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SignalP 5.0 improves signal peptide predictions using deep neural networks

Almagro Armenteros, José Juan (author)
Tsirigos, Konstantinos D. (author)
Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab),Technical University of Denmark, Denmark; Max Planck Institute for Molecular Genetics, Germany
Kaae Sonderby, Casper (author)
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Nordahl Petersen, Thomas (author)
Winther, Ole (author)
Brunak, Søren (author)
von Heijne, Gunnar (author)
Stockholms universitet,Institutionen för biokemi och biofysik,Science for Life Laboratory (SciLifeLab)
Nielsen, Henrik (author)
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 (creator_code:org_t)
2019-02-18
2019
English.
In: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 37:4, s. 420-423
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Biologi (hsv//swe)
NATURAL SCIENCES  -- Biological Sciences (hsv//eng)

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