The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The InterPro protein families database: the classification resource after 15 years
Open accessAbstract
The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36,766 member database signatures integrated into 26,238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.
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
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Accelerated Profile HMM Searches
Eddy, S. R. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002195 · Cited by 6,975
Caspi, R., Billington, R., Ferrer, L., Foerster, H., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., Latendresse, M., Mueller, L. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1164 · Cited by 3,395
Cited by
The Pfam protein families database: towards a more sustainable future
Finn, R. D., Coggill, P., Eberhardt, R. Y., Eddy, S. R., Mistry, J., Mitchell, A. L., Potter, S. C., Punta, M., Qureshi, M., Sangrador-Vegas, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1344 · Cited by 5,975
Self-citationUniProt: the universal protein knowledgebase
The UniProt Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1099 · Cited by 4,436
Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma
Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.02.065 · Cited by 3,448
Expansion of the Gene Ontology knowledgebase and resources
The Gene Ontology Consortium (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1108 · Cited by 1,850
Huson, D. H., Beier, S., Flade, I., Górska, A., El-Hadidi, M., Mitra, S., Ruscheweyh, H.-J., & Tappu, R. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004957 · Cited by 1,761
The extracellular matrix: tools and insights for the "omics" era
Naba, A., Clauser, K. R., Ding, H., Whittaker, C. A., Carr, S. A., & Hynes, R. O. (2016) · Matrix Biology · doi:10.1016/j.matbio.2015.06.003 · Cited by 1,037
Genome evolution in the allotetraploid frog Xenopus laevis
Session, A. M., Uno, Y., Kwon, T., Chapman, J. A., Toyoda, A., Takahashi, S., Fukui, A., Hikosaka, A., Suzuki, A., Kondo, M., et al. (2016) · Nature · doi:10.1038/nature19840 · Cited by 949
CAMP R3 : a database on sequences, structures and signatures of antimicrobial peptides: Table 1.
Waghu, F. H., Barai, R. S., Gurung, P., & Idicula-Thomas, S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1051 · Cited by 715
The revisited genome of Pseudomonas putida KT2440 enlightens its value as a robust metabolic chassis
Belda, E., van Heck, R. G. A., José Lopez‐Sanchez, M., Cruveiller, S., Barbe, V., Fraser, C., Klenk, H., Petersen, J., Morgat, A., Nikel, P. I., et al. (2016) · Environmental Microbiology · doi:10.1111/1462-2920.13230 · Cited by 338
A Review of bioinformatics tools for bio-prospecting from metagenomic sequence data
Roumpeka, D. D., Wallace, R. J., Escalettes, F., Fotheringham, I., & Watson, M. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00023 · Cited by 199
MitoMiner v3.1, an update on the mitochondrial proteomics database
Smith, A. C., & Robinson, A. J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1001 · Cited by 162
Laying the foundation for genomically-based risk assessment in chronic myeloid leukemia
on behalf of the International CML Foundation Genomics Alliance, Branford, S., Kim, D. D. H., Apperley, J. F., Eide, C. A., Mustjoki, S., Ong, S. T., Nteliopoulos, G., Ernst, T., Chuah, C., et al. (2019) · Leukemia · doi:10.1038/s41375-019-0512-y · Cited by 149
FFPred 3: feature-based function prediction for all Gene Ontology domains
Cozzetto, D., Minneci, F., Currant, H., & Jones, D. T. (2016) · Scientific Reports · doi:10.1038/srep31865 · Cited by 127
Why do eukaryotic proteins contain more intrinsically disordered regions?
Basile, W., Salvatore, M., Bassot, C., & Elofsson, A. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1007186 · Cited by 113
Structure of a 1.5-MDa adhesin that binds its Antarctic bacterium to diatoms and ice
Guo, S., Stevens, C. A., Vance, T. D. R., Olijve, L. L. C., Graham, L. A., Campbell, R. L., Yazdi, S. R., Escobedo, C., Bar-Dolev, M., Yashunsky, V., et al. (2017) · Science Advances · doi:10.1126/sciadv.1701440 · Cited by 107
Recognition of protein-linked glycans as a determinant of peptidase activity
Noach, I., Ficko-Blean, E., Pluvinage, B., Stuart, C., Jenkins, M. L., Brochu, D., Buenbrazo, N., Wakarchuk, W., Burke, J. E., Gilbert, M., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615141114 · Cited by 89
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet
Viner, C., Ishak, C. A., Johnson, J., Walker, N. J., Shi, H., Sjöberg-Herrera, M. K., Shen, S. Y., Lardo, S. M., Adams, D. J., Ferguson-Smith, A. C., et al. (2024) · Genome Biology · doi:10.1186/s13059-023-03070-0 · Cited by 30
Zhou, Z., Wu, J., Wang, M., & Zhang, J. (2017) · Microbial Pathogenesis · doi:10.1016/j.micpath.2017.06.028 · Cited by 25
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet
Viner, C., Ishak, C. A., Johnson, J., Walker, N. J., Shi, H., Sjöberg-Herrera, M. K., Shen, S. Y., Lardo, S. M., Adams, D. J., Ferguson-Smith, A. C., et al. (2016) · doi:10.1101/043794 · Cited by 18
Wottrich, S., Mendonca, S., Safarpour, C., Nguyen, C., Marinelli, L. J., Hancock, S. P., Modlin, R. L., & Parker, J. M. (2024) · Microbiome Research Reports · doi:10.20517/mrr.2023.42 · Cited by 7
Metadata
- Authors
- Mitchell, A., Chang, H.-Y., Daugherty, L., Fraser, M., Hunter, S., Lopez, R., McAnulla, C., McMenamin, C., Nuka, G., Pesseat, S., Sangrador-Vegas, A., Scheremetjew, M., Rato, C., Yong, S.-Y., Bateman, A., Punta, M., Attwood, T. K., Sigrist, C. J. A., Redaschi, N., Rivoire, C., Xenarios, I., Kahn, D., Guyot, D., Bork, P., Letunic, I., Gough, J., Oates, M., Haft, D., Huang, H., Natale, D. A., Wu, C. H., Orengo, C., Sillitoe, I., Mi, H., Thomas, P. D., & Finn, R. D.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-11-26
- Volume
- 43
- Issue
- D1
- Pages
- D213-D221
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2164461702
- Cited by
- 1,274
- References
- 3
- FWCI
- 79.34
- 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
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.