bioconductor-proteodisco

name	bioconductor-proteodisco
description	ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings.

ProteoDisco

Workflows

Standard Workflow

Generate a customized protein database incorporating genomic variants (SNVs, MNVs, InDels) from VCF/MAF files.

library(ProteoDisco)

# Generate the ProteoDiscography
ProteoDiscography.hg19 <- ProteoDisco::generateProteoDiscography(
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  genomeSeqs = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19
)

# Import genomic variants
ProteoDiscography.hg19 <- ProteoDisco::importGenomicVariants(
  ProteoDiscography = ProteoDiscography.hg19,
  files = system.file('extdata', 'validationSet_hg19.vcf', package = 'ProteoDisco'),
  samplenames = 'Validation Set (GRCh37)',
  threads = 1,
  performAnchorCheck = FALSE
)

# Incorporate genomic variants into transcripts
ProteoDiscography.hg19 <- ProteoDisco::incorporateGenomicVariants(
  ProteoDiscography = ProteoDiscography.hg19,
  aggregateSamples = FALSE,
  aggregateWithinExon = TRUE,
  aggregateWithinTranscript = TRUE,
  ignoreOverlappingMutations = TRUE,
  threads = 1
)

# Export to FASTA
ProteoDisco::exportProteoDiscography(
  ProteoDiscography = ProteoDiscography.hg19,
  outFile = 'example.fasta',
  aggregateSamples = TRUE
)

Note: Inputs are a TxDb object, genome sequences, and VCF/MAF files; the output is a customized protein database in FASTA format.

Splice Junctions Workflow

Generate a customized protein database incorporating alternative splicing events from BED or custom splice junction files.

library(ProteoDisco)

# Initialize ProteoDiscography
ProteoDiscography.hg19 <- ProteoDisco::generateProteoDiscography(
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  genomeSeqs = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19
)

# Import splice junctions
ProteoDiscography.hg19 <- ProteoDisco::importSpliceJunctions(
  ProteoDiscography = ProteoDiscography.hg19,
  inputSpliceJunctions = system.file('extdata', 'spliceJunctions_pyQUILTS_chr22.bed', package = 'ProteoDisco'),
  samples = c('pyQUILTS'),
  isTopHat = TRUE,
  aggregateSamples = FALSE,
  removeExisting = TRUE
)

# Generate junction models
ProteoDiscography.hg19 <- ProteoDisco::generateJunctionModels(
  ProteoDiscography = ProteoDiscography.hg19,
  maxDistance  
  skipCanonical  
  threads  

Note: Inputs are splice junction BED files or custom tables; the output is putative splice-isoform fragments added to the ProteoDiscography.

Manual Transcripts Workflow

Import manually-curated or long-read transcript sequences to check for proteotypic peptides and export to a customized protein database.

library(ProteoDisco)

# Initialize ProteoDiscography
ProteoDiscography.hg19 <- ProteoDisco::generateProteoDiscography(
  TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene,
  genomeSeqs = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19
)

# Create manual sequences DataFrame
testSeq1 <- Biostrings::DNAString('ATGACCGCGTCCTCCTCCAGCGACTATGGACAGACTTCCAAGATGAGCCCACGCGTCCCTCAGCAGGATTGGCTGTCTCAACCCCCAGCCAGGGTCACCATCAAAATGGAATGTAACCCTAGCCAGGTGAATGGCTCAAGGAACTCTCCTGATGAATGCAGTGTGGCCAAAGGCGGGAAGATGGTGGGCAGCCCAGACACCGTTGGGATGAACTACGGCAGCTACATGGAGGAGAAGCACATGCCACCCCCAAACATGACCACGAACGAGCGCAGAGTTATCGTGCCAGCAGATCCTACGCTATGGAGTACAGACCATGTGCGGCAGTGGCTGGAGTGGGCGGTGAAAGAATATGGCCTTCCAGACGTCAACATCTTGTTATTCCAGAACATCGATGGGAAGGAACTGTGCAAGATGACCAAGGACGACTTCCAGAGGCTCACCCCCAGCTACAACGCCGACATCCTTCTCTCACATCTCCACTACCTCAGAGAGACTCCTCTTCCACATTTGACTTCAGATGATGTTGATAAAGCCTTACAAAACTCTCCACGGTTAATGCATGCTAGAAACACAGGGGGTGCAGCTTTTATTTTCCCAAATACTTCAGTATATCCTGAAGCTACGCAAAGAATTACAACTAGGCCAGTCTCTTACAGATAA')
manualSeq <- S4Vectors::DataFrame(
  sample = 'Validation Set (GRCh37)',
  identifier = 'TMPRSS2-ERG prostate cancer specific isoform 1',
  gene = 'TMPRSS2-ERG',
  Tx.SequenceMut = Biostrings::DNAStringSet(base::list(testSeq1))
)


ProteoDiscography.hg19  ProteoDiscoimportTranscriptSequences
  ProteoDiscography.hg19
  transcripts  manualSeq
  removeExisting  

Note: Inputs are a DataFrame containing manually-curated or long-read transcript sequences; the output is imported transcript sequences added to the ProteoDiscography.

When to Use

• To generate customized protein databases (FASTA) incorporating genomic variants (SNVs, MNVs, InDels) from VCF or MAF files using importGenomicVariants() and incorporateGenomicVariants().
• To model alternative splicing events and novel exon-exon junctions from BED or custom splice junction files using importSpliceJunctions() and generateJunctionModels().
• To integrate manually-curated or long-read transcript sequences (e.g., from Nanopore sequencing) using importTranscriptSequences().
• To identify unique proteotypic peptides by comparing cleaved variant sequences against wild-type databases using checkProteotypicFragments().

When NOT to Use

• For general mass spectrometry data preprocessing, normalization, or statistical analysis of protein abundance; use MSnbase or MSstats instead.
• For general peptide-spectrum matching or raw spectra visualization; use Spectra instead.

Data Requirements

• Reference genome sequences (e.g., BSgenome object or FASTA file read via readDNAStringSet()).
• Transcript annotations (e.g., TxDb object containing 'EXONID', 'TXID', and 'GENEID' columns, or GTF/GFF file parsed via makeTxDbFromGFF()).
• Genomic variants in VCF or MAF format, or as VRanges objects.
• Splice junctions in BED, TopHat, or custom tabular format.

Key Parameters

• performAnchorCheck (TRUE): Validates whether reference alleles in VCF/MAF correspond to nucleotides on the reference genome.
• ignoreNonMatch (FALSE): If TRUE, removes non-matching reference anchor records instead of throwing an error.
• aggregateSamples (FALSE): Determines whether to aggregate samples or generate mutant transcripts per sample.
• aggregateWithinExon (TRUE): If TRUE, multiple mutations within the same exon (CDS) are placed on the same mutant CDS sequence.
• aggregateWithinTranscript (TRUE): If TRUE, aggregates multiple mutant exons within the same transcript.
• ignoreOverlappingMutations (TRUE): If TRUE, retains only the first of overlapping mutations on the same coding position.
• maxDistance (150): Maximum distance from a known exon boundary before introducing a novel exon in junction modeling.
• skipCanonical (TRUE): If TRUE, skips known canonical exon-exon junctions in junction modeling.

Best Practices

• Perform a sanity check on reference anchors during variant import to ensure concordance between the reference genome and the variant caller.
• Use summary() or print() on the ProteoDiscography object to inspect imported variants, mutational types, and sample counts.
• Convert transcript identifiers (e.g., ENTREZ IDs) to gene symbols using AnnotationDbi::select() and org.Hs.eg.db to ease downstream interpretation.
• Use clearLoggers() and registerLogger() from ParallelLogger to configure logging thresholds (e.g., 'TRACE' or 'INFO') for detailed execution tracking.

Common Pitfalls

• Mismatches between the reference genome used for variant calling and the one supplied to generateProteoDiscography() will cause anchor check failures. Fix by ensuring identical reference versions or setting performAnchorCheck = FALSE (not recommended).
• Missing 'EXONID', 'TXID', or 'GENEID' columns in the custom TxDb object. Fix by using standard Bioconductor annotation packages or correctly formatted GTF files with makeTxDbFromGFF().
• Overlapping mutations on the same coding position causing errors. Fix by setting ignoreOverlappingMutations = TRUE to retain only the first mutation.

Alternatives

• MSstats for statistical relative quantification of proteins and peptides.
• MSnbase for mass spectrometry data container and processing.
• Spectra for low-level mass spectrometry raw data representation and handling.

Citations

• van Riet et al. (2026), ProteoDisco: Generation of customized protein databases.

References

• Homepage: bioconductor.org/packages/proteodisco
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/proteodisco/inst/doc/Overview_of_Proteodisco.html