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BIMIB-DISCo / TRONCO

Repository of the TRanslational ONCOlogy library, which includes various algorithms (such as CAPRESE and CAPRI) and the Pipeline for Cancer Inference (PICNIC).
https://bimib-disco.github.io/TRONCO
GNU General Public License v3.0
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Using my own maf object as input for TRONCO #118

Closed beginner984 closed 4 years ago

beginner984 commented 4 years ago

Hi

I have made maf file from converting annovar to maf

I want to use that as an input for your software, is it possible?

head(res[1:5, 1:20])
   Tumor_Sample_Barcode Chromosome Start_Position End_Position Reference_Allele Tumor_Seq_Allele2 Hugo_Symbol Variant_Classification
1:    LP6005334-DNA_H01       chr1        1026918      1026918                C                 T    C1orf159                 Silent
2:    LP6005334-DNA_H01       chr1       15772155     15772155                G                 A        CTRC      Missense_Mutation
3:    LP6005334-DNA_H01       chr1       16054813     16054813                G                 A     PLEKHM2      Missense_Mutation
4:    LP6005334-DNA_H01       chr1       24120222     24120222                G                 A      LYPLA2      Missense_Mutation
5:    LP6005334-DNA_H01       chr1       47284367     47284367                C                 T      CYP4B1      Missense_Mutation
             tx   exon txChange aaChange Variant_Type Func.refGene Gene.refGene GeneDetail.refGene ExonicFunc.refGene
1:    NM_017891  exon3    c.G6A    p.A2A          SNP       exonic     C1orf159                  .     synonymous SNV
2:    NM_007272  exon7  c.G703A  p.V235I          SNP       exonic         CTRC                  .  nonsynonymous SNV
3:    NM_015164 exon11 c.G1882A  p.V628M          SNP       exonic      PLEKHM2                  .  nonsynonymous SNV
4:    NM_007260  exon5  c.G204A   p.M68I          SNP       exonic       LYPLA2                  .  nonsynonymous SNV
5: NM_001319162 exon10  c.C931T  p.R311C          SNP       exonic       CYP4B1                  .  nonsynonymous SNV
                                                                                                                                                                                                         AAChange.refGene
1:                                                                                                   C1orf159:NM_017891:exon3:c.G6A:p.A2A,C1orf159:NM_001330306:exon4:c.G6A:p.A2A,C1orf159:NM_001363525:exon4:c.G6A:p.A2A
2:                                                                                                                                                                                   CTRC:NM_007272:exon7:c.G703A:p.V235I
3:                                                                                                                                                                              PLEKHM2:NM_015164:exon11:c.G1882A:p.V628M
4:                                                                                                                                                                                  LYPLA2:NM_007260:exon5:c.G204A:p.M68I
5: CYP4B1:NM_001319162:exon10:c.C931T:p.R311C,CYP4B1:NM_001319163:exon10:c.C928T:p.R310C,CYP4B1:NM_001319161:exon11:c.C1375T:p.R459C,CYP4B1:NM_000779:exon12:c.C1417T:p.R473C,CYP4B1:NM_001099772:exon12:c.C1420T:p.R474C
   Func.knownGene Gene.knownGene
1:           <NA>           <NA>
2:           <NA>           <NA>
3:           <NA>           <NA>
4:           <NA>           <NA>
5:           <NA>           <NA>
>

Can you help?

Thanks

danro9685 commented 4 years ago

Dear @beginner984,

the function import.MAF can take as inputs MAF files (not directly MAF objects) either as text files or as RData. Required fields in your MAF file are 'Hugo_Symbol', 'Tumor_Sample_Barcode', 'Variant_Classification'.

Best, Daniele