Cd-hit: Accelerated for clustering the next-generation sequencing data
Open accessAbstract
SUMMARY: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. AVAILABILITY: http://cd-hit.org. CONTACT: liwz@sdsc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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
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?
Homologous sequences with more than 60% similarity were removed from the dataset using CD-HIT to avoid overestimation of performance.
“To avoid the overestimation of the performance of the methods, the homologous sequences with more than 60% similarity were removed from the dataset by using CD-HIT program [45].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [45]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 20,617 · see citing works
A human gut microbial gene catalogue established by metagenomic sequencing
MetaHIT Consortium, Qin, J., Li, R., Raes, J., Arumugam, M., Burgdorf, K. S., Manichanh, C., Nielsen, T., Pons, N., Levenez, F., et al. (2010) · Nature · doi:10.1038/nature08821 · Cited by 11,624 · see citing works
Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences
Li, W.-Z., & Godzik, A. (2006) · Bioinform. · doi:10.1093/bioinformatics/btl158 · Cited by 10,994 · see citing works
Self-citationA core gut microbiome in obese and lean twins
Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan, A., Ley, R. E., Sogin, M. L., Jones, W. J., Roe, B. A., Affourtit, J. P., et al. (2009) · Nature · doi:10.1038/nature07540 · Cited by 7,823 · see citing works
UniRef: comprehensive and non-redundant UniProt reference clusters
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R., & Wu, C. H. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm098 · Cited by 1,613 · see citing works
Clustering of highly homologous sequences to reduce the size of large protein databases
Li, W.-Z., Jaroszewski, L., & Godzik, A. (2001) · Bioinform. · doi:10.1093/bioinformatics/17.3.282 · Cited by 1,021 · see citing works
Self-citationSun, S., Chen, J., Li, W., Altıntaş, İ., Lin, A. W., Peltier, S., Stocks, K. I., Allen, E. E., Ellisman, M. H., Grethe, J. S., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1102 · Cited by 327 · see citing works
Self-citationArtificial and natural duplicates in pyrosequencing reads of metagenomic data
Niu, B., Fu, L., Sun, S., & Li, W. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-187 · Cited by 278 · see citing works
Self-citationLoong, S. N. K., & Mishra, S. K. (2006) · RNA · doi:10.1261/rna.223807 · Cited by 76 · see citing works
Yooseph, S., Li, W., & Sutton, G. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-182 · Cited by 50 · see citing works
Self-citationPredicting disulfide bond connectivity in proteins by correlated mutations analysis
Rubinstein, R., & Fiser, A. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm637 · Cited by 44 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
metaSPAdes: a new versatile metagenomic assembler
Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017) · Genome Research · doi:10.1101/gr.213959.116 · Cited by 4,496 · see citing works
Ashkenazy, H., Abadi, S., Martz, E., Chay, O., Mayrose, I., Pupko, T., & Ben-Tal, N. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw408 · Cited by 3,163 · see citing works
KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold
Aramaki, T., Blanc-Mathieu, R., Endo, H., Ohkubo, K., Kanehisa, M., Goto, S., & Ogata, H. (2019) · doi:10.1101/602110 · Cited by 2,054 · see citing works
Disease-Specific Alterations in the Enteric Virome in Inflammatory Bowel Disease
Norman, J. M., Handley, S. A., Baldridge, M. T., Droit, L., Liu, C. Y., Keller, B. C., Kambal, A., Monaco, C. L., Zhao, G., Fleshner, P., et al. (2015) · Cell · doi:10.1016/j.cell.2015.01.002 · Cited by 1,285 · see citing works
A new genomic blueprint of the human gut microbiota
Almeida, A., Mitchell, A. L., Boland, M., Forster, S. C., Gloor, G. B., Tarkowska, A., Lawley, T. D., & Finn, R. D. (2019) · Nature · doi:10.1038/s41586-019-0965-1 · Cited by 1,314 · see citing works
Scheiman, J., Luber, J. M., Chavkin, T. A., MacDonald, T., Tung, A., Pham, L.-D., Wibowo, M. C., Wurth, R. C., Punthambaker, S., Tierney, B. T., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0485-4 · Cited by 892 · see citing works
The microbiome of uncontacted Amerindians
Clemente, J. C., Pehrsson, E. C., Blaser, M. J., Sandhu, K., Gao, Z., Wang, B., Magris, M., Hidalgo, G., Contreras, M., Noya-Alarcón, Ó., et al. (2015) · Science Advances · doi:10.1126/sciadv.1500183 · Cited by 851 · see citing works
Domestication and Divergence of Saccharomyces cerevisiae Beer Yeasts
Gallone, B., Steensels, J., Prahl, T., Soriaga, L., Saels, V., Herrera-Malaver, B., Merlevede, A., Roncoroni, M., Voordeckers, K., Miraglia, L., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.020 · Cited by 679 · see citing works
Isolation of an archaeon at the prokaryote–eukaryote interface
Imachi, H., Nobu, M. K., Nakahara, N., Morono, Y., Ogawara, M., Takaki, Y., Takano, Y., Uematsu, K., Ikuta, T., Ito, M., et al. (2020) · Nature · doi:10.1038/s41586-019-1916-6 · Cited by 624 · see citing works
Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians
Sato, Y., Atarashi, K., Plichta, D. R., Arai, Y., Sasajima, S., Kearney, S. M., Suda, W., Takeshita, K., Sasaki, T., Okamoto, S., et al. (2021) · Nature · doi:10.1038/s41586-021-03832-5 · Cited by 632 · see citing works
Modeling the language of life – Deep Learning Protein Sequences
Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606 · see citing works
Predicting multiple conformations via sequence clustering and AlphaFold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545 · see citing works
Cirelli, K. M., Carnathan, D. G., Nogal, B., Martin, J. T., Rodriguez, O. L., Upadhyay, A. A., Enemuo, C. A., Gebru, E. H., Choe, Y., Viviano, F., et al. (2019) · Cell · doi:10.1016/j.cell.2019.04.012 · Cited by 433 · see citing works
Adaptive Evolution within Gut Microbiomes of Healthy People
Zhao, S., Lieberman, T. D., Poyet, M., Kauffman, K. M., Gibbons, S. M., Groussin, M., Xavier, R. J., & Alm, E. J. (2019) · Cell Host & Microbe · doi:10.1016/j.chom.2019.03.007 · Cited by 434 · see citing works
A multidimensional systems biology analysis of cellular senescence in aging and disease
Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., Binetti, P., Budovsky, A., Chatsirisupachai, K., Johnson, E., Murray, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-01990-9 · Cited by 399 · see citing works
Guo, S.-H., Deng, E.-Z., Xu, L.-Q., Ding, H., Lin, H., Chen, W., & Chou, K.-C. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu083 · Cited by 375 · see citing works
Benchmarking <scp>AlphaFold</scp> for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2022) · Protein Science · doi:10.1002/pro.4379 · Cited by 335 · see citing works
Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes
Heintz-Buschart, A., May, P., Laczny, C. C., Lebrun, L. A., Bellora, C., Krishna, A., Wampach, L., Schneider, J. G., Hogan, A., de Beaufort, C., et al. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.180 · Cited by 316 · see citing works
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants
Yin, R., Feng, B. Y., Varshney, A., & Pierce, B. G. (2021) · doi:10.1101/2021.10.23.465575 · Cited by 335 · see citing works
Strassert, J. F. H., Irisarri, I., Williams, T. A., & Burki, F. (2021) · Nature Communications · doi:10.1038/s41467-021-22044-z · Cited by 298 · see citing works
High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome
Zheng, W., Zhao, S., Yin, Y., Zhang, H., Needham, D. M., Evans, E. D., Dai, C. L., Lu, P. J., Alm, E. J., & Weitz, D. A. (2022) · Science · doi:10.1126/science.abm1483 · Cited by 283 · see citing works
iRNA-PseU: Identifying RNA pseudouridine sites
Chen, W., Tang, H., Ye, J., Lin, H., & Chou, K.-C. (2016) · Molecular Therapy - Nucleic Acids · doi:10.1038/mtna.2016.37 · Cited by 291 · see citing works
Wesolowska-Andersen, A., Bahl, M. I., Carvalho, V., Kristiansen, K., Sicheritz-Pontén, T., Gupta, R., & Licht, T. R. (2014) · Microbiome · doi:10.1186/2049-2618-2-19 · Cited by 277 · see citing works
Tubiana, J., Schneidman-Duhovny, D., & Wolfson, H. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01490-7 · Cited by 274 · see citing works
Structure of the bacterial ribosome at 2 Å resolution
Watson, Z. L., Ward, F. R., Méheust, R., Ad, O., Schepartz, A., Banfield, J. F., & Cate, J. H. (2020) · eLife · doi:10.7554/elife.60482 · Cited by 263 · see citing works
Large-Scale Analyses of Human Microbiomes Reveal Thousands of Small, Novel Genes
Sberro, H., Fremin, B. J., Zlitni, S., Edfors, F., Greenfield, N., Snyder, M. P., Pavlopoulos, G. A., Kyrpides, N. C., & Bhatt, A. S. (2019) · Cell · doi:10.1016/j.cell.2019.07.016 · Cited by 237 · see citing works
Vollmers, J., Wiegand, S., & Kaster, A.-K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169662 · Cited by 240 · see citing works
Discovering highly potent antimicrobial peptides with deep generative model HydrAMP
Szymczak, P., Możejko, M., Grzegorzek, T., Jurczak, R., Bauer, M., Neubauer, D., Sikora, K., Michalski, M., Sroka, J., Setny, P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36994-z · Cited by 231 · see citing works
Jang, L.-G., Choi, G., Kim, S.-W., Kim, B.-Y., Lee, S., & Park, H. (2019) · Journal of the International Society of Sports Nutrition · doi:10.1186/s12970-019-0290-y · Cited by 224 · see citing works
Spang, A., Stairs, C. W., Dombrowski, N., Eme, L., Lombard, J., Caceres, E. F., Greening, C., Baker, B. J., & Ettema, T. J. G. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0406-9 · Cited by 200 · see citing works
Bioinformatic and Statistical Analysis of Adaptive Immune Repertoires
Greiff, V., Miho, E., Menzel, U., & Reddy, S. T. (2015) · Trends in Immunology · doi:10.1016/j.it.2015.09.006 · Cited by 210 · see citing works
Chen, J., Hu, Z., Sun, S., Tan, Q., Wang, Y., Yu, Q., Zong, L., Hong, L., Xiao, J., Shen, T., et al. (2022) · doi:10.1101/2022.08.06.503062 · Cited by 247 · see citing works
Birth, D., Kao, W.-C., & Hunte, C. (2014) · Nature Communications · doi:10.1038/ncomms5029 · Cited by 205 · see citing works
A general model to predict small molecule substrates of enzymes based on machine and deep learning
Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38347-2 · Cited by 193 · see citing works
Foley, M. H., Walker, M. E., Stewart, A. K., O’Flaherty, S., Gentry, E. C., Patel, S., Beaty, V. V., Allen, G., Pan, M., Simpson, J. B., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01337-7 · Cited by 181 · see citing works
Greener, J. G., Kandathil, S. M., & Jones, D. T. (2019) · Nature Communications · doi:10.1038/s41467-019-11994-0 · Cited by 178 · see citing works
Zrimec, J., Börlin, C. S., Buric, F., Muhammad, A. S., Chen, R., Siewers, V., Verendel, V., Nielsen, J., Töpel, M., & Zelezniak, A. (2020) · Nature Communications · doi:10.1038/s41467-020-19921-4 · Cited by 174 · see citing works
Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure
Zrimec, J., Buric, F., Muhammad, A. S., Chen, R., Verendel, V., Töpel, M., & Zelezniak, A. (2019) · doi:10.1101/792531 · Cited by 173 · see citing works
Association between gut microbiota and bone health: Potential mechanisms and prospective
Chen, Y.-C., Greenbaum, J., Shen, H., & Deng, H.-W. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-00513 · Cited by 165 · see citing works
Philippe, H., Poustka, A. J., Chiodin, M., Hoff, K. J., Dessimoz, C., Tomiczek, B., Schiffer, P. H., Müller, S., Domman, D., Horn, M., et al. (2019) · Current Biology · doi:10.1016/j.cub.2019.04.009 · Cited by 152 · see citing works
Park, M., Choi, J., & Lee, H.-J. (2020) · Nutrients · doi:10.3390/nu12061815 · Cited by 139 · see citing works
AlphaFold2-aware protein–DNA binding site prediction using graph transformer
Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab564 · Cited by 137 · see citing works
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 · see citing works
The <i>Lactobacillus</i> 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 · see citing works
Humanization of antibodies using a machine learning approach on large-scale repertoire data
Marks, C., Hummer, A. M., Chin, M., & Deane, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab434 · Cited by 124 · see citing works
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 · see citing works
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107 · see citing works
Sequence-based predictive modeling to identify cancerlectins
Lai, H.-Y., Chen, X.-X., Chen, W., Tang, H., & Lin, H. (2017) · Oncotarget · doi:10.18632/oncotarget.15963 · Cited by 94 · see citing works
SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
Xu, L., Liang, G., Shi, S., & Liao, C. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19061773 · Cited by 95 · see citing works
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 · see citing works
Antibody interface prediction with 3D Zernike descriptors and SVM
Daberdaku, S., & Ferrari, C. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty918 · Cited by 88 · see citing works
Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences
Ruffolo, J. A., Nayfach, S., Gallagher, J., Bhatnagar, A., Beazer, J., Hussain, R., Russ, J., Yip, J., Hill, E., Pacesa, M., et al. (2024) · doi:10.1101/2024.04.22.590591 · Cited by 90 · see citing works
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 · see citing works
Yao, 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 · see citing works
Identifying antioxidant proteins by using optimal dipeptide compositions
Feng, P., Chen, W., & Lin, H. (2016) · Interdisciplinary Sciences: Computational Life Sciences · doi:10.1007/s12539-015-0124-9 · Cited by 60 · see citing works
The monoaminergic system is a bilaterian innovation
Goulty, M., Botton-Amiot, G., Rosato, E., Sprecher, S. G., & Feuda, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39030-2 · Cited by 60 · see citing works
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 · see citing works
Stein, D., Kars, M. E., Wu, Y., Bayrak, Ç. S., Stenson, P. D., Cooper, D. N., Schlessinger, A., & Itan, Y. (2023) · Genome Medicine · doi:10.1186/s13073-023-01261-9 · Cited by 53 · see citing works
Carrier, G., Garnier, M., Le Cunff, L., Bougaran, G., Probert, I., De Vargas, C., Corre, E., Cadoret, J.-P., & Saint-Jean, B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086889 · Cited by 52 · see citing works
Zeng, W., Dou, Y., Pan, L., Xu, L., & Peng, S. (2024) · Nature Communications · doi:10.1038/s41467-024-52293-7 · Cited by 49 · see citing works
DIP/Dpr interactions and the evolutionary design of specificity in protein families
Sergeeva, A. P., Katsamba, P. S., Cosmanescu, F., Brewer, J. J., Ahlsen, G., Mannepalli, S., Shapiro, L., & Honig, B. (2020) · Nature Communications · doi:10.1038/s41467-020-15981-8 · Cited by 38 · see citing works
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 · see citing works
Zhu, X., Chen, L., Chen, Y., Cao, Q., Liu, X., & Li, D. (2020) · Energy · doi:10.1016/j.energy.2020.117368 · Cited by 41 · see citing works
The origin and evolution of methanogenesis and <i>Archaea</i> are intertwined
Mei, R., Kaneko, M., Imachi, H., & Nobu, M. K. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad023 · Cited by 37 · see citing works
From primordial clocks to circadian oscillators
Pitsawong, W., Pádua, R. A. P., Grant, T., Hoemberger, M., Otten, R., Bradshaw, N., Grigorieff, N., & Kern, D. (2023) · Nature · doi:10.1038/s41586-023-05836-9 · Cited by 37 · see citing works
Shen, Y.-C., Lee, P.-C., Kuo, Y.-L., Wu, W.-K., Chen, C.-C., Lei, C.-H., Yeh, C.-P., Hsu, C., Hsu, C.-H., Lin, Z.-Z., et al. (2021) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s315696 · Cited by 36 · see citing works
Barnier, J.-P., Meyer, J., Kolappan, S., Bouzinba-Ségard, H., Gesbert, G., Jamet, A., Frapy, E., Schönherr-Hellec, S., Capel, E., Virion, Z., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2109364118 · Cited by 34 · see citing works
Breastfeeding and early Bifidobacterium-driven microbial colonization shape the infant gut resistome
Samarra, A., Alcañiz, A. J., Martínez-Costa, C., Marina, A., Comas, I., Segata, N., Quijada, N. M., & Collado, M. C. (2025) · Nature Communications · doi:10.1038/s41467-025-61154-w · Cited by 32 · see citing works
Computed cancer interactome explains the effects of somatic mutations in cancers
Zhang, J., Pei, J., Durham, J., Bos, T., & Cong, Q. (2022) · Protein Science · doi:10.1002/pro.4479 · Cited by 31 · see citing works
Protein multiple conformations prediction using multi-objective evolution algorithm
Hou, M.-H., Jin, S.-R., Cui, X.-Y., Peng, C.-X., Zhao, K.-L., Song, L., & Zhang, G.-J. (2023) · doi:10.1101/2023.04.21.537776 · Cited by 29 · see citing works
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 · see citing works
“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 -”
InfluentialPlastic biodegradation by sediment microbial populations under denitrifying conditions
Sun, X., Wang, S., Lin, Z., Chen, Z., Huang, W., Kong, T., Huang, D., Li, B., Zhang, H., & Sun, W. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.4c12187 · Cited by 20 · see citing works
Kim, M. C., Lee, S., Park, J. K., Park, J., Lee, D., Park, J., Kim, B.-Y., Cho, M. S., Kim, T.-Y., Park, H. Y., et al. (2021) · Journal of Medicinal Food · doi:10.1089/jmf.2020.4746 · Cited by 18 · see citing works
Genz, L. R., Nair, S., Nagar, N., & Topf, M. (2025) · Protein Science · doi:10.1002/pro.70327 · Cited by 17 · see citing works
Viruses of sulfur oxidizing phototrophs encode genes for pigment, carbon, and sulfur metabolisms
Hesketh-Best, P. J., Bosco-Santos, A., Garcia, S. L., O’Beirne, M. D., Werne, J. P., Gilhooly, W. P., & Silveira, C. B. (2023) · Communications Earth & Environment · doi:10.1038/s43247-023-00796-4 · Cited by 16 · see citing works
Rivas-Santisteban, J., Yubero, P., Robaina-Estévez, S., González, J. M., Tamames, J., & Pedrós-Alió, C. (2024) · ISME Communications · doi:10.1093/ismeco/ycae042 · Cited by 15 · see citing works
Wahdan, S. F. M., Tanunchai, B., Wu, Y., Sansupa, C., Schädler, M., Dawoud, T. M., Buscot, F., & Purahong, W. (2021) · MicrobiologyOpen · doi:10.1002/mbo3.1217 · Cited by 15 · see citing works
The vibriophage-encoded inhibitor OrbA abrogates BREX-mediated defense through the ATPase BrxC
Oshiro, R. T., Dunham, D. T., & Seed, K. D. (2024) · Journal of Bacteriology · doi:10.1128/jb.00206-24 · Cited by 15 · see citing works
Genomic Characteristion of Opportunistic Pathogen Kluyvera Reveals a Novel CTX-M Subgroup
Yu, K., Huang, Z., Lan, R., Morris, J. G., Xiao, Y., Fu, S., Gao, H., Bai, X., Li, K., & Wang, D. (2023) · Microorganisms · doi:10.3390/microorganisms11122836 · Cited by 12 · see citing works
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 · see citing works
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 · see citing works
“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).”
MethodImproving microbial phylogeny with citizen science within a mass-market video game
Sarrazin-Gendron, R., Ghasemloo Gheidari, P., Butyaev, A., Keding, T., Cai, E., Zheng, J., Mutalova, R., Mounthanyvong, J., Zhu, Y., Nazarova, E., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02175-6 · Cited by 9 · see citing works
Genetic structure in Louisiana Iris species reveals patterns of recent and historical admixture
Zalmat, A. S., Sotola, V. A., Nice, C. C., & Martin, N. H. (2021) · American Journal of Botany · doi:10.1002/ajb2.1758 · Cited by 8 · see citing works
Constructing a Draft Indian Cattle Pangenome Using Short-Read Sequencing
Azam, S., Sahu, A., Pandey, N. K., Neupane, M., Van Tassell, C. P., Rosen, B. D., Gandham, R. K., Rath, S. N., & Majumdar, S. S. (2025) · Communications Biology · doi:10.1038/s42003-025-07978-0 · Cited by 5 · see citing works
Chasapi, A., Michailidis, A., Schriml, L. M., Donati, C., Kyrpides, N. C., & Ouzounis, C. A. (2026) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00403-25
- Authors
- Fu, L., Niu, B., Zhu, Z., Wu, S., & Li, W.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Fu, Limin
- Niu, Beifang
- Zhu, Zhengwei
- Li, Weizhong
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-10-11
- Volume
- 28
- Issue
- 23
- Pages
- 3150-3152
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC3516142
- OpenAlex
- W2170747616
- Cited by
- 11,175
- References
- 11
- Citations by year
- 2026: 1378 · 2025: 1604 · 2024: 1626 · 2023: 1264 · 2022: 1242 · 2021: 1228 · 2020: 842 · 2019: 580 · 2018: 462 · 2017: 298 · 2016: 279 · 2015: 202 · 2014: 118 · 2013: 42 · 2012: 4
- FWCI
- 47.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Human Genome Research Institute (R01HG005978)
- National Center for Research Resources (R01RR025030)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- CD-HIT, sequence clustering, next-generation sequencing data, parallelization, speedup
- MeSH terms
- Algorithms, Software, Cluster Analysis, Computational Biology, Sequence Analysis, Protein
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
