The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
BUSCA: an integrative web server to predict subcellular localization of proteins
Open accessAbstract
Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.
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Cites
Prediction of protein subcellular localization
Yu, C., Chen, Y., Lu, C., & Hwang, J. (2006) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.21018 · Cited by 1,790
DeepLoc: prediction of protein subcellular localization using deep learning
Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H., & Winther, O. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx431 · Cited by 1,143
PredGPI: a GPI-anchor predictor
Pierleoni, A., Martelli, P. L., & Casadio, R. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-392 · Cited by 646
Self-citationAn expanded evaluation of protein function prediction methods shows an improvement in accuracy
Jiang, Y., Oron, T. R., Clark, W. T., Bankapur, A. R., D’Andrea, D., Lepore, R., Funk, C. S., Kahanda, I., Verspoor, K. M., Ben-Hur, A., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-1037-6 · Cited by 396
Self-citationBaCelLo: a balanced subcellular localization predictor
Pierleoni, A., Martelli, P. L., Fariselli, P., & Casadio, R. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl222 · Cited by 340
Self-citationLocTree3 prediction of localization
Goldberg, T., Hecht, M., Hamp, T., Karl, T., Yachdav, G., Ahmed, N., Altermann, U., Angerer, P., Ansorge, S., Balasz, K., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku396 · Cited by 319
DeepSig: deep learning improves signal peptide detection in proteins
Savojardo, C., Martelli, P. L., Fariselli, P., & Casadio, R. (2018) · Bioinformatics · doi:10.1093/bioinformatics/btx818 · Cited by 160
Self-citationDas, S., Lee, D., Sillitoe, I., Dawson, N. L., Lees, J., & Orengo, C. A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv398 · Cited by 149
MS-k NN: protein function prediction by integrating multiple data sources
Lan, L., Djuric, N., Guo, Y., & Vučetić, S. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s3-s8 · Cited by 101
Prediction of subcellular locations of proteins: Where to proceed?
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Building structural models of a whole mycoplasma cell
Maritan, M., Autin, L., Karr, J., Covert, M. W., Olson, A. J., & Goodsell, D. S. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167351 · Cited by 75
Zile, K., Dessimoz, C., Wurm, Y., & Masel, J. (2020) · Genome Biology and Evolution · doi:10.1093/gbe/evaa127 · Cited by 18
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Metadata
- Authors
- Savojardo, C., Martelli, P. L., Fariselli, P., Profiti, G., & Casadio, R.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-04-30
- Volume
- 46
- Issue
- W1
- Pages
- W459-W466
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2801813992
- Cited by
- 452
- References
- 21
- FWCI
- 10.91
- Type
- journal article
- Language
- en
- Open access
- gold
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