SignalP 5.0 improves signal peptide predictions using deep neural networks

Research output: Contribution to journal › Journal article › Communication

Standard

SignalP 5.0 improves signal peptide predictions using deep neural networks. / Armenteros, José Juan Almagro; Tsirigos, Konstantinos; Sønderby, Casper Kaae; Petersen, Thomas Nordahl; Winther, Ole; Brunak, Søren; von Heijne, Gunnar; Nielsen, Henrik.

In: Nature Biotechnology, Vol. 37, No. 4, 18.02.2019, p. 420-423.

Research output: Contribution to journal › Journal article › Communication

Harvard

Armenteros, JJA, Tsirigos, K, Sønderby, CK, Petersen, TN, Winther, O, Brunak, S, von Heijne, G & Nielsen, H 2019, 'SignalP 5.0 improves signal peptide predictions using deep neural networks', Nature Biotechnology, vol. 37, no. 4, pp. 420-423. https://doi.org/10.1038/s41587-019-0036-z

APA

Armenteros, J. J. A., Tsirigos, K., Sønderby, C. K., Petersen, T. N., Winther, O., Brunak, S., von Heijne, G., & Nielsen, H. (2019). SignalP 5.0 improves signal peptide predictions using deep neural networks. Nature Biotechnology, 37(4), 420-423. https://doi.org/10.1038/s41587-019-0036-z

Vancouver

Armenteros JJA, Tsirigos K, Sønderby CK, Petersen TN, Winther O, Brunak S et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nature Biotechnology. 2019 Feb 18;37(4):420-423. https://doi.org/10.1038/s41587-019-0036-z

Author

Armenteros, José Juan Almagro ; Tsirigos, Konstantinos ; Sønderby, Casper Kaae ; Petersen, Thomas Nordahl ; Winther, Ole ; Brunak, Søren ; von Heijne, Gunnar ; Nielsen, Henrik. / SignalP 5.0 improves signal peptide predictions using deep neural networks. In: Nature Biotechnology. 2019 ; Vol. 37, No. 4. pp. 420-423.

Bibtex

@article{4bdc17662de94061baa8763cf7ced4fd,
title = "SignalP 5.0 improves signal peptide predictions using deep neural networks",
abstract = "Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.",
author = "Armenteros, {Jos{\'e} Juan Almagro} and Konstantinos Tsirigos and S{\o}nderby, {Casper Kaae} and Petersen, {Thomas Nordahl} and Ole Winther and S{\o}ren Brunak and {von Heijne}, Gunnar and Henrik Nielsen",
year = "2019",
month = feb,
day = "18",
doi = "10.1038/s41587-019-0036-z",
language = "English",
volume = "37",
pages = "420--423",
journal = "Nature Biotechnology",
issn = "1087-0156",
publisher = "nature publishing group",
number = "4",

}

RIS

TY - JOUR

T1 - SignalP 5.0 improves signal peptide predictions using deep neural networks

AU - Armenteros, José Juan Almagro

AU - Tsirigos, Konstantinos

AU - Sønderby, Casper Kaae

AU - Petersen, Thomas Nordahl

AU - Winther, Ole

AU - Brunak, Søren

AU - von Heijne, Gunnar

AU - Nielsen, Henrik

PY - 2019/2/18

Y1 - 2019/2/18

N2 - Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

AB - Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

U2 - 10.1038/s41587-019-0036-z

DO - 10.1038/s41587-019-0036-z

M3 - Journal article

C2 - 30778233

VL - 37

SP - 420

EP - 423

JO - Nature Biotechnology

JF - Nature Biotechnology

SN - 1087-0156

IS - 4

ER -

ID: 214126009