Cited by 2,607Genomics and Phylogenetic StudiesMachine Learning in BioinformaticsScientific Computing and Data Management

The DOI matches its Crossref record; 21 of the 30 references it cites were found in a registry or an index.

HMMER web server: 2018 update

Open access

Potter, Simon C, Luciani, Aurélien, Eddy, Sean R, Park, Youngmi, Lopez, Rodrigo, Finn, Robert D·2018·Nucleic Acids Research

DOI 10.1093/nar/gky448PMC PMC6030962

Open this paper in CoChat — free account Open PDF

Abstract

The HMMER webserver [http://www.ebi.ac.uk/Tools/hmmer] is a free-to-use service which provides fast searches against widely used sequence databases and profile hidden Markov model (HMM) libraries using the HMMER software suite (http://hmmer.org). The results of a sequence search may be summarized in a number of ways, allowing users to view and filter the significant hits by domain architecture or taxonomy. For large scale usage, we provide an application programmatic interface (API) which has been expanded in scope, such that all result presentations are available via both HTML and API. Furthermore, we have refactored our JavaScript visualization library to provide standalone components for different result representations. These consume the aforementioned API and can be integrated into third-party websites. The range of databases that can be searched against has been expanded, adding four sequence datasets (12 in total) and one profile HMM library (6 in total). To help users explore the biological context of their results, and to discover new data resources, search results are now supplemented with cross references to other EMBL-EBI databases.

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

Crossref · 10.1093/nar/gky448

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Figure 1. Results of a search in the ‘score’ view with the new ‘Cross references’ column, populated with links to the hit sequence in other EMBL-EBI databases. As the page is loaded, these symbols may disappear if no cross-references are found in this category.CC BY-NC · PubMed Central

Figure 2. New taxonomy view. The tree is navigable via keyboard or mouse, with the red circle showing the currently selected node, and the size of the blue circles being proportional to the number of hits. Figure 3. As the user changes the selected node, the red line highlights the selected route to the node. The lineage is shown below the figure and the small barchart shows the distribution of E-values at the selected node.CC BY-NC · PubMed Central

Figure 3. Annotation of the domain graphic with the catalytic active sites predicted by Pfam, with extra information about the middle site displayed in a mouse-activated pop-up.CC BY-NC · PubMed Central

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.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

UniProt: the Universal Protein knowledgebase
Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593
Accelerated Profile HMM Searches
Eddy, S. R. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002195 · Cited by 6,975
Self-citation
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-citation
HMMER web server: interactive sequence similarity searching
Finn, R. D., Clements, J., & Eddy, S. R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr367 · Cited by 5,952
Self-citation
Profile hidden Markov models
Eddy, S. R. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.9.755 · Cited by 5,515
Self-citation
Ensembl 2018
Zerbino, D. R., Achuthan, P., Akanni, W., Amode, M. R., Barrell, D., Bhai, J., Billis, K., Cummins, C., Gall, A., Girón, C. G., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1098 · Cited by 2,605
UniProt: the universal protein knowledgebase
The UniProt Consortium (2018) · Nucleic Acids Research · doi:10.1093/nar/gky092 · Cited by 2,043
The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database
Rawlings, N. D., Barrett, A. J., Thomas, P. D., Huang, X., Bateman, A., & Finn, R. D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1134 · Cited by 1,749
Self-citation
InterPro in 2017—beyond protein family and domain annotations
Finn, R. D., Attwood, T. K., Babbitt, P. C., Bateman, A. G., Peer Bork, B., Bridge, A. J., Chang, H.-Y., Dosztányi, Z., El-Gebali, S., Fraser, M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1107 · Cited by 1,455
Self-citation
HMMER web server: 2015 update
Finn, R. D., Clements, J., Arndt, W. D., Miller, B. L., Wheeler, T. J., Schreiber, F., Bateman, A. G., & Eddy, S. R. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv397 · Cited by 911
Self-citation

Show all 30 references

Sorted by citations

Evolutionary-scale prediction of atomic level protein structure with a language model
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., et al. (2022) · doi:10.1101/2022.07.20.500902 · Cited by 5,990
The EMBL-EBI search and sequence analysis tools APIs in 2019
Madeira, F., Park, Y. M., Lee, J., Buso, N., Gur, T., Madhusoodanan, N., Basutkar, P., Tivey, A. R. N., Potter, S. C., Finn, R. D., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz268 · Cited by 4,167
Self-citation
Improved protein structure prediction using predicted interresidue orientations
Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421
InterPro in 2019: improving coverage, classification and access to protein sequence annotations
Mitchell, A. L., Attwood, T. K., Babbitt, P. C., Blum, M., Bork, P., Bridge, A., Brown, S. D., Chang, H.-Y., El-Gebali, S., Fraser, M. I., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1100 · Cited by 1,426
Self-citation
Protein Sequence Analysis Using the MPI Bioinformatics Toolkit
Gabler, F., Nam, S., Till, S., Mirdita, M., Steinegger, M., Söding, J., Lupas, A. N., & Alva, V. (2020) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.108 · Cited by 831
AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors
Hu, H., Miao, Y., Jia, L.-H., Yu, Q.-Y., Zhang, Q., & Guo, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky822 · Cited by 816
Computed structures of core eukaryotic protein complexes
Humphreys, I. R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T. J., Banjade, S., Bagde, S. R., et al. (2021) · Science · doi:10.1126/science.abm4805 · Cited by 542
The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities
Chen, I.-M. A., Chu, K., Palaniappan, K., Ratner, A., Huang, J., Huntemann, M., Hajek, P. P., Ritter, S., Varghese, N. J., Seshadri, R., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa939 · Cited by 433
Learning inverse folding from millions of predicted structuresPreprint
Hsu, C., Verkuil, R., Liu, J. J., Lin, Z., Hie, B., Sercu, T., Lerer, A. K., & Rives, A. W. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.10.487779 · Cited by 492
Low-N protein engineering with data-efficient deep learning
Biswas, S., Khimulya, G., Alley, E. C., Esvelt, K. M., & Church, G. M. (2021) · Nature Methods · doi:10.1038/s41592-021-01100-y · Cited by 422
Ancient gene linkages support ctenophores as sister to other animals
Schultz, D. T., Haddock, S. H. D., Bredeson, J. V., Green, R. E., Simakov, O., & Rokhsar, D. S. (2023) · Nature · doi:10.1038/s41586-023-05936-6 · Cited by 362
BRENDA in 2019: a European ELIXIR core data resource
Jeske, L., Placzek, S., Schomburg, I., Chang, A., & Schomburg, D. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1048 · Cited by 385
The IMG/M data management and analysis system v.7: content updates and new features
Chen, I.-M. A., Chu, K., Palaniappan, K., Ratner, A., Huang, J., Huntemann, M., Hajek, P. P., Ritter, S., Webb, C., Wu, D., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac976 · Cited by 376
Meta‐Analysis of Gut Dysbiosis in Parkinson's Disease
Nishiwaki, H., Ito, M., Ishida, T., Hamaguchi, T., Maeda, T., Kashihara, K., Tsuboi, Y., Ueyama, J., Shimamura, T., Mori, H., et al. (2020) · Movement Disorders · doi:10.1002/mds.28119 · Cited by 370
NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning
Høie, M. H., Kiehl, E. N., Petersen, B., Nielsen, M., Winther, O., Nielsen, H., Hallgren, J., & Marcatili, P. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac439 · Cited by 374
The Pfam protein families database: embracing AI/ML
Paysan-Lafosse, T., Andreeva, A., Blum, M., Chuguransky, S., Grego, T., Pinto, B. L., Salazar, G. A., Bileschi, M. L., Llinares-López, F., Meng-Papaxanthos, L., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae997 · Cited by 382
AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations
Shen, W., Chen, S., Gan, Z.-Q., Zhang, Y., Yue, T., Chen, M., Xue, Y., Hu, H., & Guo, A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac907 · Cited by 364
Structural variation in the gut microbiome associates with host health
Zeevi, D., Korem, T., Godneva, A., Bar, N., Kurilshikov, A., Lotan-Pompan, M., Weinberger, A., Fu, J., Wijmenga, C., Zhernakova, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1065-y · Cited by 351
An integrated gene catalog and over 10,000 metagenome-assembled genomes from the gastrointestinal microbiome of ruminants
Xie, F., Jin, W., Si, H., Yuan, Y., Tao, Y., Liu, J., Wang, X., Yang, C., Li, Q., Yan, X., et al. (2021) · Microbiome · doi:10.1186/s40168-021-01078-x · Cited by 333
Expansion of the global RNA virome reveals diverse clades of bacteriophages
Neri, U., Wolf, Y. I., Roux, S., Camargo, A. P., Lee, B., Kazlauskas, D., Chen, I. M., Ivanova, N., Allen, L. Z., Páez-Espino, D., et al. (2022) · Cell · doi:10.1016/j.cell.2022.08.023 · Cited by 301
Chromosome-scale genome assembly provides insights into rye biology, evolution and agronomic potential
Rabanus‐Wallace, M. T., Hackauf, B., Mascher, M., Lux, T. M., Wicker, T., Gundlach, H., Baez, M. A., Houben, A., Mayer, K., Guo, L., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00807-0 · Cited by 293
SIFTS: updated Structure Integration with Function, Taxonomy and Sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins
Dana, J. M., Gutmanas, A., Tyagi, N., Qi, G., O’Donovan, C., Martin, M. J., & Velankar, S. S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1114 · Cited by 268
Phigaro: high-throughput prophage sequence annotation
Валентиновна, С. Е., Tikhonova, P., Prianichnikov, N. A., Rands, C. M., Zdobnov, E. M., Ilina, E. N., & Govorun, V. M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa250 · Cited by 257
The nearly complete genome of Ginkgo biloba illuminates gymnosperm evolution
Liu, H., Wang, X., Wang, G., Cui, P., Wu, S., Ai, C., Hu, N., Li, A., He, B., Shao, X., et al. (2021) · Nature Plants · doi:10.1038/s41477-021-00933-x · Cited by 229
Plant Metabolic Network 15: A resource of genome‐wide metabolism databases for 126 plants and algae
Hawkins, C., Ginzburg, D. N., Zhao, K., Dwyer, W., Xue, B., Xu, A., Rice, S. L., Cole, B., Paley, S., Karp, P. D., et al. (2021) · Journal of Integrative Plant Biology · doi:10.1111/jipb.13163 · Cited by 214
Type IV CRISPR–Cas systems are highly diverse and involved in competition between plasmids
Pinilla‐Redondo, R., Mayo-Muñoz, D., Russel, J., Garrett, R. A., Randau, L., Sørensen, S. J., & Shah, S. A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1197 · Cited by 202
ProteInfer, deep neural networks for protein functional inference
Sanderson, T., Bileschi, M. L., Belanger, D., & Colwell, L. J. (2023) · eLife · doi:10.7554/elife.80942 · Cited by 197
The Spatial Heterogeneity of the Gut Limits Predation and Fosters Coexistence of Bacteria and Bacteriophages
Lourenço, M., Chaffringeon, L., Lamy-Besnier, Q., Pédron, T., Campagne, P., Eberl, C., Berard, M., Stecher, B., Debarbieux, L., & Sordi, L. D. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.06.002 · Cited by 195
ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning
Wei, L., Ye, X., Sakurai, T., Mu, Z., & Wei, L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac006 · Cited by 181
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
Greener, J. G., Kandathil, S. M., & Jones, D. T. (2019) · Nature Communications · doi:10.1038/s41467-019-11994-0 · Cited by 178
Host-Specific Evolutionary and Transmission Dynamics Shape the Functional Diversification of Staphylococcus epidermidis in Human Skin
Zhou, W. L., Spoto, M., Hardy, R., Guan, C., Fleming, E. S., Larson, P. J., Brown, J. S., & Oh, J. (2020) · Cell · doi:10.1016/j.cell.2020.01.006 · Cited by 169
PhageAI - Bacteriophage Life Cycle Recognition with Machine Learning and Natural Language ProcessingPreprint
Tynecki, P., Guziński, A., Kazimierczak, J., Jadczuk, M., Dastych, J., & Oniśko, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.11.198606 · Cited by 175
The genetic adaptations of fall armyworm Spodoptera frugiperda facilitated its rapid global dispersal and invasion
Xiao, H., Ye, X., Xu, H., Yang, M., Yang, Y., Chen, X., Yang, Y., Liu, T., Yu, Y., Yang, W., et al. (2020) · Molecular Ecology Resources · doi:10.1111/1755-0998.13182 · Cited by 167
Genome sequences of horticultural plants: past, present, and future
Chen, F., Song, Y., Li, X., Chen, J., Mo, L., Zhang, X., Lin, Z., & Zhang, L. (2019) · Horticulture Research · doi:10.1038/s41438-019-0195-6 · Cited by 162
Using artificial intelligence to document the hidden RNA virosphere
Hou, X., He, Y., Fang, P., Mei, S.-Q., Xu, Z., Wu, W.-C., Tian, J.-H., Zhang, S., Zeng, Z.-Y., Gou, Q.-Y., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.027 · Cited by 162
The GMC superfamily of oxidoreductases revisited: analysis and evolution of fungal GMC oxidoreductases
Sützl, L., Foley, G., Gillam, E. M. J., Mikael Bodén, A., & Haltrich, D. (2019) · Biotechnology for Biofuels · doi:10.1186/s13068-019-1457-0 · Cited by 159
Discovery of multiple anti-CRISPRs highlights anti-defense gene clustering in mobile genetic elements
Pinilla-Redondo, R., Shehreen, S., Marino, N. D., Fagerlund, R., Brown, C. M., Sørensen, S., Fineran, P., & Bondy-Denomy, J. (2020) · Nature Communications · doi:10.1038/s41467-020-19415-3 · Cited by 142
Two defence systems eliminate plasmids from seventh pandemic Vibrio cholerae
Jaskólska, M., Adams, D. W., & Blokesch, M. (2022) · Nature · doi:10.1038/s41586-022-04546-y · Cited by 142
Bacterial NLR-related proteins protect against phage
Kibby, E. M., Conte, A. N., Burroughs, A. M., Nagy, T. A., Vargas, J. A., Whalen, L. A., Aravind, L., & Whiteley, A. T. (2023) · Cell · doi:10.1016/j.cell.2023.04.015 · Cited by 151
Cenote-Taker 2 democratizes virus discovery and sequence annotation
Tisza, M. J., Belford, A. K., Domínguez‐Huerta, G., Bolduc, B., Sullivan, M. B., & Buck, C. B. (2020) · Virus Evolution · doi:10.1093/ve/veaa100 · Cited by 145
Resolving the structure of phage–bacteria interactions in the context of natural diversity
Kauffman, K. M., Chang, W. K., Brown, J. M., Hussain, F. A., Yang, J., Polz, M. F., & Kelly, L. (2022) · Nature Communications · doi:10.1038/s41467-021-27583-z · Cited by 149
ProteinNet: a standardized data set for machine learning of protein structure
AlQuraishi, M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2932-0 · Cited by 147
Prophages encode phage-defense systems with cognate self-immunity
Owen, S. V., Wenner, N., Dulberger, C. L., Rodwell, E. V., Bowers-Barnard, A., Quinones‐Olvera, N., Rigden, D. J., Rubin, E. J., Garner, E. C., Baym, M., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2021.09.002 · Cited by 139
Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning
Hong, J., Luo, Y., Zhang, Y., Ying, J., Xue, W., Xie, T., Tao, L., & Zhu, F. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz081 · Cited by 139
Engineered triply orthogonal pyrrolysyl–tRNA synthetase/tRNA pairs enable the genetic encoding of three distinct non-canonical amino acids
Dunkelmann, D. L., Willis, J. C. W., Beattie, A. T., & Chin, J. W. (2020) · Nature Chemistry · doi:10.1038/s41557-020-0472-x · Cited by 132
VPF-Class: taxonomic assignment and host prediction of uncultivated viruses based on viral protein families
Pons, J. C., Páez-Espino, D., Riera, G., Ivanova, N., Kyrpides, N. C., & Llabrés, M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab026 · Cited by 127
Streamlining CRISPR spacer-based bacterial host predictions to decipher the viral dark matter
Dion, M. B., Plante, P.-L., Zufferey, E., Shah, S. A., Corbeil, J., & Moineau, S. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab133 · Cited by 123
Diversity and distribution of viruses inhabiting the deepest ocean on Earth
Jian, H., Yi, Y., Wang, J., Hao, Y., Zhang, M., Wang, S., Meng, C., Zhang, Y., Jing, H., Wang, Y., et al. (2021) · The ISME Journal · doi:10.1038/s41396-021-00994-y · Cited by 130
Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows
Bossche, T. V. D., Kunath, B. J., Schallert, K., Schäpe, S. S., Abraham, P. E., Armengaud, J., Arntzen, M. Ø., Bassignani, A., Benndorf, D., Fuchs, S., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27542-8 · Cited by 123
Loss of microbial diversity and pathogen domination of the gut microbiota in critically ill patients
Ravi, A., Halstead, F. D., Bamford, A. L., Casey, A., Thomson, N. M., van Schaik, W., Snelson, C., Goulden, R., Foster-Nyarko, E., Savva, G. M., et al. (2019) · Microbial Genomics · doi:10.1099/mgen.0.000293 · Cited by 128
Genome binning of viral entities from bulk metagenomics data
Johansen, J., Plichta, D. R., Nissen, J. N., Jespersen, M. L., Shah, S. A., Deng, L., Stokholm, J., Bisgaard, H. F., Nielsen, D. S., Sørensen, S. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28581-5 · Cited by 116
PilY1 and minor pilins form a complex priming the type IVa pilus in Myxococcus xanthus
Treuner‐Lange, A., Chang, Y., Glatter, T., Herfurth, M., Lindow, S., Chreifi, G., Jensen, G. J., & Søgaard‐Andersen, L. (2020) · Nature Communications · doi:10.1038/s41467-020-18803-z · Cited by 112
Solubility-Weighted Index: fast and accurate prediction of protein solubility
Bhandari, B. K., Gardner, P. P., & Lim, C. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa578 · Cited by 97
Phage-centric ecological interactions in aquatic ecosystems revealed through ultra-deep metagenomics
Kavagutti, V., Andrei, A., Mehrshad, M., Salcher, M. M., & Ghai, R. (2019) · Microbiome · doi:10.1186/s40168-019-0752-0 · Cited by 112
ZEAMAP, a Comprehensive Database Adapted to the Maize Multi-Omics Era
Gui, S., Yang, L., Li, J., Luo, J., Xu, X., Yuan, J., Chen, L., Li, W., Yang, X., Wu, S., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101241 · Cited by 111
Composition and stage dynamics of mitochondrial complexes in Plasmodium falciparum
Evers, F., Cabrera‐Orefice, A., Elurbe, D. M., Lindert, M. K., Boltryk, S. D., Voss, T. S., Huynen, M. A., Brandt, U., & Kooij, T. W. A. (2021) · Nature Communications · doi:10.1038/s41467-021-23919-x · Cited by 111
A pan-Zea genome map for enhancing maize improvement
Gui, S., Wei, W., Jiang, C., Luo, J., Chen, L., Wu, S., Li, W., Wang, Y., Li, S., Yang, N., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02742-7 · Cited by 111
The gastrointestinal microbiome in dairy cattle is constrained by the deterministic driver of the region and the modified effect of diet
Lin, L., Lai, Z., Zhang, J., Zhu, W., & Mao, S. (2023) · Microbiome · doi:10.1186/s40168-022-01453-2 · Cited by 113
Viruses interact with hosts that span distantly related microbial domains in dense hydrothermal mats
Hwang, Y., Roux, S., Coclet, C., Krause, S. J. E., & Girguis, P. R. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01347-5 · Cited by 101
Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis
Schiebenhoefer, H., Bossche, T. V. D., Fuchs, S., Renard, B. Y., Muth, T., & Martens, L. (2019) · Expert Review of Proteomics · doi:10.1080/14789450.2019.1609944 · Cited by 108
The dynamic mechanism of 4E-BP1 recognition and phosphorylation by mTORC1
Böhm, R., Imseng, S., Jakob, R. P., Hall, M. N., Maier, T., & Hiller, S. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.03.031 · Cited by 111
Clostridium butyricum and carbohydrate active enzymes contribute to the reduced fat deposition in pigs
Ma, L., Tao, S., Song, T., Lyu, W., Li, Y., Wang, W., Shen, Q., Ni, Y., Zhu, J., Zhao, J., et al. (2024) · iMeta · doi:10.1002/imt2.160 · Cited by 105
Functional plasticity and evolutionary adaptation of allosteric regulation
Leander, M., Yuan, Y., Meger, A., Cui, Q., & Raman, S. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002613117 · Cited by 98
HVIDB: a comprehensive database for human–virus protein–protein interactions
Yang, X., Lian, X., Fu, C., Wuchty, S., Yang, S., & Zhang, Z. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa425 · Cited by 100
Comparative Genomics of Peroxisome Biogenesis Proteins: Making Sense of the PEX Proteins
Jansen, R., Santana‐Molina, C., van den Noort, M., Devos, D. P., & van der Klei, I. J. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.654163 · Cited by 100
Emerging concepts in pseudoenzyme classification, evolution, and signaling
Ribeiro, A. J. M., Das, S., Dawson, N. L., Zaru, R., Orchard, S. E., Thornton, J. M., Orengo, C. A., Zeqiraj, E., Murphy, J. M., & Eyers, P. A. (2019) · Science Signaling · doi:10.1126/scisignal.aat9797 · Cited by 97
The telomere-to-telomere genome of Fragaria vesca reveals the genomic evolution of Fragaria and the origin of cultivated octoploid strawberry
Zhou, Y., Xiong, J., Shu, Z., Chao, D., Gu, T., Sun, P., He, S., Jiang, M., Xia, Z., Xue, J., et al. (2023) · Horticulture Research · doi:10.1093/hr/uhad027 · Cited by 96
ToxDL: deep learning using primary structure and domain embeddings for assessing protein toxicity
Pan, X., Zuallaert, J., Wang, X., Shen, H., Campos, E. P., Marushchak, D. O., & Neve, W. D. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa656 · Cited by 99
Polyploidy underlies co-option and diversification of biosynthetic triterpene pathways in the apple tribe
Su, W., Jing, Y., Lin, S., Yue, Z., Yang, X., Wu, J., Wu, J., Zhang, Z., Xia, R., Zhu, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101767118 · Cited by 98
ADAR2 increases in exercised heart and protects against myocardial infarction and doxorubicin-induced cardiotoxicity
Wu, X., Wang, L., Wang, K., Li, J., Chen, R., Wu, X., Ni, G., Liu, C., Das, S., Sluijter, J. P. G., et al. (2021) · Molecular Therapy · doi:10.1016/j.ymthe.2021.07.004 · Cited by 100
Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants
Jiang, Q., Lin, L., Xie, F., Jin, W., Zhu, W., Wang, M., Qiu, Q., Li, Z., Liu, J., & Mao, S. (2022) · Microbiome · doi:10.1186/s40168-022-01298-9 · Cited by 95
Genome-centric investigation of bile acid metabolizing microbiota of dairy cows and associated diet-induced functional implications
Lin, L., Lai, Z., Huisheng, Y., Zhang, J., Qi, W., Xie, F., & Mao, S. (2022) · The ISME Journal · doi:10.1038/s41396-022-01333-5 · Cited by 94
Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics
Shankar, A., & Sharma, K. K. (2022) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-022-11945-8 · Cited by 71
Large language models improve annotation of prokaryotic viral proteins
Flamholz, Z. N., Biller, S. J., & Kelly, L. (2024) · Nature Microbiology · doi:10.1038/s41564-023-01584-8 · Cited by 92
A polyene macrolide targeting phospholipids in the fungal cell membrane
Deng, Q., Li, Y., He, W., Chen, T., Liu, N., Ma, L., Qiu, Z., Shang, Z., & Wang, Z. (2025) · Nature · doi:10.1038/s41586-025-08678-9 · Cited by 98
Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms
Patowary, A., Zhang, P., Jops, C. T., Vuong, C. K., Ge, X., Hou, K., Kim, M., Gong, N. N., Margolis, M. P., Vo, D., et al. (2024) · Science · doi:10.1126/science.adh7688 · Cited by 90
RNA-binding and prion domains: the Yin and Yang of phase separation
Lorenzo-Gotor, N., Armaos, A., Calloni, G., Torrent, M., Vabulas, R. M., de Groot, N. S., & Tartaglia, G. G. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa681 · Cited by 82
Comparative functional genomics analysis of cytochrome P450 gene superfamily in wheat and maize
Li, Y., & Wei, K. (2020) · BMC Plant Biology · doi:10.1186/s12870-020-2288-7 · Cited by 119
How to use the MEROPS database and website to help understand peptidase specificity
Rawlings, N. D., & Bateman, A. G. (2020) · Protein Science · doi:10.1002/pro.3948 · Cited by 88
Insulin signaling regulates longevity through protein phosphorylation in Caenorhabditis elegans
Li, W.-J., Wang, C., Li, T., Yan, Y.-H., Zhang, M.-J., Liu, Z., Li, Y.-X., Zhao, H., Li, X.-M., He, X.-D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24816-z · Cited by 86
Predicting protein-protein interactions in the human proteome
Zhang, J., Humphreys, I. R., Pei, J., Kim, J., Choi, C., Yuan, R., Durham, J., Liu, S., Choi, H.-J., Baek, M., et al. (2025) · Science · doi:10.1126/science.adt1630 · Cited by 87
Genomic basis of high-altitude adaptation in Tibetan Prunus fruit trees
Wang, X., Liu, S., Zuo, H., Zheng, W., Zhang, S., Huang, Y., Pingcuo, G., Ying, H., Zhao, F., Li, Y., et al. (2021) · Current Biology · doi:10.1016/j.cub.2021.06.062 · Cited by 84
TADB 3.0: an updated database of bacterial toxin–antitoxin loci and associated mobile genetic elements
Guan, J., Chen, Y., Goh, Y., Wang, M., Tai, C., Deng, Z., Song, J., & Ou, H. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad962 · Cited by 83
DYW domain structures imply an unusual regulation principle in plant organellar RNA editing catalysis
Takenaka, M., Takenaka, S., Barthel, T., Frink, B., Haag, S., Verbitskiy, D., Oldenkott, B., Schallenberg‐Rüdinger, M., Feiler, C., Weiss, M. S., et al. (2021) · Nature Catalysis · doi:10.1038/s41929-021-00633-x · Cited by 85
Genome-wide identification and gene expression pattern of ABC transporter gene family in Capsicum spp.
Ortíz, C. E., Dutta, S. K., Natarajan, P., Peña-García, Y., Abburi, V. L., Saminathan, T., Nimmakayala, P., & Reddy, U. K. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215901 · Cited by 79
Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units
Zhao, Z., Gui, J., Yao, A., Le, N. Q. K., & Chua, M. C. H. (2022) · ACS Omega · doi:10.1021/acsomega.2c05881 · Cited by 80
Large-scale genome sequencing reveals the driving forces of viruses in microalgal evolution
Nelson, D. R., Hazzouri, K. M., Lauersen, K. J., Jaiswal, A. K., Chaiboonchoe, A., Mystikou, A., Fu, W., Daakour, S., Dohai, B., Alzahmi, A. S., et al. (2021) · Cell Host & Microbe · doi:10.1016/j.chom.2020.12.005 · Cited by 73
Cell division in the archaeon Haloferax volcanii relies on two FtsZ proteins with distinct functions in division ring assembly and constriction
Liao, Y., Ithurbide, S., Evenhuis, C. R., Löwe, J., & Duggin, I. G. (2021) · Nature Microbiology · doi:10.1038/s41564-021-00894-z · Cited by 78
A high-quality genome compendium of the human gut microbiome of Inner Mongolians
Jin, H., Quan, K., He, Q., Kwok, L., Ma, T., Li, Y., Zhao, F., You, L., Zhang, H., & Sun, Z. (2023) · Nature Microbiology · doi:10.1038/s41564-022-01270-1 · Cited by 74
Sulfoquinovose is a select nutrient of prominent bacteria and a source of hydrogen sulfide in the human gut
Hanson, B. T., Kits, K. D., Löffler, J., Burrichter, A. G., Fiedler, A. W., Denger, K., Frommeyer, B., Herbold, C. W., Rattei, T., Karcher, N., et al. (2021) · The ISME Journal · doi:10.1038/s41396-021-00968-0 · Cited by 70
A BRD4-mediated elongation control point primes transcribing RNA polymerase II for 3′-processing and termination
Arnold, M., Bressin, A., Jasnovidova, O., Meierhofer, D., & Mayer, A. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.06.026 · Cited by 75
A diverse uncultivated microbial community is responsible for organic matter degradation in the Black Sea sulphidic zone
Suominen, S., Dombrowski, N., Damsté, J. S. S., & Villanueva, L. (2019) · Environmental Microbiology · doi:10.1111/1462-2920.14902 · Cited by 73
A Family of T6SS Antibacterial Effectors Related to l,d-Transpeptidases Targets the Peptidoglycan
Sibinelli‐Sousa, S., Hespanhol, J. T., Nicastro, G. G., Matsuyama, B. Y., Mesnage, S., Patel, A. V., de Souza, R. F., Guzzo, C. R., & Bayer‐Santos, E. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107813 · Cited by 70
R2R3-MYB transcription factor family in tea plant (Camellia sinensis): Genome-wide characterization, phylogeny, chromosome location, structure and expression patterns
Chen, X., Wang, P., Gu, M., Lin, X.-Y., Hou, B., Zheng, Y., Sun, Y., Jin, S., & Ye, N. (2021) · Genomics · doi:10.1016/j.ygeno.2021.03.033 · Cited by 73
The Class III Peroxidase (POD) Gene Family in Cassava: Identification, Phylogeny, Duplication, and Expression
Wu, C., Ding, X., Ding, Z., Tie, W., Yan, Y., Wang, Y., Yang, H., & Hu, W. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112730 · Cited by 72
Origin and Evolution of Polycyclic Triterpene Synthesis
Santana‐Molina, C., Rivas‐Marín, E., Rojas, A. M., & Devos, D. P. (2020) · Molecular Biology and Evolution · doi:10.1093/molbev/msaa054 · Cited by 64
Genome-wide identification and expression analysis of the CBF/DREB1 gene family in lettuce
Park, S., Shi, A., & Mou, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-62458-1 · Cited by 67
Spatiotemporal dynamics of GEF-H1 activation controlled by microtubule- and Src-mediated pathways
Azoitei, M. L., Noh, J., Marston, D. J., Roudot, P., Marshall, C. B., Daugird, T. A., Lisanza, S. L., Sandí, M.-J., Ikura, M., Sondek, J., et al. (2019) · Journal of Cell Biology · doi:10.1083/jcb.201812073 · Cited by 61
Genome-wide identification and expression analysis of the WRKY genes in sugar beet ( Beta vulgaris L.) under alkaline stress
Wu, G.-Q., Li, Z., Cao, H., & Wang, J. (2019) · PeerJ · doi:10.7717/peerj.7817 · Cited by 69
Metagenome-derived virus-microbe ratios across ecosystems
López‐García, P., Gutiérrez‐Preciado, A., Krupovìč, M., Ciobanu, M., Deschamps, P., Jardillier, L. E., López‐Pérez, M., Rodríguez‐Valera, F., & Moreira, D. (2023) · The ISME Journal · doi:10.1038/s41396-023-01431-y · Cited by 64

Authors: Potter, S. C., Luciani, A., Eddy, S. R., Park, Y., Lopez, R., & Finn, R. D.
Affiliations: European Bioinformatics Institute (GB)
Howard Hughes Medical Institute (US)
EMBL-EBI European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
Corresponding author: Finn, Robert D
ORCID: Potter, Simon C
Luciani, Aurélien
Eddy, Sean R
Finn, Robert D
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2018-06-14
Volume: 46
Issue: W1
Pages: W200-W204
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gky448
PMC: PMC6030962
OpenAlex: W2918063357
Cited by: 2,607
References: 30
Citations by year: 2026: 348 · 2025: 433 · 2024: 407 · 2023: 385 · 2022: 372 · 2021: 357 · 2020: 208 · 2019: 88 · 2018: 9
FWCI: 61.61
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/4.0/ (vor)
Locations: Nucleic Acids Research (publishedVersion, cc-by-nc)
Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
PubMed Central (publishedVersion, cc-by-nc)
PubMed (publishedVersion)
Funders: Howard Hughes Medical Institute (R01 HG009116)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Keywords: profile hidden Markov models, sequence database searching, domain architecture, sequence databases
MeSH terms: Markov Chains, Software, User-Computer Interface, Sequence Analysis, Catalytic Domain, Internet, Sequence Analysis, Protein, Databases, Genetic

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

HMMER web server: 2018 update

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks