BPG: Seamless, automated and interactive visualization of scientific data

P’ng, Christine; Green, Jeffrey; Chong, Lauren C.; Waggott, Daryl; Prokopec, Stephenie D.; Shamsi, Mehrdad; Nguyen, Francis; Mak, Davina; Lam, Felix; Albuquerque, Marco A.; Wu, Yaoguo; Jung, Esther; Starmans, Maud H.W.; Chan-Seng-Yue, Michelle; Yao, Cindy Q.; Liang, Bianca; Lalonde, Emilie; Haider, Syed; Simone, Nicole A.; Sendorek, Dorota H.; Chu, Kenneth C.; Moon, Nathalie C.; Fox, Natalie S.; Grzadkowski, Michal R.; Harding, Nicholas J.; Fung, Clement; Murdoch, Amanda R.; Houlahan, Kathleen E.; Wang, Jianxin; Garcia, David R.; Borja, Richard de; Sun, Ren; Lin, Xihui; Chen, Gregory M.; Lu, Aileen; Shiah, Yu-Jia; Zia, Amin; Kearns, Ryan; Boutros, Paul C.

doi:10.1186/s12859-019-2610-2

Cited by 71 publications

(41 citation statements)

References 31 publications

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“…All plotting was performed in the R statistical environment (v3.4.3) using the lattice (v0.20–38), latticeExtra (v0.6–28), RColorBrewer (v1.1–2) and cluster (v2.0.7–1) packages via the BPG plotting framework 43 (v5.9.8).…”

Section: Methodsmentioning

confidence: 99%

Landscape of transcriptomic interactions between breast cancer and its microenvironment

et al. 2019

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Solid tumours comprise mixtures of tumour cells (TCs) and tumour-adjacent cells (TACs), and the intricate interconnections between these diverse populations shape the tumour’s microenvironment. Despite this complexity, clinical genomic profiling is typically performed from bulk samples, without distinguishing TCs from TACs. To better understand TC–TAC interactions, we computationally distinguish their transcriptomes in 1780 primary breast tumours. We show that TC and TAC mRNA abundances are divergently associated with clinical phenotypes, including tumour subtypes and patient survival. These differences reflect distinct responses of TCs and TACs to specific somatic driver mutations, particularly TP53 . These data further elucidate how the molecular interplay between breast tumours and their microenvironment drives aggressive tumour phenotypes.

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Section: Methodsmentioning

confidence: 99%

Landscape of transcriptomic interactions between breast cancer and its microenvironment

et al. 2019

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“…An updated mapping of probes to Entrez Gene IDs was performed using the mogene20stmmentrezgcdf (v22.0.0) package . Visualizations were generated using the BPG package in R (v5.9.1) . A minimum expression threshold was identified as the level at which chromosome Y probes were expressed in control female samples and Nat2 in KO mice.…”

Section: Methodsmentioning

confidence: 99%

A readout of metabolic efficiency in arylamine N‐acetyltransferase‐deficient mice reveals minor energy metabolism changes

et al. 2019

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Recent studies have revealed a possible link between the activities of polymorphic arylamine N‐acetyltransferases (NATs) and energy metabolism. We used a Nat1/Nat2 double knockout (KO) mouse model to demonstrate that ablation of the two Nat genes is associated with modest, intermittent alterations in respiratory exchange rate. Pyruvate tolerance tests show that double KO mice have attenuated hepatic gluconeogenesis when maintained on a high‐fat/high‐sucrose diet. Absence of the two Nat genes also leads to an increase in the hepatic concentration of coenzyme A in mice fed a high‐fat/high‐sucrose diet. Our results suggest a modest involvement of NAT in energy metabolism in mice, which is consistent with the absence of major phenotypic deregulation of energy metabolism in slow human acetylators.

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“…All statistical analyses and data visualization were performed in the R statistical environment (v3.2.1) using the BPG 66…”

Section: Statistical Analysis and Data Visualization Softwarementioning

confidence: 99%

Ancestry Influences on the Molecular Presentation of Tumours

Haider

Boutros

2020

Preprint

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Epidemiological studies have identified innumerable ways in which cancer presentation and behaviour is associated with patient ancestry. The molecular bases for these relationships remain largely unknown. We analyzed ancestry associations in the somatic mutational landscape of 12,774 tumours across 33 tumour-types, including 2,562 with whole-genome sequencing. Ancestry influences both the number of mutations in a tumour and the evolutionary timing of when they occur. Specific mutational signatures are associated with ancestry, reflecting potential differences in exogenous and endogenous oncogenic processes. A subset of known cancer driver genes was mutated in ancestry-associated patterns, with transcriptomic consequences. Cancer genome sequencing data is not well-balanced in epidemiologic factors; these data suggest ancestry strongly shapes the somatic mutational landscape of cancer, with potential functional implications.

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BPG: Seamless, automated and interactive visualization of scientific data

Cited by 71 publications

References 31 publications

Landscape of transcriptomic interactions between breast cancer and its microenvironment

Landscape of transcriptomic interactions between breast cancer and its microenvironment

A readout of metabolic efficiency in arylamine N‐acetyltransferase‐deficient mice reveals minor energy metabolism changes

Ancestry Influences on the Molecular Presentation of Tumours

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