2016
DOI: 10.1093/bioinformatics/btw325
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GenVisR: Genomic Visualizations in R

Abstract: Summary: Visualizing and summarizing data from genomic studies continues to be a challenge. Here, we introduce the GenVisR package to addresses this challenge by providing highly customizable, publication-quality graphics focused on cohort level genome analyses. GenVisR provides a rapid and easy-to-use suite of genomic visualization tools, while maintaining a high degree of flexibility by leveraging the abilities of ggplot2 and Bioconductor.Availability and Implementation: GenVisR is an R package available via… Show more

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Cited by 271 publications
(198 citation statements)
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“…Visualization created using GenVisR. 41 (Inset) Histogram depicts the distribution of expected total histone gene mutation co-occurrences from 10 000 randomly permutated datasets with respect to the observed total co-occurrence in this cohort indicated by a red line (estimated P value , .0001). Although some patients had more than 1 mutation per histone gene, as indicated at the top, genes were considered mutated or not mutated for co-occurrence analysis.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Visualization created using GenVisR. 41 (Inset) Histogram depicts the distribution of expected total histone gene mutation co-occurrences from 10 000 randomly permutated datasets with respect to the observed total co-occurrence in this cohort indicated by a red line (estimated P value , .0001). Although some patients had more than 1 mutation per histone gene, as indicated at the top, genes were considered mutated or not mutated for co-occurrence analysis.…”
Section: Discussionmentioning
confidence: 99%
“…For individuals with multiple samples, the union of mutations in all samples for that individual was used. The mutation waterfall plot was created using the "GenVisR" package in R. 41 …”
Section: /2mentioning
confidence: 99%
“…The SNP and RGA density/distribution plots were generated using karyoploteR v1.4.2 (Gel and Serra, ). Waterfall plots were drawn using Variant Effect Predictor v88.13 (McLaren et al ., ), GenVisR v1.11.3 (Skidmore et al ., ), vcftools v0.1.15 (Danecek et al ., ) and R 3.4.4 (R Core Team, ).…”
Section: Methodsmentioning
confidence: 99%
“…Allele frequencies for a set of 24 ‘identity’ SNPs were analyzed to verify that no sample studied was significantly contaminated by DNA from an unrelated sample, (47) and were plotted using the GenVisR package (http://biorxiv.org/content/early/2016/03/25/043604) (Figure 4B). (48) Pairwise comparisons were each performed twice, by swapping the control and variable sample, resulting in a total of 20 comparisons (Supplementary Appendix, Figure 3A). …”
Section: Iii) Materials and Methodsmentioning
confidence: 99%