Forskning
Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

SignalP 6.0 predicts all five types of signal peptides using protein language models

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

DOI

  1. Deep Visual Proteomics defines single-cell identity and heterogeneity

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. A knowledge graph to interpret clinical proteomics data

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Improved metagenome binning and assembly using deep variational autoencoders

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Butler enables rapid cloud-based analysis of thousands of human genomes

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  5. Modeling neurodegenerative diseases with patient-derived induced pluripotent cells: Possibilities and challenges

    Publikation: Bidrag til tidsskriftReviewForskningpeer review

  1. DeepLoc 2.0: Multi-label subcellular localization prediction using protein language models

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  4. Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  5. A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  • Felix Teufel
  • José Juan Almagro Armenteros
  • Alexander Rosenberg Johansen
  • Magnús Halldór Gíslason
  • Silas Irby Pihl
  • Konstantinos D Tsirigos
  • Ole Winther
  • Søren Brunak
  • Gunnar von Heijne
  • Henrik Nielsen
Vis graf over relationer

Signal peptides (SPs) are short amino acid sequences that control protein secretion and translocation in all living organisms. SPs can be predicted from sequence data, but existing algorithms are unable to detect all known types of SPs. We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.

OriginalsprogEngelsk
TidsskriftNature Biotechnology
Vol/bind40
Udgave nummer7
Sider (fra-til)1023-1025
Antal sider3
ISSN1087-0156
DOI
StatusUdgivet - jul. 2022

Bibliografisk note

© 2022. The Author(s).

ID: 79813752