UniRef: comprehensive and non-redundant UniProt reference clusters

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 10,996

Database resources of the National Center for Biotechnology Information: update

Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,695

NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

Pruitt, K. D., Tatusova, T. A., & Maglott, D. R. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl842 · Cited by 4,441

The Universal Protein Resource (UniProt)

Bairoch, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki070 · Cited by 3,774

The Universal Protein Resource (UniProt): an expanding universe of protein information

Wu, C. H. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj161 · Cited by 1,116

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

Length-dependent prediction of protein intrinsic disorder

Peng, K., Predrag Radivojac, A., Vučetić, S., Dunker, A. K., & Zoran Obradovic, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-208 · Cited by 940

Selection of representative protein data sets

Hobohm, U., Scharf, M. E., Schneider, R., & Sander, C. (1992) · Protein Science · doi:10.1002/pro.5560010313 · Cited by 816

Database resources of the National Center for Biotechnology Information

Wheeler, D. L., Barrett, T., Benson, D. A., Bryant, S. H., Canese, K., Chetvernin, V., Church, D. M., DiCuccio, M., Edgar, R., Federhen, S., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl1031 · Cited by 734

Identification of multiple distinct Snf2 subfamilies with conserved structural motifs

Flaus, A. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl295 · Cited by 727

MetalTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in Protein Metal-Binding Sites

Zhang, M., Wang, X., Xu, S., Ge, F., Paixao, I. C., Song, J., & Yu, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00739 · Cited by 8

SeaMoon: From protein language models to continuous structural heterogeneity

Lombard, V., Timsit, D., Grudinin, S., & Laine, E. (2025) · Structure · doi:10.1016/j.str.2025.06.010 · Cited by 8

Advancing biomolecular understanding and design following human instructions

Zhuang, X., Ding, K., Lyu, T., Jiang, Y., Li, X., Xiang, Z., Wang, Z., Qin, M., Feng, K., Wang, J., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01064-0 · Cited by 13

Structural and evolutionary features of red algal UV sex chromosomes

Lipinska, A. P., Cossard, G. G., Epperlein, P., Woertwein, T., Molinier, C., Godfroy, O., Carli, S., Ayres-Ostrock, L., Lavaut, E., Marchi, F., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03797-y · Cited by 8

Structure-Based Drug Design with Geometric Deep Learning: A Comprehensive Survey

Zhang, Z., Yan, J., Huang, Y., Liu, Q., Chen, E., Wang, M., & Žitnik, M. (2025) · ACM Computing Surveys · doi:10.1145/3769677 · Cited by 33

Clustering the protein universe of life using DIAMOND DeepClust

Buchfink, B., Barbe, E., Ashkenazy, H., Reuter, K., Kennedy, J. A., & Drost, H. (2026) · Nature Methods · doi:10.1038/s41592-026-03030-z · Cited by 33

Physiological Tradeoffs of Immune Response Differs by Infection Type in Pieris napi

Keehnen, N. L. P., Kučerová, L., Nylin, S., Theopold, U., & Wheat, C. W. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2020.576797 · Cited by 8

POOE: predicting oomycete effectors based on a pre-trained large protein language model

Zhao, M., Lei, C., Zhou, K., Huang, Y., Fu, C., Yang, S., & Zhang, Z. (2023) · mSystems · doi:10.1128/msystems.01004-23 · Cited by 9

Improvements in viral gene annotation using large language models and soft alignments

Harrigan, W., Ferrell, B. D., Wommack, K. E., Polson, S. W., Schreiber, Z. D., & Belcaid, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05779-6 · Cited by 9

The gut microbiota in persistent post-operative pain following breast cancer surgery

Masaud, K., Collins, J. M., Cabrera‐Rubio, R., Corrigan, M. A., Cotter, P. D., O’Brien, N., Bluett, R., Jimenez, C. K., O’Mahony, S. M., & Shorten, G. (2024) · Scientific Reports · doi:10.1038/s41598-024-62397-1 · Cited by 9

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA

Rimal, P., Paul, S. K., Panday, S. K., & Alexov, E. G. (2025) · Genes · doi:10.3390/genes16010101 · Cited by 8

Machine learning approaches for predicting protein-ligand binding sites from sequence data

Vural, O., & Jololian, L. (2025) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1520382 · Cited by 8

A Brief Overview of a Few Popular and Important Protein Databases

Bagchi, A. (2012) · Computational Molecular Bioscience · doi:10.4236/cmb.2012.24012 · Cited by 8

Deorphanization of Malonyl CoA:ACP Transacylase Drug Target in Plasmodium falciparum (PfFabD) Using Bacterial Antagonists: A ‘Piggyback’ Approach for Antimalarial Drug Discovery

Sreshty, M. A. L., Surolia, A., Sastry, G. N., & Murty, U. S. (2012) · Molecular Informatics · doi:10.1002/minf.201100051 · Cited by 8

SDS‐PAGE fractionation to increase metaproteomic insight into the taxonomic and functional composition of microbial communities for biogas plant samples

Wenzel, L., Heyer, R., Schallert, K., Löser, L., Wünschiers, R., Reichl, U., & Benndorf, D. (2018) · Engineering in Life Sciences · doi:10.1002/elsc.201800062 · Cited by 8

Transcriptome profiling of peppermint (Mentha piperita) with improved antioxidant properties in response to salicylic acid elicitation

Figueroa-Pérez, M. G., Reynoso‐Camacho, R., García-Ortega, L. F., & Guevara-González, R. G. (2018) · Journal of Plant Biochemistry and Biotechnology · doi:10.1007/s13562-018-0482-6 · Cited by 8

Allosteric Mechanism of the Hsp90 Chaperone Interactions with Cochaperones and Client Proteins by Modulating Communication Spines of Coupled Regulatory Switches: Integrative Atomistic Modeling of Hsp90 Signaling in Dynamic Interaction Networks

Astl, L., Stetz, G., & Verkhivker, G. M. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00380 · Cited by 8

Proteomics—A Powerful Tool for Understanding Saline Stress Response in Germinating Seed

Sarkar, A. K., & Sadhukhan, S. (2022) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-030-95365-2_24 · Cited by 8

A procedure to recruit members to enlarge protein family databases - the building of UECOG (UniRef-Enriched COG Database) as a model

Fernandes, G. D. R., Barbosa, D., Prosdocimi, F., Pena, I. A., Santana‐Santos, L., Coelho, O. A. M., Barbosa-Silva, A., Velloso, H., Mudado, M. A., Natale, D. A., et al. (2008) · Genetics and Molecular Research · doi:10.4238/vol7-3x-meeting008 · Cited by 8

Theoretical structural characterization of lymphoguanylin: A potential candidate for the development of drugs to treat gastrointestinal disorders

Pires, Á. S., Porto, W. F., Castro, P. O., Franco, O. L., & de Alencar, S. A. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.02.016 · Cited by 8

Improving prediction of helix–helix packing in membrane proteins using predicted contact numbers as restraints

Li, B., Mendenhall, J., Nguyen, E. D., Weiner, B. E., Fischer, A. W., & Meiler, J. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25281 · Cited by 8

SeqScreen: a biocuration platform for robust taxonomic and biological process characterization of nucleic acid sequences of interest

Albin, D., Nasko, D., Elworth, R. A. L., Lu, J., Balaji, A., Christian, D., Shah, N., Selengut, J., Hulme-Lowe, C., Muthu, P., et al. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8982987 · Cited by 7

Expressed sequence tags (ESTs) based identification of genes and expression analysis of leukocyte cell-derived chemotaxin-2 (LECT2) from Epinephelus bruneus

Harikrishnan, R., Kim, J.-S., Kim, M., Balasundaram, C., & Heo, M. (2011) · Gene · doi:10.1016/j.gene.2011.08.029 · Cited by 8

CloVR-Microbe: Assembly, gene finding and functional annotation of raw sequence data from single microbial genome projects – standard operating procedure, version 1.0Preprint

White, J., White, O. R., Angiuoli, S. V., Fricke, W. F., Galens, K., White, J., Arze, C., Matalka, M. N., Giglio, M. G., & The CloVR Team (2011) · Nature Precedings · doi:10.1038/npre.2011.5887.2 · Cited by 8

Microbial diversity and metabolic potential in cyanotoxin producing cyanobacterial mats throughout a river networkPreprint

Bouma‐Gregson, K., Olm, M. R., Probst, A. J., Anantharaman, K., Power, M. E., & Banfield, J. F. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/294421 · Cited by 8

Akkermansia species : phylogeny, physiology and comparative genomics

Ouwerkerk, J. P. (2016) · doi:10.18174/371369 · Cited by 8

The grape MYB24 mediates the coordination of light-induced terpene and flavonol accumulation in response to berry anthocyanin sunscreen depletionPreprint

Chen, Z., Dai, Z., Ferrier, T., Orduña, L., Antonio, S., Arnau, P., Darren, W. L., Kappel, C., Savoi, S., Loyola, R., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.16.472692 · Cited by 8

Rapid protein-protein interaction network creation from multiple sequence alignments with Deep LearningPreprint

Bryant, P., & Noé, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.15.536993 · Cited by 7

Recovery of 679 metagenome-assembled genomes from different soil depths along a precipitation gradient

Kazarina, A., Wiechman, H., Sarkar, S., Richie, T. G., & Lee, S. T. M. (2025) · Scientific Data · doi:10.1038/s41597-025-04884-2 · Cited by 8

A deep reinforcement learning platform for antibiotic discoveryPreprint

Cao, H., Torres, M. D. T., Zhang, J., Gao, Z., Wu, F., Gu, C., Leskovec, J., Choi, Y., de la Fuente‐Núñez, C., Chen, G., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.23.678086 · Cited by 10

ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks

Li, G., Zhang, N., & Fan, L. (2025) · ACS Omega · doi:10.1021/acsomega.4c09688 · Cited by 7

Comparative metagenome-associated analysis of gut microbiota and antibiotic resistance genes in acute gastrointestinal injury patients with the risk of in-hospital mortality

Bai, Y., Hu, Y., Chen, X., Hu, L., Wu, K., Liang, S., Zheng, J., Gänzle, M. G., & Chen, C. (2025) · mSystems · doi:10.1128/msystems.01444-24 · Cited by 8

Development of antibiotic resistome in premature infants

Keshet, A., Hochwald, O., Lavon, A., Borenstein‐Levin, L., Shoer, S., Godneva, A., Glantz-Gashai, Y., Cohen-Dolev, N., Timstut, F., Lotan‐Pompan, M., et al. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115515 · Cited by 8

Identification and characterization of substrate- and product-selective nylon hydrolases

Drufva, E. E., Cahill, J. F., Saint-Vincent, P. M. B., Williams, A. N., Bocharova, V., Capra, N., Meilleur, F., Carper, D. L., Bourgery, C., Miyazaki, K., et al. (2025) · Chem Catalysis · doi:10.1016/j.checat.2025.101418 · Cited by 7

AI-first structural identification of pathogenic protein target interfaces

Saluri, M., Landreh, M., & Bryant, P. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013168 · Cited by 8

Review of protein structure-based analyses that illuminate plant stress mechanisms

Shahid, F., Sen, N., Rasni, H. N., Isa, N. M., Ling, N., Orengo, C. A., & Lam, S. D. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.021 · Cited by 9

Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer

Voskanian, A., Katsonis, P., Lichtarge, O., Pejaver, V. R., Predrag Radivojac, A., Mooney, S. D., Capriotti, E., Bromberg, Y., Wang, Y., Miller, M., et al. (2019) · Human Mutation · doi:10.1002/humu.23849 · Cited by 8

Analysis of the Evolution of the MoxR ATPases

Bhandari, V., Ommen, D. A. J. V., Wong, K. S., & Houry, W. A. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c02554 · Cited by 7

Genomes of Endotrypanum monterogeii from Panama and Zelonia costaricensis from Brazil: Expansion of Multigene Families in Leishmaniinae Parasites That Are Close Relatives of Leishmania spp.

Tullume-Vergara, P. O., Caicedo, K. Y. O., Tantalean, J. F. C., Serrano, M. G., Buck, G. A., Teixeira, M. M. G., Shaw, J. J., & Alves, J. M. P. (2023) · Pathogens · doi:10.3390/pathogens12121409 · Cited by 7

Bacterial cysteate dissimilatory pathway involves a racemase and d-cysteate sulfo-lyase

Liu, C., Ma, K., Jiang, L., Liu, X., Tong, Y., Yang, S., Jin, X., Wei, Y., & Zhang, Y. (2024) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2024.107371 · Cited by 7

Effect of Palmitoylethanolamide Compared to a Placebo on the Gut Microbiome and Biochemistry in an Overweight Adult Population: A Randomised, Placebo Controlled, Double-Blind Study

Batacan, R. B., Briskey, D. R., Bajagai, Y. S., Smith, C., Stanley, D., & Rao, A. (2024) · Biomedicines · doi:10.3390/biomedicines12071620 · Cited by 7

Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction

Wang, B., & Li, W. (2024) · Genes · doi:10.3390/genes15081090 · Cited by 7

PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides

Kurata, H., Harun-Or-Roshid, M., Tsukiyama, S., & Maeda, K. (2024) · PLoS ONE · doi:10.1371/journal.pone.0309078 · Cited by 7

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation

Dudek, C.-A., Dannheim, H., & Schomburg, D. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182216 · Cited by 7

Effects of BAC-filtration, disinfection, and temperature on water quality in simulated reclaimed water distribution systems

Zhu, N. J., Ghosh, S., Strom, L. E., Pruden, A. J., & Edwards, M. A. (2020) · Environmental Science Water Research & Technology · doi:10.1039/d0ew00581a · Cited by 7

FALCON2: a web server for high-quality prediction of protein tertiary structures

Kong, L., Ju, F., Zhang, H., Sun, S., & Bu, D. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04353-8 · Cited by 8

Neuronal Activation of the Gastrointestinal Tract Shapes the Gut Environment in Mice

Yoo, B. B., Griffiths, J. A., Thuy-Boun, P. S., Cantu, V., Weldon, K. C., Challis, C., Sweredoski, M. J., Chan, K. Y., Thron, T., Sharon, G., et al. (2021) · CaltechAUTHORS (California Institute of Technology) · Cited by 8

Biological databases and their application

Sharma, P. K., & Yadav, I. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89775-4.00021-3 · Cited by 7

Textile industry wastewater microbiome: Recovery of metagenome assembled genomes (MAGs) using shotgun sequencing approach from Jetpur, Gujarat, India

Kumar, D., Patel, Z. Z., Pandit, P. R., Pandit, R. J., Puvar, A., Patel, A. K., Joshi, M. N., & Joshi, C. G. (2022) · Ecological Genetics and Genomics · doi:10.1016/j.egg.2022.100155 · Cited by 7

iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction

Suviriyapaisal, N., & Wichadakul, D. (2023) · RSC Advances · doi:10.1039/d3ra03796g · Cited by 8

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Wang, X., Li, A., Li, X., & Cui, H. (2024) · Chemistry - A European Journal · doi:10.1002/chem.202303889 · Cited by 7

Unveiling the evolution of policies for enhancing protein structure predictions: A comprehensive analysis

Rahimzadeh, F., Khanli, L. M., Salehpoor, P., Golabi, F., & Pourbahrami, S. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108815 · Cited by 7

Efficient and Interpretable Prediction of Protein Functional Classes by Correspondence Analysis and Compact Set Relations

Chang, J., Taly, J.-F., Erb, I., Sung, T.-Y., Hsu, W.-L., Tang, C. Y., Notredame, C., & Su, E. C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0075542 · Cited by 7

HD 5 and HBD 1 variants’ solvation potential energy correlates with their antibacterial activity against Escherichia coli

Porto, W. F., Nolasco, D. O., Pires, Á. S., Fernandes, G. D. R., Franco, O. L., & de Alencar, S. A. (2015) · Biopolymers · doi:10.1002/bip.22763 · Cited by 7

Size distribution of function-based human gene sets and the split–merge model

Li, W., Fontanelli, Ó., & Miramóntes, P. (2016) · Royal Society Open Science · doi:10.1098/rsos.160275 · Cited by 7

Associating Gene Ontology Terms with Pfam Protein Domains

Alborzi, S. Z., Devignes, M., & Ritchie, D. W. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-56154-7_13 · Cited by 7

Draft de novo transcriptome assembly and proteome characterization of the electric lobe of Tetronarce californica: a molecular tool for the study of cholinergic neurotransmission in the electric organ

Stavrianakou, M., Pérez, R., Wu, C., Sachs, M. S., Aramayo, R., & Harlow, M. L. (2017) · BMC Genomics · doi:10.1186/s12864-017-3890-4 · Cited by 7

Deciphering the structural basis for glucocorticoid resistance caused by missense mutations in the ligand binding domain of glucocorticoid receptor

Monteiro, L., Franco, O. L., de Alencar, S. A., & Porto, W. F. (2019) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2019.07.020 · Cited by 7

Benchmarking analysis of deleterious SNP prediction tools on CYP2D6 enzyme

Ferreira, K. C. D. V., Fialho, L. F., Franco, O. L., de Alencar, S. A., & Porto, W. F. (2020) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13676 · Cited by 7

Towards a Shared, Conceptual Model-Based Understanding of Proteins and Their Interactions

Palacio, A. L., & Pastor, Ó. (2021) · IEEE Access · doi:10.1109/access.2021.3080040 · Cited by 7

Structural and Functional Investigation of the Ag+/Cu+ Binding Domains of the Periplasmic Adaptor Protein SilB from Cupriavidus metallidurans CH34

Urbina, P., Bersch, B., Angelis, F. D., Derfoufi, K.-M., Prévost, M., Goormaghtigh, E., & Vandenbussche, G. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00022 · Cited by 7

Channel-interacting PDZ protein, ‘CIPP’, interacts with proteins involved in cytoskeletal dynamics

Alpi, E., Landi, E., Barilari, M., Serresi, M., Salvadori, P. A., Bachi, A., & Dente, L. (2009) · Biochemical Journal · doi:10.1042/bj20081387 · Cited by 7

The microbiome of the eastern oyster, Crassostrea virginica, in health and disease

Sakowski, E. G. (2024) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/d7k4-kf14 · Cited by 6

Evaluation of Methods for Protein Representation Learning: A Quantitative AnalysisPreprint

Ünsal, S., Ataş, H., Albayrak, M., Turhan, K., Acar, A. C., & Doğan, T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.28.359828 · Cited by 7

Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope InteractionsPreprint

Zhang, P., Bang, S., Cai, M., & Lee, H. (2023) · doi:10.7554/elife.88837.1 · Cited by 8

An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodiesPreprint

Wang, Y., Lv, H., Lei, R., Yeung, Y.-H., Shen, I. R., Choi, D., Teo, Q. W., Tan, T. J. C., Gopal, A. B., Chen, X., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.11.557288 · Cited by 7

CD-GPT As a Biological Foundation Model Bridging the Gap between Molecular Sequences Through Central DogmaPreprint

Zhu, X., Qin, C., Wang, F., Yang, F., He, B., Zhao, Y., & Yao, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.24.600337 · Cited by 8

Genome-scale annotation of protein binding sites via language model and geometric deep learning

Yuan, Q., Tian, C., & Yang, Y. (2024) · eLife · doi:10.7554/elife.93695.3 · Cited by 9

Planet in Peril: Unveiling the Microbial Consequences of Environmental Degradation on Human Health Through Omics Approaches

Shukla, N., Bhatia, A., Kesheri, M., Kanchan, S., & Narayan, J. (2024) · Springer eBooks · doi:10.1007/978-981-97-1769-9_2 · Cited by 6

Altitude-dependent agro-ecologies impact the microbiome diversity of scavenging indigenous chicken in Ethiopia

Glendinning, L., Jia, X., Kebede, A., Oyola, S. O., Park, J., Park, W., Assiri, A., Holm, J. B., Kristiansen, K., Jianlin, H., et al. (2024) · Microbiome · doi:10.1186/s40168-024-01847-4 · Cited by 6

DeepCheck: multitask learning aids in assessing microbial genome quality

Guo, W., Wu, N., Zhao, K., Yang, S., Wang, L., & Liu, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae539 · Cited by 6

Protein–protein and protein–nucleic acid binding site prediction via interpretable hierarchical geometric deep learning

Zhang, S., Han, J., & Liu, J. (2024) · GigaScience · doi:10.1093/gigascience/giae080 · Cited by 5

Multi-omics analysis reveals interactions between host and microbes in Bama miniature pigs during weaning

Ma, W., Li, Y., Hu, Y., Liu, X., Guo, Z., Zhong, B., Qiu, H., & Li, J. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1482925 · Cited by 6

Diverse microbiome functions, limited temporal variation and substantial genomic conservation within sedimentary and granite rock deep underground research laboratories

Amano, Y., Sachdeva, R., Gittins, D. B., Anantharaman, K., Lei, S.-F., Valentin-Alvarado, L., Diamond, S., Beppu, H., Iwatsuki, T., Mochizuki, A., et al. (2024) · Environmental Microbiome · doi:10.1186/s40793-024-00649-3 · Cited by 9

Interpretation of machine learning-based prediction models and functional metagenomic approach to identify critical genes in HBCD degradation

Lin, Y., Hsieh, P.-H., Mao, C.-C., Shih, Y., Chen, S., & Lin, C. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.136976 · Cited by 6

Enhanced prediction of antigen and antibody binding interface using ESM-2 and Bi-LSTM

Li, Q., Zhao, Y., Chordia, M. D., Xia, X., Zhang, B., & Zheng, H. (2025) · Human Immunology · doi:10.1016/j.humimm.2025.111304 · Cited by 7

Dietary drivers of gut microbiota diversity and function in wildlife of Wolong Nature Reserve: a metagenomic study

Huang, F., Shi, X., Chen, P., Hu, Q., Zhao, Y., Chen, Z., Ma, W., Tan, Q., Feng, X., & Zhang, X. (2025) · Current Zoology · doi:10.1093/cz/zoaf018 · Cited by 6

Phage Host Prediction Using Deep Neural Network With Multi-Source Protein Language Models and Squeeze-and-Excitation Attention Mechanism

Gao, P., Xu, L., Bai, Y., Lin, Q., Ji, J., & Ma, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3582652 · Cited by 6

The comprehensive oral microbiome landscape unveils its interplay with poor oral health in esophageal squamous cell carcinoma risk

Gao, P., Yuan, H., Mei, Z., Yin, X., Zeng, Y., Liu, Z., Yang, X., Xue, J., Liu, Z., Jiang, Y., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102431 · Cited by 6

Learning the language of phylogeny with MSA Transformer

Chen, R., Foley, G., & Mikael Bodén, A. (2025) · Cell Systems · doi:10.1016/j.cels.2025.101445 · Cited by 7

Machine learning-guided discovery of thermophilic carbonic anhydrases from environmental metagenomes

Pairoh, S., Mhuantong, W., Boonyapakron, K., Yuvaniyama, J., Kanokratana, P., Bunterngsook, B., Lekakarn, H., Arunrattanamook, N., Laothanachareon, T., & Champreda, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-24713-1 · Cited by 6

Metagenomic Profiling of Pathogens in Simulated Reclaimed Water Distribution Systems Operated at Elevated Temperature Reveals Distinct Response of Mycobacteria to Filtration and Disinfection Conditions

Ghosh, S., Strom, L. E., Zhu, N. J., Donaldson, K., Kurowski, A., Majeed, H., Falkinham, J. O., Edwards, M. A., & Pruden, A. J. (2023) · ACS ES&T Water · doi:10.1021/acsestwater.2c00504 · Cited by 6

AntiRef: reference clusters of human antibody sequences

Briney, B. S. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad109 · Cited by 6

Divergence within the Taxon ‘Candidatus Phytoplasma asteris’ Confirmed by Comparative Genome Analysis of Carrot Strains

Toth, R., Ilic, A.-M., Hüettel, B., Duduk, B., & Kube, M. (2024) · Microorganisms · doi:10.3390/microorganisms12051016 · Cited by 6

Protein Sequence–Structure–Function–Network Links Discovered with the ANNOTATOR Software Suite: Application to ELYS/Mel-28

Schneider, G., Sherman, W. A., Kuchibhatla, D., Ooi, H., Sirota, F. L., Maurer‐Stroh, S., Eisenhaber, B., & Eisenhaber, F. (2012) · Springer eBooks · doi:10.1007/978-3-7091-0947-2_7 · Cited by 6

The architecture of T rypanosoma brucei tubulin‐binding cofactor B and implications for function

Fleming, J. R., Morgan, R. E., Fyfe, P. K., Kelly, S. M., & Hunter, W. N. (2013) · FEBS Journal · doi:10.1111/febs.12308 · Cited by 6

In Silico Prediction of the Effects of Nonsynonymous Single Nucleotide Polymorphisms in the Human Catechol-O-Methyltransferase (COMT) Gene

Yılmaz, A., & Çetin, İ. (2020) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-020-00905-6 · Cited by 6

Identification and Validation of Ikaros (IKZF1) as a Cancer Driver Gene for Marek’s Disease Virus-Induced Lymphomas

Steep, A., Hildebrandt, E., Xu, H., Hearn, C., Frishman, D., Niikura, M., Dunn, J., Kim, T., Conrad, S., Muir, W., et al. (2022) · Microorganisms · doi:10.3390/microorganisms10020401 · Cited by 6

Short-Term Dietary Intervention with Whole Oats Protects from Antibiotic-Induced Dysbiosis

Costa, S. K., Antosca, K. M., Beekman, C. N., Peterson, R. L., Penumutchu, S., & Belenky, P. A. (2023) · Microbiology Spectrum · doi:10.1128/spectrum.02376-23 · Cited by 6

Deep Isolated Aquifer Brines Harbor Atypical Halophilic Microbial Communities in Quebec, Canada

Gagnon, J., Beauregard-Tousignant, S., Marcil, J.-S., & Lazar, C. S. (2023) · Genes · doi:10.3390/genes14081529 · Cited by 6

Metagenomic analysis of oral and intestinal microbiome of patients during the initial stage of orthodontic treatment

Liu, J., Jiang, J., Lan, Y., Li, C., Han, R., Wang, J., Wang, T., Zhao, Z., Fan, Z., He, L., et al. (2023) · American Journal of Orthodontics and Dentofacial Orthopedics · doi:10.1016/j.ajodo.2023.07.019 · Cited by 7

BeEP Server: using evolutionary information for quality assessment of protein structure models

Palópoli, N., Lanzarotti, E., & Parisi, G. D. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt453 · Cited by 6

Complete genome sequence of the thermophilic Thermus sp. CCB_US3_UF1 from a hot spring in Malaysia

Teh, B. S., Lau, N., Ng, F. L., Rahman, A. Y. A., Wan, X., Saito, J. A., Hou, S., Teh, A.-H., Najimudin, N., & Alam, M. S. (2015) · Standards in Genomic Sciences · doi:10.1186/s40793-015-0053-6 · Cited by 6

Visualizing the Protein Sequence Universe

Stanberry, L. I., Higdon, R., Haynes, W. A., Kolker, N., Broomall, W., Ekanayake, S., Hughes, A., Ruan, Y., Qiu, J., Kolker, E., et al. (2013) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3072 · Cited by 6

Conserved water molecules in bacterial serine hydroxymethyltransferases

Milano, T., di Salvo, M. L., Angelaccio, S., & Pascarella, S. (2015) · Protein Engineering Design and Selection · doi:10.1093/protein/gzv026 · Cited by 6

KIXBASE: A comprehensive web resource for identification and exploration of KIX domains

Yadav, A., Thakur, J. K., & Yadav, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-14617-0 · Cited by 6

Bioinformatics Database Resources

Khandelwal, I., Sharma, A., Agrawal, P., & Shrivastava, R. (2017) · Advances in library and information science (ALIS) book series · doi:10.4018/978-1-5225-1871-6.ch004 · Cited by 6

A Hierarchy of Interactions between Pathogenic Virus and Vertebrate Host

Friedman, R. (2022) · Symmetry · doi:10.3390/sym14112274 · Cited by 7

Disorder Atlas: Web-based software for the proteome-based interpretation of intrinsic disorder predictions

Vincent, M. P., & Schnell, S. D. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.107090 · Cited by 6

Gene and Repetitive Sequence Annotation in the Triticeae

Wicker, T., & Buell, C. R. (2009) · Springer eBooks · doi:10.1007/978-0-387-77489-3_15 · Cited by 6

BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm098 Databases and ontologies UniRef: comprehensive and non-redundant UniProt reference

Suzek, B. E., Huang, H., Mcgarvey, P., Mazumder, R., & Wu, C. H. (2008)

UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches

Suzek, B. E., Wang, Y., Huang, H., McGarvey, P. B., Wu, C. H., & the UniProt Consortium (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu739 · Cited by 1,928

The Universal Protein Resource (UniProt): an expanding universe of protein information

Wu, C. H. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj161 · Cited by 1,116

doi:10.1093/nar/gkj161 The Universal Protein Resource (UniProt): an expanding universe of protein information

Wu, C. H., Apweiler, R., Bairoch, A., Natale, D. A., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., López, R., et al. (2005)

Removing near-neighbour redundancy from large protein sequence collections.

Holm, L., & Sander, C. (1998) · Bioinformatics · doi:10.1093/bioinformatics/14.5.423 · Cited by 293