Package 'pepitope'

Annotate VCF variants with coding changes

Description

Annotate VCF variants with coding changes

Usage

annotate_coding(vr, txdb, asm)

Arguments

vr	A VRanges object with SNVs and small indels
txdb	TxDb or EnsDb object
asm	Genomic sequence BSGenome object

Value

A GRanges object with annotated variants

Examples

if (interactive()) {
    vcf = system.file("my_variants.vcf", package="pepitope")
    vr = readVcfAsVRanges(vcf)
    txdb = AnnotationHub::AnnotationHub()[["AH100643"]]
    asm = BSgenome.Hsapiens.NCBI.GRCh38::BSgenome.Hsapiens.NCBI.GRCh38
    annotate_coding(vr, txdb, asm)
}

---

Aggregate fusion VCFs into a table

Description

Aggregate fusion VCFs into a table

Usage

annotate_fusions(vr, txdb, asm)

Arguments

vr	A VRanges object with RNA fusions from readVcfAsRanges
txdb	A transcription database, eg. AnnotationHub()[["AH100643"]]
asm	A Genome sequence package object, eg. ::BSgenome.Hsapiens.NCBI.GRCh38

Value

A DataFrame objects with fusions

Examples

if (interactive()) {
    vcf = system.file("my_fusions.vcf", package="pepitope")
    vr = readVcfAsVRanges(vcf)
    txdb = AnnotationHub::AnnotationHub()[["AH100643"]]
    asm = BSgenome.Hsapiens.NCBI.GRCh38::BSgenome.Hsapiens.NCBI.GRCh38
    annotate_fusions(vr, txdb, asm)
}

---

Count barcodes directly from source FASTQ files

Description

Count barcodes directly from source FASTQ files

Usage

count_fastq(
  fq,
  samples,
  all_constructs,
  valid_barcodes,
  read_structure,
  verbose = TRUE
)

Arguments

fq	Path to one FASTQ file
samples	A sample sheet as data.frame in tsv format. Requires the columns 'sample_id', 'patient', 'rep', 'origin', 'barcode'
all_constructs	A named list of all construct libraries
valid_barcodes	A character vector of all possible construct barcodes
read_structure	A character string describing the FASTQ read structure. If missing, this will be inferred from the first reads in 'fq'.
verbose	Whether to print progress messages (default: TRUE)

Value

A SummarizedExperiment object with counts and metadata

Examples

samples = data.frame(sample_id="sample1", patient="pat1", rep="1",
    origin="library", barcode="GGG")
constructs = list(pat1=data.frame(gene_name="GENE1", mut_id="GENE1_A1V",
    pep_id="GENE1_A1V", pep_type="alt", tiled="ATGGCCGCC", barcode_1="AAAA"))
fq = tempfile(fileext=".fq")
writeLines(c("@r1", "GGGAAAA", "+", "IIIIIII", "@r2", "GGGAAAA", "+", "IIIIIII"), fq)
count_fastq(fq, samples, constructs, read_structure="3B4M", verbose=FALSE)

---

Filter a fusion VRanges object by number of reads and tools

Description

Filter a fusion VRanges object by number of reads and tools

Usage

filter_fusions(vr, min_reads = NULL, min_split_reads = NULL, min_tools = NULL)

Arguments

vr	A VRanges object with RNA fusions from readVcfAsRanges
min_reads	The minimum number of linked read support for a fusion
min_split_reads	The minimum number of split read support for a fusion
min_tools	The minimum number of tools that identify a fusion

Value

A filtered VRanges object

Examples

vr = VariantAnnotation::VRanges("chr1", IRanges::IRanges(1, 1), ref="A", alt="T")
vr$DV = 3L
vr$RV = 2L
vr$TOOL_HITS = IRanges::IntegerList(2L)
filter_fusions(vr, min_reads=4, min_split_reads=2, min_tools=1)

---

Make results report to save as xlsx sheets (full, filtered, peptides)

Description

Make results report to save as xlsx sheets (full, filtered, peptides)

Usage

filter_variants(
  vr,
  ...,
  min_cov = 2,
  min_af = 0.05,
  pass = TRUE,
  sample = NULL,
  chrs = NULL
)

Arguments

vr	A VRanges object from 'readVcfAsVRanges'
...	Force filters by name (ignored)
min_cov	Minimum number of reads to span the ALT allele
min_af	Minimum allele frequency of the ALT allele
pass	Whether to only include softFilterMatrix PASS
sample	Only include if in 'sampleNames(vr)' (required if more than one present)
chrs	Either "default" or a character vector of chromosome names

Value

A filtered VRanges object

Examples

vcf = system.file("my_variants.vcf", package="pepitope")
vr = readVcfAsVRanges(vcf)
filter_variants(vr, min_cov=2, min_af=0.05, pass=TRUE)

---

Make a variants report as named list of tables

Description

Make a variants report as named list of tables

Usage

make_peptides(subs, fus = DataFrame())

Arguments

subs	Variants within mutation context from 'subset_context()'
fus	Variants within fusion context from 'subset_context_fusions()'

Value

A data.frame with cDNA and peptide sequences

Examples

if (interactive()) {
    peptides = make_peptides(subs)
}

---

Make a variants report as named list of tables

Description

Make a variants report as named list of tables

Usage

make_report(vars, subs, fus = DataFrame(), tiled)

Arguments

vars	Variant results from 'annotate_coding()'
subs	Variants within mutation context from 'subset_context()'
fus	Variants within fusion context from 'subset_context_fusions()'
tiled	A data.frame of the tiled peptide sequences

Value

A named list of data.frames for report output

Examples

if (interactive()) {
    make_report(vars, subs, fus, tiled)
}

---

Tile cDNA into peptide sequences

Description

Tile cDNA into peptide sequences

Usage

pep_tile(peptides, tile_size = 93, tile_ov = 45)

Arguments

peptides	A 'data.frame' with context-subset peptide/minigene data
tile_size	Oligo tiling size
tile_ov	Oligo tiling overlap

Value

A data.frame with tiled nucleotide and peptide sequences

Examples

peptides = data.frame(var_id="v1", mut_id="M1_A1V", gene_name="M1",
    gene_id="gene1", tx_id="tx1", pep_id="M1_A1V", cDNA="ATGGCCGCCGCC")
pep_tile(peptides, tile_size=9, tile_ov=3)

---

Pepitope: peptide epitopes from reference genome and variant (VCF) file

Description

Given a reference genome and VCF file, this package will provide the upstream/downstream peptide context of a variant. It will generate a summary report for protein-coding variants including the reference and mutated allele, read coverage, amino acid sequence, and other information. It can also be used to remove restriction sites from cDNA, alongside other helper functions.

Value

Package documentation for pepitope

Author(s)

Maintainer: Michael Schubert [email protected] (ORCID)

Authors:

• Michael Schubert [email protected] (ORCID)

See Also

Useful links:

• https://github.com/mschubert/pepitope

• Report bugs at https://github.com/mschubert/pepitope/issues

---

Plot barcode overlap between different samples

Description

Plot barcode overlap between different samples

Usage

plot_barcode_overlap(all_constructs, valid_barcodes)

Arguments

all_constructs	A named list of all constructs
valid_barcodes	A character vector of possible barcodes (optional)

Value

A ggplot2 object

Examples

if (interactive()) {
    constructs = list(pat1=data.frame(gene_name="GENE1", mut_id="GENE1_A1V",
        pep_id="GENE1_A1V", pep_type="alt", tiled="ATGGCCGCC", barcode_1="AAAA"))
    plot_barcode_overlap(constructs, valid_barcodes=c("AAAA", "CCCC"))
}

---

Plot the overall read counts

Description

Plot the overall read counts

Usage

plot_read_count(dset, n_cutoff = 10)

plot_reads(dset)

Arguments

dset	The 'SummarizedExperiment' object from 'count_fastq'
n_cutoff	Minimum reads for counting a barcode as represented

Value

A patchwork object containing the read count summary plots

Examples

if (interactive()) {
    samples = data.frame(sample_id="sample1", patient="pat1", rep="1",
        origin="library", barcode="GGG")
    constructs = list(pat1=data.frame(gene_name="GENE1", mut_id="GENE1_A1V",
        pep_id="GENE1_A1V", pep_type="alt", tiled="ATGGCCGCC", barcode_1="AAAA"))
    fq = tempfile(fileext=".fq")
    writeLines(c("@r1", "GGGAAAA", "+", "IIIIIII", "@r2", "GGGAAAA", "+", "IIIIIII"), fq)
    dset = count_fastq(fq, samples, constructs, read_structure="3B4M", verbose=FALSE)
    plot_read_count(dset)
}

---

Plot the read distribution across barcodes

Description

Plot the read distribution across barcodes

Usage

plot_read_distr(dset)

plot_distr(dset)

Arguments

dset	The 'SummarizedExperiment' object from 'count_fastq'

Value

A 'ggplot2' object with cumulative read distribution plots

Examples

if (interactive()) {
    samples = data.frame(sample_id="sample1", patient="pat1", rep="1",
        origin="library", barcode="GGG")
    constructs = list(pat1=data.frame(gene_name="GENE1", mut_id="GENE1_A1V",
        pep_id="GENE1_A1V", pep_type="alt", tiled="ATGGCCGCC", barcode_1="AAAA"))
    fq = tempfile(fileext=".fq")
    writeLines(c("@r1", "GGGAAAA", "+", "IIIIIII", "@r2", "GGGAAAA", "+", "IIIIIII"), fq)
    dset = count_fastq(fq, samples, constructs, read_structure="3B4M", verbose=FALSE)
    plot_read_distr(dset)
}

---

Plot annotated read structure examples

Description

Plot annotated read structure examples

Usage

plot_read_structure(fq, samples, all_constructs, nrec = 100000L)

Arguments

fq	Path to one FASTQ file
samples	A sample sheet as 'data.frame' in tsv format. Requires the columns 'sample_id', 'patient', 'rep', 'origin', 'barcode'
all_constructs	A named list of all construct libraries
nrec	Number of FASTQ records to inspect

Value

A 'ggplot2' object

Examples

if (interactive()) {
    samples = data.frame(sample_id="sample1", patient="pat1", rep="1",
        origin="library", barcode="GGG")
    constructs = list(pat1=data.frame(gene_name="GENE1", mut_id="GENE1_A1V",
        pep_id="GENE1_A1V", pep_type="alt", tiled="ATGGCCGCC", barcode_1="AAAA"))
    fq = tempfile(fileext=".fq")
    writeLines(c("@r1", "GGGAAAA", "+", "IIIIIII", "@r2", "GGGAAAA", "+", "IIIIIII"), fq)
    plot_read_structure(fq, samples, constructs, nrec=10)
}

---

Plot screen results

Description

Plot screen results

Usage

plot_screen(
  res,
  sample = NULL,
  links = TRUE,
  labs = TRUE,
  min_reads = 10,
  cap_fc = 8,
  p_sig = 0.05
)

Arguments

res	A results 'data.frame' from 'screen_calc()'
sample	Which library to plot (default: all)
links	Whether to draw arrows between ref and significant alt peptides
labs	Whether to label genes in less dense areas
min_reads	Minimum number of reads to show as points (default: 10)
cap_fc	Maximum amount of fold-change to limit values to
p_sig	Maximum adjusted p-value to consider significant (default: 0.05)

Value

A 'ggplot2' object of the differential expression results

Examples

if (interactive()) {
    res = data.frame(baseMean=c(20, 30), log2FoldChange=c(-1, 1), padj=c(0.2, 0.05),
        gene_name=c("GENE1", "GENE1"), pep_type=c("ref", "alt"),
        bc_type=c("pat1", "pat1"), mut_id=c("GENE1_A1", "GENE1_A1"))
    plot_screen(res, links=FALSE, labs=FALSE)
}

---

Remove a Restriction Enzyme cut site but keep AA in a tiled peptide data.frame

Description

Remove a Restriction Enzyme cut site but keep AA in a tiled peptide data.frame

Usage

remove_cutsite(pep, ...)

Arguments

pep	A data.frame of tiled peptides
...	Named argumennts of cut sites, e.g. 'BbsI="GAAGAC"'

Value

A data.frame with replace nucleotides and number of replacements

Examples

pep = data.frame(pep_id="p1", tiled="ATGGCCGCC")
remove_cutsite(pep, BbsI="GAAGAC")

---

Calculate differential abundance of construct barcodes

Description

Calculate differential abundance of construct barcodes

Usage

screen_calc(dset, comparisons)

Arguments

dset	A 'SummarizedExperiment' object from 'count_fastq()'
comparisons	A character vector of sample and reference condition, or list thereof

Value

A data.frame of DESeq2 results, or a named list of data.frames

Examples

if (interactive()) {
    screen_calc(dset, c("screen", "library"))
}

---

Subset nucleotide/protein sequences to codon +/- 45 bp context

Description

Subset nucleotide/protein sequences to codon +/- 45 bp context

Usage

subset_context(codv, ctx_codons)

Arguments

codv	Annotated variants from 'annotate_coding()'
ctx_codons	How many flanking codons each to include in the context

Value

GRanges object with sequence information of only context

Examples

if (interactive()) {
    subset_context(codv, ctx_codons=15)
}

---

Subset the peptide context for gene fusions

Description

Subset the peptide context for gene fusions

Usage

subset_context_fusion(res, ctx_codons)

Arguments

res	A DataFrame object from 'fusions'
ctx_codons	How many flanking codons each to include in the context

Value

A DataFrame object of gene fusions

Examples

if (interactive()) {
    subset_context_fusion(fusions, ctx_codons=15)
}

---