology research arises when different types of data must be accessed from separate sources and visualized using separate tools. The high cognitive load required to navigate such a workflow is detrimental to hypothesis generation. Accordingly, there is a need for a robust research platform that incorporates all data and provides integrated search, analysis, and visualization features through a single portal. Here, we present ePlant (http://bar.utoronto.ca/eplant), a visual analytic tool for exploring multiple levels of Arabidopsis thaliana data through a zoomable user interface. ePlant connects to several publicly available web services to download genome, proteome, interactome, transcriptome, and 3D molecular structure data for one or more genes or gene products of interest. Data are displayed with a set of visualization tools that are presented using a conceptual hierarchy from big to small, and many of the tools combine information from more than one data type. We describe the development of ePlant in this article and present several examples illustrating its integrative features for hypothesis generation. We also describe the process of deploying ePlant as an "app" on Araport. Building on readily available web services, the code for ePlant is freely available for any other biological species research.


Assuntos
Botânica , Software , Estatística como Assunto , Biologia de Sistemas , Sequência de Bases , Cromossomos de Plantas/genética , Regulação da Expressão Gênica de Plantas , Frações Subcelulares/metabolismo , Interface Usuário-Computador
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