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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "IsoBayes" in publications use:'
type: software
license: GPL-3.0-only
title: 'IsoBayes: IsoBayes: Single Isoform protein inference Method via Bayesian Analyses'
version: 1.10.0
doi: 10.1101/2024.06.10.598223
identifiers:
- type: doi
  value: 10.32614/CRAN.package.IsoBayes
abstract: 'IsoBayes is a Bayesian method to perform inference on single protein isoforms.
  Our approach infers the presence/absence of protein isoforms, and also estimates
  their abundance; additionally, it provides a measure of the uncertainty of these
  estimates, via: i) the posterior probability that a protein isoform is present in
  the sample; ii) a posterior credible interval of its abundance. IsoBayes inputs
  liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts,
  and intensities. When available, trascript isoform abundances (i.e., TPMs) are also
  incorporated: TPMs are used to formulate an informative prior for the respective
  protein isoform relative abundance. We further identify isoforms where the relative
  abundance of proteins and transcripts significantly differ. We use a two-layer latent
  variable approach to model two sources of uncertainty typical of MS data: i) peptides
  may be erroneously detected (even when absent); ii) many peptides are compatible
  with multiple protein isoforms. In the first layer, we sample the presence/absence
  of each peptide based on its estimated probability of being mistakenly detected,
  also known as PEP (i.e., posterior error probability). In the second layer, for
  peptides that were estimated as being present, we allocate their abundance across
  the protein isoforms they map to. These two steps allow us to recover the presence
  and abundance of each protein isoform.'
authors:
- family-names: Bollon
  given-names: Jordy
  email: jordy.bollon@iit.it
- family-names: Tiberi
  given-names: Simone
  email: simone.tiberi@unibo.it
  orcid: https://orcid.org/0000-0002-3054-9964
preferred-citation:
  type: article
  title: 'IsoBayes: a Bayesian approach for single-isoform proteomics inference'
  authors:
  - family-names: Bollon
    given-names: Jordy
    email: jordy.bollon@iit.it
  - family-names: Shortreed
    given-names: Michael R.
  - family-names: Jordan
    given-names: Ben T.
  - family-names: Miller
    given-names: Rachel
  - family-names: Jeffery
    given-names: Erin
  - family-names: Cavalli
    given-names: Andrea
  - family-names: Smith
    given-names: Lloyd M.
  - family-names: Dewey
    given-names: Colin
  - family-names: Sheynkman
    given-names: Gloria M.
  - family-names: Tiberi
    given-names: Simone
    email: simone.tiberi@unibo.it
    orcid: https://orcid.org/0000-0002-3054-9964
  journal: bioRxiv
  year: '2024'
  doi: 10.1101/2024.06.10.598223
  url: https://doi.org/10.1101/2024.06.10.598223
repository: https://bioc-release.r-universe.dev
repository-code: https://github.com/SimoneTiberi/IsoBayes
commit: 1f1dc5c2d258cfb6078367587d86f2cde72bcd11
url: https://github.com/SimoneTiberi/IsoBayes
date-released: '2026-04-28'
contact:
- family-names: Tiberi
  given-names: Simone
  email: simone.tiberi@unibo.it
  orcid: https://orcid.org/0000-0002-3054-9964