visualizing mutational data using Maftools for reference genome hg19
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13 months ago

Dear all,

I am trying to do somatic variation analysis using TCGABiolinks package in R 3.6.

I want to use hg19 version of human genome. However I could not find any way to visualize the results using Maftools. It seems Maftools has only been designed for hg38.

The following is the punch of codes I used for data visualization by Maftools:

 library(maftools)
    library(dplyr)

    maf <- GDCquery_Maf("KIRP", pipelines = "mutect") %>% read.maf

    datatable(getSampleSummary(maf),
              filter = 'top',
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5000), 
              rownames = FALSE)

    plotmafSummary(maf = maf, rmOutlier = TRUE, addStat = 'median', dashboard = TRUE)

    oncoplot(maf = maf, top = 20, removeNonMutated = TRUE)
    titv = titv(maf = maf, plot = FALSE, useSyn = FALSE)
    #plot titv summary
    plotTiTv(res = titv)

Can you guide me how to change this code to be used for hg19?

I am looking forward to your comments

Nazanin

hg19 visualize Maftools SNV • 556 views
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Default in maftools is hg19 unless you change that

Only gisticChromPlot uses chromosome length information for plotting. Default is hg19 and can be changed with ref.build argument.

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Hi, After running "maf <- GDCquery_Maf("CESC", pipelines = "mutect") %>% read.maf", I get this :

-

-------------------------------------
o GDCquery: Searching in GDC database
--------------------------------------
Genome of reference: hg38
--------------------------------------------
oo Accessing GDC. This might take a while...
--------------------------------------------
ooo Project: TCGA-CESC
--------------------
oo Filtering results
--------------------
ooo By access
ooo By data.type
ooo By workflow.type
----------------
oo Checking data
----------------
ooo Check if there are duplicated cases
ooo Check if there results for the query
-------------------
o Preparing output
-------------------

In line 2 you can see the version of genome reference is hg38

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Actually you don't need this

I suggest you may just download maf file for cancer of your interest here

enter image description here

You then use merge_maf function from Maftools itself to read what you have downloaded (click on the mutation annotation ) the files name is gdac.broadinstitute.org_CESC.Mutation_Packager_Oncotated_Calls.Level_3.2016012800.0.0

Default in maftoools is hg19

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