The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Open accessAbstract
Motivation: In 2001 and 2002, we published two papers (Bioinformatics, 17, 282–283, Bioinformatics, 18, 77–82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST. Availability: Contact: liwz@sdsc.edu
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
Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach
Manichanh, C., Rigottier-Gois, L., Bonnaud, E., Gloux, K., Pelletier, E., Frangeul, L., Nalin, R., Jarrin, C., Chardon, P., Marteau, P., et al. (2006) · Gut · doi:10.1136/gut.2005.073817 · Cited by 2,277
Mcbain, A. J., & Macfarlane, G. T. (1998) · Journal of Medical Microbiology · doi:10.1099/00222615-47-5-407 · Cited by 179
Beaud, D., Tailliez, P., & Anba-Mondoloni, J. (2005) · Microbiology · doi:10.1099/mic.0.27712-0 · Cited by 160
Research works printed but not linked: 5
- Apweiler, 2004, UniProt: the Universal Protein knowledgebase, Nucleic Acids Res., 32, D115, 10.1093/nar/gkh131 · DOI
- Bourne, 2004, The distribution and query systems of the RCSB Protein Data Bank, Nucleic Acids Res., 32, D223, 10.1093/nar/gkh096 · DOI
- Li, 2001, Clustering of highly homologous sequences to reduce the size of large protein databases, Bioinformatics, 17, 282, 10.1093/bioinformatics/17.3.282 · DOI · Paper page
- Li, 2002, Sequence clustering strategies improve remote homology recognitions while reducing search times, Bioinformatics, 15, 643
- Li, 2002, Tolerating some redundancy significantly speeds up clustering of large protein databases, Protein Eng., 18, 77
Cited by
Items 101–200 of 223
Lohmann, P., Luna, R. A., Hollister, E. B., Devaraj, S., Mistretta, T.-A., Welty, S. E., & Versalovic, J. (2014) · Pediatric Research · doi:10.1038/pr.2014.85 · Cited by 146
A structure-informed atlas of human-virus interactions
Lasso, G., Mayer, S. V., Winkelmann, E. R., Chu, T., Elliot, O., Patino-Galindo, J. A., Park, K., Rabadan, R., Honig, B., & Shapira, S. D. (2019) · Cell · doi:10.1016/j.cell.2019.08.005 · Cited by 133
Unifying the known and unknown microbial coding sequence space
Vanni, C., Schechter, M. S., Acinas, S. G., Barberán, A., Buttigieg, P. L., Casamayor, E. O., Delmont, T. O., Duarte, C. M., Eren, A. M., Finn, R. D., et al. (2022) · eLife · doi:10.7554/elife.67667 · Cited by 134
DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network
Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006718 · Cited by 133
Walia, R. R., Xue, L. C., Wilkins, K., El-Manzalawy, Y., Dobbs, D., & Honavar, V. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097725 · Cited by 132
The Lactobacillus Bile Salt Hydrolase Repertoire Reveals Niche-Specific Adaptation
O’Flaherty, S., Briner Crawley, A., Theriot, C. M., & Barrangou, R. (2018) · mSphere · doi:10.1128/msphere.00140-18 · Cited by 118
Multiple independent origins of apicomplexan-like parasites
Mathur, V., Kolísko, M., Hehenberger, E., Irwin, N. A. T., Leander, B. S., Kristmundsson, Á., Freeman, M. A., & Keeling, P. J. (2019) · Current Biology · doi:10.1016/j.cub.2019.07.019 · Cited by 126
ABangle: characterising the VH-VL orientation in antibodies
Dunbar, J., Fuchs, A., Shi, J., & Deane, C. M. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt020 · Cited by 125
Furin cleavage sites naturally occur in coronaviruses
Wu, Y., & Zhao, S. (2021) · Stem Cell Research · doi:10.1016/j.scr.2020.102115 · Cited by 117
Gut resistome development in healthy twin pairs in the first year of life
Moore, A. M., Ahmadi, S., Patel, S., Gibson, M. K., Wang, B., Ndao, I. M., Deych, E., Shannon, W., Tarr, P. I., Warner, B. B., et al. (2015) · Microbiome · doi:10.1186/s40168-015-0090-9 · Cited by 109
The active human gut microbiota differs from the total microbiota
Peris-Bondia, F., Latorre, A., Artacho, A., Moya, A., & D'Auria, G. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022448 · Cited by 113
Identification of Antioxidants from Sequence Information Using Naïve Bayes
Feng, P.-M., Lin, H., & Chen, W. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/567529 · Cited by 109
Mirusviruses link herpesviruses to giant viruses
Gaïa, M., Meng, L., Pelletier, E., Forterre, P., Vanni, C., Fernandez-Guerra, A., Jaillon, O., Wincker, P., Ogata, H., Krupovic, M., et al. (2023) · Nature · doi:10.1038/s41586-023-05962-4 · Cited by 108
Bian, C., Hu, Y., Ravi, V., Kuznetsova, I. S., Shen, X., Mu, X., Sun, Y., You, X., Li, J., Li, X., et al. (2016) · Scientific Reports · doi:10.1038/srep24501 · Cited by 107
Self-citationThézé, J., Li, T., du Plessis, L., Bouquet, J., Kraemer, M. U. G., Somasekar, S., Yu, G., de Cesare, M., Balmaseda, A., Kuan, G., et al. (2018) · Cell Host & Microbe · doi:10.1016/j.chom.2018.04.017 · Cited by 106
Topological structures and syntenic conservation in sea anemone genomes
Zimmermann, B., Montenegro, J. D., Robb, S. M. C., Fropf, W. J., Weilguny, L., He, S., Chen, S., Lovegrove-Walsh, J., Hill, E. M., Chen, C.-Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-44080-7 · Cited by 106
Wang, Y., Wang, S., Wu, C., Chen, X., Duan, Z., Xu, Q., Jiang, W., Xu, L., Wang, T., Su, L., et al. (2019) · mSystems · doi:10.1128/msystems.00450-19 · Cited by 105
LOGICOIL—multi-state prediction of coiled-coil oligomeric state
Vincent, T. L., Green, P. J., & Woolfson, D. N. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts648 · Cited by 103
DLAB-deep learning methods for structure-based virtual screening of antibodies
Schneider, C., Buchanan, A., Taddese, B., & Deane, C. M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab660 · Cited by 102
Carberry, C. A., Waters, S. M., Kenny, D. A., & Creevey, C. J. (2014) · Applied and Environmental Microbiology · doi:10.1128/aem.03131-13 · Cited by 100
Multicomponent nature underlies the extraordinary mechanical properties of spider dragline silk
Kono, N., Nakamura, H., Mori, M., Yoshida, Y., Ohtoshi, R., Malay, A. D., Pedrazzoli Moran, D. A., Tomita, M., Numata, K., & Arakawa, K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2107065118 · Cited by 99
The evolution and structure prediction of coiled coils across all genomes.
Rackham, O. J. L., Madera, M., Armstrong, C. T., Vincent, T. L., Woolfson, D. N., & Gough, J. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.08.032 · Cited by 97
Prediction of site-specific interactions in antibody–antigen complexes: the proABC method and server
Olimpieri, P. P., Chailyan, A., Tramontano, A., & Marcatili, P. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt369 · Cited by 90
ATP synthase evolution on a cross-braced dated tree of life
Mahendrarajah, T. A., Moody, E. R. R., Schrempf, D., Szánthó, L. L., Dombrowski, N., Davín, A. A., Pisani, D., Donoghue, P. C. J., Szöllősi, G. J., Williams, T. A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42924-w · Cited by 86
An overview of the prediction of protein DNA-binding sites
Si, J., Zhao, R., & Wu, R. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16035194 · Cited by 91
Nuclear Wave1 Is Required for Reprogramming Transcription in Oocytes and for Normal Development
Miyamoto, K., Teperek, M., Yusa, K., Allen, G. E., Bradshaw, C. R., & Gurdon, J. B. (2013) · Science · doi:10.1126/science.1240376 · Cited by 91
Antibody interface prediction with 3D Zernike descriptors and SVM
Daberdaku, S., & Ferrari, C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty918 · Cited by 88
Rational Engineering of Enzyme Allosteric Regulation through Sequence Evolution Analysis
Yang, J.-S., Seo, S. W., Jang, S., Jung, G. Y., & Kim, S. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002612 · Cited by 87
Krawczyk, K., Baker, T., Shi, J., & Deane, C. M. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt043 · Cited by 87
AlphaFold2 Can Predict Single-Mutation Effects
McBride, J. M., Polev, K., Abdirasulov, A., Reinharz, V., Grzybowski, B. A., & Tlusty, T. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.218401 · Cited by 87
The diversity of H 3 loops determines the antigen‐binding tendencies of antibody CDR loops
Tsuchiya, Y., & Mizuguchi, K. (2016) · Protein Science · doi:10.1002/pro.2874 · Cited by 86
Bromberg, J. S., Hittle, L., Xiong, Y., Saxena, V., Smyth, E. M., Li, L., Zhang, T., Wagner, C., Fricke, W. F., Simon, T., et al. (2018) · JCI Insight · doi:10.1172/jci.insight.121045 · Cited by 73
Gut microbiota signature in treatment-naïve attention-deficit/hyperactivity disorder
Richarte, V., Sánchez-Mora, C., Corrales, M., Fadeuilhe, C., Vilar-Ribó, L., Arribas, L., Garcia, E., Rosales-Ortiz, S. K., Arias-Vasquez, A., Soler-Artigas, M., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01504-6 · Cited by 75
Zhang, D., Zhang, J., Kalimuthu, S., Liu, J., Song, Z.-M., He, B.-B., Cai, P., Zhong, Z., Feng, C., Neelakantan, P., et al. (2023) · Microbiome · doi:10.1186/s40168-023-01540-y · Cited by 72
“Those sequences were then grouped into clusters using Cd-hit [72], with the parameters of “-n 2 -p 1 -c 0.5 -d 200 -”
InfluentialUsing a combined computational-experimental approach to predict antibody-specific B cell epitopes
Sela-Culang, I., Benhnia, M. R.-E.-I., Matho, M. H., Kaever, T., Maybeno, M., Schlossman, A., Nimrod, G., Li, S., Xiang, Y., Zajonc, D., et al. (2014) · Structure · doi:10.1016/j.str.2014.02.003 · Cited by 69
Endosymbiotic gene transfer in tertiary plastid-containing dinoflagellates
Burki, F., Imanian, B., Hehenberger, E., Hirakawa, Y., Maruyama, S., & Keeling, P. J. (2014) · Eukaryotic Cell · doi:10.1128/ec.00299-13 · Cited by 67
“These unique queries were used to search with BLASTP a curated in-house protein database (see Table S1 in the supplemental material for the complete list of taxa) that was subjected to CDHIT (55) to remove redundant sequences and close paralogs in order to simplify interpretations of the downstream phylogenetic trees.”
MethodYao, G., Yu, J., Hou, Q., Hui, W., Liu, W., Kwok, L.-Y., Menghe, B., Sun, T., Zhang, H., & Zhang, W. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.00165 · Cited by 66
Exploring the reservoir of potential fungal plant pathogens in agricultural soil
Agtmaal, M. V., Straathof, A., Termorshuizen, A., Teurlincx, S., Hundscheid, M., Ruyters, S., Busschaert, P., Lievens, B., & Boer, W. D. (2017) · Applied Soil Ecology · doi:10.1016/j.apsoil.2017.09.032 · Cited by 62
Transposon-encoded nucleases use guide RNAs to promote their selfish spread
Meers, C., Le, H. C., Pesari, S. R., Hoffmann, F. T., Walker, M. W. G., Gezelle, J., Tang, S., & Sternberg, S. H. (2023) · Nature · doi:10.1038/s41586-023-06597-1 · Cited by 60
De novo gene synthesis by an antiviral reverse transcriptase
Tang, S., Conte, V., Zhang, D. J., Žedaveinytė, R., Lampe, G. D., Wiegand, T., Tang, L. C., Wang, M., Walker, M. W. G., George, J. T., et al. (2024) · Science · doi:10.1126/science.adq0876 · Cited by 58
Ringel-Kulka, T., Benson, A. K., Carroll, I. M., Kim, J., Legge, R. M., & Ringel, Y. (2016) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00044.2015 · Cited by 53
Functional Strain Redundancy and Persistent Phage Infection in Swiss Hard Cheese Starter Cultures
Somerville, V., Berthoud, H., Schmidt, R. S., Bachmann, H.-P., Meng, Y. H., Fuchsmann, P., von Ah, U., & Engel, P. (2022) · The ISME Journal · doi:10.1038/s41396-021-01071-0 · Cited by 55
Prediction of VH-VL domain orientation for antibody variable domain modeling
Bujotzek, A., Dunbar, J., Lipsmeier, F., Schäfer, W., Antes, I., Deane, C. M., & Georges, G. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24756 · Cited by 56
ERNIE-RNA: an RNA language model with structure-enhanced representations
Yin, W., Zhang, Z., Zhang, S., He, L., Zhang, R., Jiang, R., Liu, G., Wang, J., Zhang, X., Qin, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64972-0 · Cited by 55
Hawkins, M. T. R., Hofman, C. A., Callicrate, T., McDonough, M. M., Tsuchiya, M. T. N., Gutiérrez, E. E., Helgen, K. M., & Maldonado, J. E. (2016) · Molecular Ecology Resources · doi:10.1111/1755-0998.12448 · Cited by 53
Ferrer Florensa, A., Almagro Armenteros, J. J., Kaas, R. S., Clausen, P. T. L. C., Nielsen, H., Rost, B., & Aarestrup, F. M. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag129 · Cited by 50
Gut microbiota-derived hexa-acylated lipopolysaccharides enhance cancer immunotherapy responses
Sardar, P., Beresford-Jones, B. S., Xia, W., Shabana, O., Suyama, S., Ramos, R. J. F., Soderholm, A. T., Tourlomousis, P., Kuo, P., Evans, A. C., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-025-01930-y · Cited by 49
SCORER 2.0: an algorithm for distinguishing parallel dimeric and trimeric coiled-coil sequences
Armstrong, C. T., Vincent, T. L., Green, P. J., & Woolfson, D. N. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr299 · Cited by 49
Lei, Z., Zhang, K., Li, C., Jiao, T., Wu, J., Wei, Y., Tian, K., Li, C., Tang, D., Davis, D. I., et al. (2019) · BMC Microbiology · doi:10.1186/s12866-019-1400-3 · Cited by 48
Dey, D., Nagaraja, V., & Ramakumar, S. (2017) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2016.11.014 · Cited by 46
Zhang, Q., Linke, V., Overmyer, K. A., Traeger, L. L., Kasahara, K., Miller, I. J., Manson, D. E., Polaske, T. J., Kerby, R. L., Kemis, J. H., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01326-w · Cited by 43
Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking
Jeppesen, M., & André, I. (2023) · Nature Communications · doi:10.1038/s41467-023-43681-6 · Cited by 44
Xu, T., Qin, S., Hu, Y., Song, Z., Ying, J., Li, P., Dong, W., Zhao, F., Yang, H., & Bao, Q. (2016) · DNA Research · doi:10.1093/dnares/dsw023 · Cited by 37
Derrien, M., Mikulic, N., Uyoga, M. A., Chenoll, E., Climent, E., Howard-Varona, A., Nyilima, S., Stoffel, N. U., Karanja, S., Kottler, R., et al. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2178793 · Cited by 36
Kudo, A., Shigenobu, S., Kadota, K., Nozawa, M., Shibata, T. F., Ishikawa, Y., & Matsuo, T. (2017) · Insect Biochemistry and Molecular Biology · doi:10.1016/j.ibmb.2017.01.006 · Cited by 37
Lignocellulose degradation in isopods: new insights into the adaptation to terrestrial life
Bredon, M., Herran, B., Lheraud, B., Bertaux, J., Grève, P., Moumen, B., & Bouchon, D. (2019) · BMC Genomics · doi:10.1186/s12864-019-5825-8 · Cited by 36
Candyrine, S. C. L., Mahadzir, M. F., Garba, S., Jahromi, M. F., Ebrahimi, M., Goh, Y. M., Samsudin, A. A., Sazili, A. Q., Chen, W. L., Ganesh, S., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0199840 · Cited by 31
Are protein-protein interfaces special regions on a protein’s surface?
Tonddast-Navaei, S., & Skolnick, J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4937428 · Cited by 31
TransPTM: a transformer-based model for non-histone acetylation site prediction
Meng, L., Chen, X., Cheng, K., Chen, N., Zheng, Z., Wang, F., Sun, H., & Wong, K.-C. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae219 · Cited by 31
ProkBERT family: genomic language models for microbiome applications
Ligeti, B., Szepesi-Nagy, I., Bodnár, B., Ligeti-Nagy, N., & Juhász, J. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1331233 · Cited by 30
Bennett, M. R., Bombardi, R. G., Kose, N., Parrish, E. H., Nagel, M. B., Petit, R. A., Read, T. D., Schey, K. L., Thomsen, I. P., Skaar, E. P., et al. (2019) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiy635 · Cited by 29
Van Vlierberghe, M., Di Franco, A., Philippe, H., & Baurain, D. (2021) · BMC Research Notes · doi:10.1186/s13104-021-05717-2 · Cited by 27
No ancient DNA damage in actinobacteria from the neanderthal bone
Zaremba-Niedźwiedzka, K., & Andersson, S. G. E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062799 · Cited by 26
Seq-InSite: sequence supersedes structure for protein interaction site prediction
Hosseini, S., Golding, G. B., & Ilie, L. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btad738 · Cited by 26
Critical assessment of coiled-coil predictions based on protein structure data
Simm, D., Hatje, K., Waack, S., & Kollmar, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-91886-w · Cited by 24
Contrasting strategies for sucrose utilization in a floral yeast clade
Gonçalves, C., Marques, M., & Gonçalves, P. (2022) · mSphere · doi:10.1128/msphere.00035-22 · Cited by 23
Himmel, N. J., Moi, D., & Benton, R. (2023) · Current Biology · doi:10.1016/j.cub.2023.10.008 · Cited by 23
Antibody side chain conformations are position-dependent
Leem, J., Georges, G., Shi, J., & Deane, C. M. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25453 · Cited by 22
Evaluation of genetic demultiplexing of single-cell sequencing data from model species
Cardiello, J. F., Joven Araus, A., Giatrellis, S., Helsens, C., Simon, A., & Leigh, N. D. (2023) · Life Science Alliance · doi:10.26508/lsa.202301979 · Cited by 21
“Transcripts were clustered with cd-hit-est (Li & Godzik, 2006; Fu et al, 2012). cd-hit-est -o s -d -c 0.98 -i reference_transcripts_v2.fa -p 1 -d 0 -b 3 -”
InfluentialLi, Q., Cheng, X., Song, C., & Liu, T. (2023) · Symmetry · doi:10.3390/sym15030731 · Cited by 20
Benchmarking protein language models for protein crystallization
Mall, R., Kaushik, R., Martinez, Z. A., Thomson, M. W., & Castiglione, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-86519-5 · Cited by 20
Choi, J., Rieke, E. L., Moorman, T. B., Soupir, M. L., Allen, H. K., Smith, S. D., & Howe, A. (2018) · FEMS Microbiology Ecology · doi:10.1093/femsec/fiy006 · Cited by 19
Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins
Wojciechowski, J. W., Tekoglu, E., Gąsior-Głogowska, M., Coustou, V., Szulc, N., Szefczyk, M., Kopaczyńska, M., Saupe, S. J., & Dyrka, W. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010787 · Cited by 19
Bioinformatics Analysis of Bacterial Annexins – Putative Ancestral Relatives of Eukaryotic Annexins
Kodavali, P. K., Dudkiewicz, M., Pikuła, S., & Pawłowski, K. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085428 · Cited by 18
Deep generalizable prediction of RNA secondary structure via base pair motif energy
Zhu, H., Tang, F., Quan, Q., Chen, K., Xiong, P., & Zhou, S. K. (2025) · Nature Communications · doi:10.1038/s41467-025-60048-1 · Cited by 17
Tian, X., Wang, R., Liu, Z., Lu, S., Chen, X., Zhang, Z., Liu, F., Li, H., Zhang, X., & Wang, M. (2025) · Genome Biology · doi:10.1186/s13059-025-03534-5 · Cited by 16
Investigation into host selection of the cecal acetogen population in rabbits after weaning.
Yang, C., Mi, L., Hu, X., Liu, J., & Wang, J. (2016) · PLOS ONE · doi:10.1371/journal.pone.0158768 · Cited by 15
Stroynowska-Czerwinska, A. M., Klimczak, M., Pastor, M., Kazrani, A. A., Misztal, K., & Bochtler, M. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04651-1 · Cited by 14
Chitwood, D. G., Wang, Q., Klaubert, S. R., Green, K., Wu, C. H., Harcum, S. W., & Saski, C. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-27962-0 · Cited by 12
Fecal microbiota of captive Antillean manatee Trichechus manatus manatus
Suzuki, A., Ueda, K., Segawa, T., & Suzuki, M. (2019) · FEMS Microbiology Letters · doi:10.1093/femsle/fnz134 · Cited by 12
Human Gut Metagenomes Encode Diverse GH156 Sialidases
Mann, E., Shekarriz, S., & Surette, M. G. (2022) · Applied and Environmental Microbiology · doi:10.1128/aem.01755-22 · Cited by 12
Functional Ambidexterity of an Ancient Nucleic Acid‐Binding Domain
Weil‐Ktorza, O., Naveh‐Tassa, S., Fridmann‐Sirkis, Y., Despotović, D., Cherukuri, K. P., Corlett, T., Levy, Y., Metanis, N., & Longo, L. M. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202505188 · Cited by 12
CC + : A searchable database of validated coiled coils in PDB structures and AlphaFold2 models
Kumar, P., Petrenas, R., Dawson, W. M., Schweke, H., Levy, E. D., & Woolfson, D. N. (2023) · Protein Science · doi:10.1002/pro.4789 · Cited by 11
“CD-Hit (Fu et al., 2012; Li & Godzik, 2006) was used to categorize sequences into four groups based on redundancy: ≤ 50% identity, ≤ 70% identity, non-redundant (<100% identity), and redundant (i.e., all examples are included, including those with identical sequences).”
MethodProteinGLUE multi-task benchmark suite for self-supervised protein modeling
Capel, H., Weiler, R., Dijkstra, M., Vleugels, R., Bloem, P., & Feenstra, K. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-19608-4 · Cited by 11
Said, N., Finazzo, M., Hilal, T., Wang, B., Selinger, T. L., Gjorgjevikj, D., Artsimovitch, I., & Wahl, M. C. (2024) · Nature Communications · doi:10.1038/s41467-024-47439-6 · Cited by 10
Designing novel solenoid proteins with in silico evolution
(2025) · doi:10.1038/s42004-025-01817-3 · Cited by 10
Learning from the unknown: exploring the range of bacterial functionality
Mahlich, Y., Zhu, C., Chung, H., Velaga, P. K., De Paolis Kaluza, M. C., Radivojac, P., Friedberg, I., & Bromberg, Y. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad757 · Cited by 10
ParaSurf: a surface-based deep learning approach for paratope–antigen interaction prediction
Papadopoulos, A.-M., Axenopoulos, A., Iatrou, A., Stamatopoulos, K., Alvarez, F., & Daras, P. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf062 · Cited by 9
SUPREM: an engineered non-site-specific m6A RNA methyltransferase with highly improved efficiency
Ochiai, Y., Clifton, B. E., Le Coz, M., Terenzio, M., & Laurino, P. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae887 · Cited by 9
Vidal-Henriquez, E., Holder, T., Lee, N. F., Pompe, C., & Teese, M. G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.10.026 · Cited by 9
Singer, A., Ramos, A., & Keating, A. E. (2024) · Protein Science · doi:10.1002/pro.5094 · Cited by 6
Valencia, J. D., & Hendrix, D. A. (2023) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1011526 · Cited by 6
Metadata
- Authors
- Li, W., & Godzik, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-05-26
- Volume
- 22
- Issue
- 13
- Pages
- 1658-1659
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2156125289
- Cited by
- 11,410
- References
- 3
- FWCI
- 30.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
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.
