DeepPeptide predicts cleaved peptides in proteins using conditional random fields

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

DeepPeptide predicts cleaved peptides in proteins using conditional random fields. / Teufel, Felix; Refsgaard, Jan Christian; Madsen, Christian Toft; Stahlhut, Carsten; Grønborg, Mads; Winther, Ole; Madsen, Dennis.

In: Bioinformatics (Oxford, England), Vol. 39, No. 10, btad616, 2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Teufel, F, Refsgaard, JC, Madsen, CT, Stahlhut, C, Grønborg, M, Winther, O & Madsen, D 2023, 'DeepPeptide predicts cleaved peptides in proteins using conditional random fields', Bioinformatics (Oxford, England), vol. 39, no. 10, btad616. https://doi.org/10.1093/bioinformatics/btad616

APA

Teufel, F., Refsgaard, J. C., Madsen, C. T., Stahlhut, C., Grønborg, M., Winther, O., & Madsen, D. (2023). DeepPeptide predicts cleaved peptides in proteins using conditional random fields. Bioinformatics (Oxford, England), 39(10), [btad616]. https://doi.org/10.1093/bioinformatics/btad616

Vancouver

Teufel F, Refsgaard JC, Madsen CT, Stahlhut C, Grønborg M, Winther O et al. DeepPeptide predicts cleaved peptides in proteins using conditional random fields. Bioinformatics (Oxford, England). 2023;39(10). btad616. https://doi.org/10.1093/bioinformatics/btad616

Author

Teufel, Felix ; Refsgaard, Jan Christian ; Madsen, Christian Toft ; Stahlhut, Carsten ; Grønborg, Mads ; Winther, Ole ; Madsen, Dennis. / DeepPeptide predicts cleaved peptides in proteins using conditional random fields. In: Bioinformatics (Oxford, England). 2023 ; Vol. 39, No. 10.

Bibtex

@article{5902f1c3efb547eb9c551f562a8f2b27,
title = "DeepPeptide predicts cleaved peptides in proteins using conditional random fields",
abstract = "MOTIVATION: Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood. RESULTS: We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes. AVAILABILITY AND IMPLEMENTATION: DeepPeptide is available online at ku.biolib.com/DeepPeptide.",
author = "Felix Teufel and Refsgaard, {Jan Christian} and Madsen, {Christian Toft} and Carsten Stahlhut and Mads Gr{\o}nborg and Ole Winther and Dennis Madsen",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2023. Published by Oxford University Press.",
year = "2023",
doi = "10.1093/bioinformatics/btad616",
language = "English",
volume = "39",
journal = "Bioinformatics (Online)",
issn = "1367-4811",
publisher = "Oxford University Press",
number = "10",

}

RIS

TY - JOUR

T1 - DeepPeptide predicts cleaved peptides in proteins using conditional random fields

AU - Teufel, Felix

AU - Refsgaard, Jan Christian

AU - Madsen, Christian Toft

AU - Stahlhut, Carsten

AU - Grønborg, Mads

AU - Winther, Ole

AU - Madsen, Dennis

N1 - Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press.

PY - 2023

Y1 - 2023

N2 - MOTIVATION: Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood. RESULTS: We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes. AVAILABILITY AND IMPLEMENTATION: DeepPeptide is available online at ku.biolib.com/DeepPeptide.

AB - MOTIVATION: Peptides are ubiquitous throughout life and involved in a wide range of biological processes, ranging from neural signaling in higher organisms to antimicrobial peptides in bacteria. Many peptides are generated post-translationally by cleavage of precursor proteins and can thus not be detected directly from genomics data, as the specificities of the responsible proteases are often not completely understood. RESULTS: We present DeepPeptide, a deep learning model that predicts cleaved peptides directly from the amino acid sequence. DeepPeptide shows both improved precision and recall for peptide detection compared to previous methodology. We show that the model is capable of identifying peptides in underannotated proteomes. AVAILABILITY AND IMPLEMENTATION: DeepPeptide is available online at ku.biolib.com/DeepPeptide.

U2 - 10.1093/bioinformatics/btad616

DO - 10.1093/bioinformatics/btad616

M3 - Journal article

C2 - 37812217

AN - SCOPUS:85174642407

VL - 39

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

SN - 1367-4811

IS - 10

M1 - btad616

ER -

ID: 371923871