Package 'vcf2mafR'

Convert cDNA_position to numeric indication of transcript length

Description

This function just pulls out digits from the cDNA_position column. Note transcript length isn't encoded into cDNA position column UNLESS –total_length flag is on when running vep (turns format into cdna_position/total_length) It is not possible to turn this flag on using VEP online. If we can't find total length - we just return 0, so for online version we just won't have a transcript sort.

Usage

cdna_to_transcript_length(cdna)

Arguments

cdna	vector representing cDNA_position VEP annotations.

Value

numeric transcript length passed from VEP cDNA_position

---

Convert VCF-like data.frame to MAF

Description

Converts a VEP-like (chr-pos-ref-alt-consequence-sample style) dataframe with to a minimal MAF dataframe. Input dataframe should include the columns:

• chr

• pos (1-based)

• sample

• ref

• alt

• consequence (Sequence Ontology terms)

• gene

Usage

df2maf(
  data,
  ref_genome,
  keep_all = TRUE,
  col_chrom = "chr",
  col_pos = "pos",
  col_sample_identifier = "sample",
  col_ref = "ref",
  col_alt = "alt",
  col_consequence = "consequence",
  consequence_dictionary = c("SO", "PAVE", "AUTO"),
  missing_to_silent = FALSE,
  col_gene = "gene",
  col_center = NULL,
  col_entrez_gene_id = NULL,
  col_dbSNP_rsid = NULL,
  col_dbSNP_validation_status = NULL,
  col_matched_normal_sample_identifier = NULL,
  col_sequencer = NULL,
  col_sequence_source = NULL,
  col_mutation_status = NULL,
  verbose = TRUE
)

Arguments

data	a data.frame with 1 row per mutation in a cohort (data.frame)
ref_genome	name of the reference genome used to call variants (string)
keep_all	keep all columns in the original data.frame? (flag). If FALSE, only includes the minimal required columns and any column explicitly mapped using ⁠col_<name>⁠ arguments.
col_chrom	name of column describing chromosome of the mutation (string)
col_pos	name of column describing the 1-based position of the mutation (string)
col_sample_identifier	name of column describing the sample containing the mutation (string)
col_ref	name of column describing the reference allele (string)
col_alt	name of column describing the alternate allele (string)
col_consequence	name of column describing the consequence of the mutation (in SO ontology terms e.g. those that VEP would use OR PAVE ontology terms) containing the mutation (string)
consequence_dictionary	What dictionary is to describe variant consequences (SO / PAVE / etc)
missing_to_silent	Assyne any missing (NA) or empty (”) consequences are 'Silent' mutations
col_gene	name of column containing Hugo_Symbol of the gene affected by the mutation (string)
col_center	name of column containing the genome sequencing center reporting the variant (string)
col_entrez_gene_id	name of column containing entrez gene IDs (string)
col_dbSNP_rsid	name of column describing the dbSNP rsid of the variant, or "novel" if there is no dbSNP record (string)
col_dbSNP_validation_status	name of column describing the validation status of the variant. Elements must be one of by1000genomes;by2Hit2Allele; byCluster; byFrequency; byHapMap; byOtherPop; bySubmitter; alternate_allele (string)
col_matched_normal_sample_identifier	name of column describing the matched normal sample identifier
col_sequencer	name of column describing the instrument used to produce data (string)
col_sequence_source	name of column describing the molecular assay type used to produce the analytes used for sequencing (string). Elements are usually one of 'WGS', 'WGA', 'WXS', 'RNA-seq', etc
col_mutation_status	name of column describing the mutation status (string). Elements must be one of: None, Germline, Somatic, LOH, Post-transcriptional modification, or Unknown
verbose	verbose (flag)

Details

Alternate column names can be used, but require col_chrom, col_pos, col_ arguments to be supplied.

Value

a maf-like data.frame (data.table)

Examples

# Start with a vcf/maf-like 'chr-pos-ref-alt-consequence-sample' data.frame
df = read.csv(system.file(package = "vcf2mafR", "testfiles/test_so.tsv"), sep = "\t")

# Convert to a MAF dataframe
df2maf(df, ref_genome = "hg38")

---

Extract Sample Names From VCF filepath

Description

Extract Sample Names From VCF filepath

Usage

paths_to_sample_names(
  filenames,
  extract = c("before_dot", "before_underscore")
)

Arguments

filenames	paths to vcf files
extract	what text do we extract as sample name. See details

Details

Extraction	Explanation
before_dot	sample_name.otherinfo.vcf
before_underscore	samplename_other_info.vcf

Value

sample names extracted from filename (character)

Examples

path <- "/path/to/sample_name.otherinfo.vcf"
paths_to_sample_names(path)

---

Maf Column Names

Description

List all valid MAF columns based on GDC specification

Usage

valid_maf_columns()

Value

Names of each GDC MAF column in the order they appear (character)

Examples

valid_maf_columns()

---

Convert a vepped VCF into a table ready to be passed into df2maf

Description

Expects a vepped VCF. VEP must be run with a couple of options:

1. Identifiers: Gene symbol & Transcript Version & (HGVS: optional)

2. Transcript annotation: Transcript biotype & 'Identify canonical transcripts'

3. (Optional) If using the commandline version of vep also make sure to use –total_length (helps in transcript choice)

Usage

vcf2df(
  vcf,
  tumor_id = vcf_tumor_id,
  normal_id = vcf_normal_id,
  vcf_tumor_id = "TUMOR",
  vcf_normal_id = "NORMAL",
  debug_mode = FALSE,
  verbose = TRUE
)

Arguments

vcf	path to a VEP-annotated VCF file
tumor_id	desired value of Tumor_Sample_Barcode in maf. By default will use the name of the tumor sample in the VCF (string)
normal_id	desired value of Matched_Norm_Sample_Barcode in maf. By default will use the name of the normal sample in the VCF (string)
vcf_tumor_id	the sample ID describing the tumor in the (string)
vcf_normal_id	the sample ID describing the normal in the (string)
debug_mode	run in debug mode (flag)
verbose	(flag)

Details

There are a couple of different types of inputs we supp

Value

data.frame

Examples

path_vcf <- system.file(package = "vcf2mafR", "testfiles/test_b38.vepgui.vcf")
vcf2df(path_vcf)

---

Convert Annotated VCF to MAF-compatible data.frame

Description

Expects a vepped VCF. VEP must be run with a couple of options:

1. Identifiers: Gene symbol & Transcript Version & (HGVS: optional)

2. Transcript annotation: Transcript biotype & 'Identify canonical transcripts'

3. (Optional) If using the commandline version of vep also make sure to use –total_length (helps in transcript choice)

Usage

vcf2maf(
  vcf,
  ref_genome,
  tumor_id = vcf_tumor_id,
  normal_id = vcf_normal_id,
  vcf_tumor_id = "TUMOR",
  vcf_normal_id = "NORMAL",
  missing_to_silent = TRUE,
  verbose = TRUE,
  debug_mode = FALSE
)

Arguments

vcf	path to a VEP-annotated VCF file
ref_genome	name of the reference genome used to call variants (string)
tumor_id	desired value of Tumor_Sample_Barcode in maf. By default will use the name of the tumor sample in the VCF (string)
normal_id	desired value of Matched_Norm_Sample_Barcode in maf. By default will use the name of the normal sample in the VCF (string)
vcf_tumor_id	the sample ID describing the tumor in the (string)
vcf_normal_id	the sample ID describing the normal in the (string)
missing_to_silent	Assyne any missing (NA) or empty (”) consequences are 'Silent' mutations
verbose	(flag)
debug_mode	run in debug mode (flag)

Value

a maf compatible data.frame

Examples

path_vcf_vepped <- system.file("testfiles/test_b38.vepgui.vcf", package = "vcf2mafR")
vcf2maf(vcf = path_vcf_vepped, ref_genome = "b38")

---

Convert several VCF files into a cohort-level MAF

Description

Take multiple VCF files, each representing a single tumour-normal file and combine into one big maf

Usage

vcfs2maf(
  vcfs,
  ref_genome,
  tumor_id = paste0("Tumor", seq_len(length(vcfs)), times = length(vcfs)),
  vcf_tumor_id = "TUMOR",
  vcf_normal_id = "NORMAL",
  parse_tumor_id_from_filename = TRUE,
  extract = c("before_dot", "before_underscore"),
  verbose = TRUE
)

Arguments

vcfs	path to vcf files (character)
ref_genome	name of the reference genome used to call variants (string)
tumor_id	desired value of Tumor_Sample_Barcode in maf. By default will use the name of the tumor sample in the VCF (string)
vcf_tumor_id	a vector describing what the tumor_names to expect in a VCF are
vcf_normal_id	the sample ID describing the normal in the (string)
parse_tumor_id_from_filename	should tumour id be parsed from filename (flag)
extract	what text do we extract as sample name. See details
verbose	(flag)

Value

a maf-compatible data.frame

Examples

vcf_filepaths = dir(system.file(package='vcf2mafR', 'testfiles/cohort_of_vcfs/'), full.names = TRUE)
vcfs2maf(vcf_filepaths, ref_genome = "b38")

---

Rank VEP biotypes

Description

This function takes a vector of VEP annotated biotypes and returns their ranks based on priority.

Usage

vep_rank_biotypes(biotypes)

Arguments

biotypes

A character vector of VEP annotated biotypes.

Value

A numeric vector where values represent the priority of each biotype (lower number = higher priority).

Examples

vep_rank_biotypes(c("protein_coding", "miRNA", "pseudogene"))

---

Rank VEP consequences

Description

This function takes a vector of VEP annotated biotypes and returns their ranks based on priority. If there are multiple consequences in one string (e.g. splice_region_variant&TFBS_ablation) this function will automatically return the priority for the most severe event

Usage

vep_rank_consequences(consequences)

Arguments

consequences

A character vector of VEP annotated consequences.

Value

A numeric vector where values represent the priority of each consequence (lower number = higher priority).

Examples

vep_rank_consequences(c("protein_coding", "miRNA", "pseudogene"))

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