The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
LocTree3 prediction of localization
Open accessAbstract
The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18=80±3% for eukaryotes and a six-state accuracy Q6=89±4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Global analysis of protein localization in budding yeast
Huh, W.-K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S., & O'Shea, E. K. (2003) · Nature · doi:10.1038/nature02026 · Cited by 4,248
A large-scale evaluation of computational protein function prediction
Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2340 · Cited by 1,028
Self-citation
Research works printed but not linked: 25
- Bairoch, 1997, The SWISS-PROT protein sequence data bank and its supplement TrEMBL, Nucleic Acids Res., 25, 31, 10.1093/nar/25.1.31 · DOI · Paper page
- Simpson, 2003, Localizing the proteome, Genome Biol., 4, 240, 10.1186/gb-2003-4-12-240 · DOI
- Koonin, 2000, Bridging the gap between sequence and function, Trends Genet., 16, 16, 10.1016/S0168-9525(99)01927-7 · DOI
- Lee, 2007, Predicting protein function from sequence and structure, Nat. Rev. Mol. Cell Biol., 8, 995, 10.1038/nrm2281 · DOI · Paper page
- Yu, 2006, Prediction of protein subcellular localization, Proteins, 64, 643, 10.1002/prot.21018 · DOI · Paper page
- Horton, 2007, WoLF PSORT: protein localization predictor, Nucleic Acids Res., 35, W585, 10.1093/nar/gkm259 · DOI · Paper page
- Briesemeister, 2010, YLoc–an interpretable web server for predicting subcellular localization, Nucleic Acids Res., 38, W497, 10.1093/nar/gkq477 · DOI · Paper page
- Yu, 2010, PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes, Bioinformatics, 26, 1608, 10.1093/bioinformatics/btq249 · DOI · Paper page
- Imai, 2010, Prediction of subcellular locations of proteins: where to proceed, Proteomics, 10, 3970, 10.1002/pmic.201000274 · DOI
- Altschul, 1997, Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Res., 25, 3389, 10.1093/nar/25.17.3389 · DOI · Paper page
- Goldberg, 2012, LocTree2 predicts localization for all domains of life, Bioinformatics, 28, i458, 10.1093/bioinformatics/bts390 · DOI
- Alberts, 2002, Molecular Biology of the Cell
- Mooney, 2013, SCL-Epred: a generalised de novo eukaryotic protein subcellular localisation predictor, Amino Acids, 45, 291, 10.1007/s00726-013-1491-3 · DOI
- Kuang, 2004, Profile-based string kernels for remote homology detection and motif extraction, 152
- Hamp, 2013, Accelerating the original profile kernel, PLoS One, 8, e68459, 10.1371/journal.pone.0068459 · DOI
- Dimmer, 2012, The UniProt-GO Annotation database in 2011, Nucleic Acids Res., 40, D565, 10.1093/nar/gkr1048 · DOI · Paper page
- Kajan, 2013, Cloud prediction of protein structure and function with PredictProtein for Debian, BioMed. Res. Int., 2013, 398968, 10.1155/2013/398968 · doi:10.1155/2013/398968
- Yachdav, 2014, PredictProtein – an open resource for online prediction of protein structural and functional features, Nucleic Acids Res., 10.1093/nar/gku366 · DOI · Paper page
- Sander, 1991, Database of homology-derived protein structures and the structural meaning of sequence alignment, Proteins, 9, 56, 10.1002/prot.340090107 · DOI · Paper page
- Rost, 1999, Twilight zone of protein sequence alignments, Protein Eng., 12, 85, 10.1093/protein/12.2.85 · DOI · Paper page
- Mika, 2003, UniqueProt: creating representative protein sequence sets, Nucleic Acids Res., 31, 3789, 10.1093/nar/gkg620 · DOI
- Rost, 2002, Enzyme function less conserved than anticipated, J. Mol. Biol., 318, 595, 10.1016/S0022-2836(02)00016-5 · DOI · Paper page
- Przybylski, 2002, Alignments grow, secondary structure prediction improves, Proteins, 46, 197, 10.1002/prot.10029 · DOI · Paper page
- Altschul, 1996, Local alignment statistics, Methods Enzymol., 266, 460, 10.1016/S0076-6879(96)66029-7 · DOI · Paper page
- Berman, 2000, The Protein Data Bank, Nucleic Acids Res., 28, 235, 10.1093/nar/28.1.235 · DOI · Paper page
Cited by
A draft network of ligand–receptor-mediated multicellular signalling in human
Ramilowski, J. A., Goldberg, T., Harshbarger, J., Kloppmann, E., Lizio, M., Satagopam, V. P., Itoh, M., Kawaji, H., Carninci, P., Rost, B., et al. (2015) · Nature Communications · doi:10.1038/ncomms8866 · Cited by 887
BUSCA: an integrative web server to predict subcellular localization of proteins
Savojardo, C., Martelli, P. L., Fariselli, P., Profiti, G., & Casadio, R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky320 · Cited by 452
Local and global influences on protein turnover in neurons and glia
Dörrbaum, A. R., Kochen, L., Langer, J. D., & Schuman, E. M. (2018) · eLife · doi:10.7554/elife.34202 · Cited by 249
Light attention predicts protein location from the language of life
Stärk, H., Dallago, C., Heinzinger, M., & Rost, B. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab035 · Cited by 130
Self-citationProtein embeddings and deep learning predict binding residues for various ligand classes
(2021) · doi:10.1038/s41598-021-03431-4 · Cited by 120
Metadata
- Authors
- Goldberg, T., Hecht, M., Hamp, T., Karl, T., Yachdav, G., Ahmed, N., Altermann, U., Angerer, P., Ansorge, S., Balasz, K., Bernhofer, M., Betz, A., Cizmadija, L., Do, K. T., Gerke, J., Greil, R., Joerdens, V., Hastreiter, M., Hembach, K., Herzog, M., Kalemanov, M., Kluge, M., Meier, A., Nasir, H., Neumaier, U., Prade, V., Reeb, J., Sorokoumov, A., Troshani, I., Vorberg, S., Waldraff, S., Zierer, J., Nielsen, H., & Rost, B.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-05-21
- Volume
- 42
- Issue
- W1
- Pages
- W350-W355
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2103132055
- Cited by
- 319
- References
- 2
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: LocTree3 prediction of localization. https://app.cochat.ai/papers/10.1093/nar/gku396
- The paper
- Goldberg, T., Hecht, M., Hamp, T., Karl, T., Yachdav, G., Ahmed, N., Altermann, U., Angerer, P., Ansorge, S., Balasz, K., Bernhofer, M., Betz, A., Cizmadija, L., Do, K. T., Gerke, J., Greil, R., Joerdens, V., Hastreiter, M., Hembach, K., . . . Rost, B. (2014). LocTree3 prediction of localization. Nucleic Acids Research. https://doi.org/10.1093/nar/gku396
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
