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

PhosphoSitePlus, 2014: mutations, PTMs and recalibrations

Hornbeck, P. V., Zhang, B., Murray, B., Kornhauser, J. M., Latham, V., & Skrzypek, E. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1267 · Cited by 3,411

The Pfam protein families database

Finn, R. D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J. E., Gavin, O. L., Gunasekaran, P., Ceric, G., Forslund, K., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp985 · Cited by 2,694

A Mitochondrial Protein Compendium Elucidates Complex I Disease Biology

Pagliarini, D. J., Calvo, S. E., Chang, B., Sheth, S. A., Vafai, S. B., Ong, S.-E., Walford, G. A., Sugiana, C., Boneh, A., Chen, W. K., et al. (2008) · Cell · doi:10.1016/j.cell.2008.06.016 · Cited by 1,994

ScanProsite: detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins

de Castro, E., Sigrist, C. J. A., Gattiker, A., Bulliard, V., Langendijk-Genevaux, P. S., Gasteiger, E., Bairoch, A., & Hulo, N. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl124 · Cited by 1,686

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

MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins

Calvo, S. E., Clauser, K. R., & Mootha, V. K. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1003 · Cited by 1,251

Pathway Commons, a web resource for biological pathway data

Cerami, E. G., Gross, B. E., Demir, E., Rodchenkov, I., Babur, O., Anwar, N., Schultz, N., Bader, G. D., & Sander, C. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1039 · Cited by 1,101

A large-scale evaluation of computational protein function prediction

Radivojac, P., Clark, W. T., Oron, T. R., Schnoes, A. M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2340 · Cited by 1,028

Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information

Ovchinnikov, S., Kamisetty, H., & Baker, D. (2014) · eLife · doi:10.7554/elife.02030 · Cited by 629

Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study

Zhao, Q.-Y., Wang, Y., Kong, Y.-M., Luo, D., Li, X., & Hao, P. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s14-s2 · Cited by 479

The Edinburgh human metabolic network reconstruction and its functional analysis

Ma, H., Sorokin, A., Mazein, A., Selkov, A., Selkov, E., Demin, O., & Goryanin, I. (2007) · Molecular Systems Biology · doi:10.1038/msb4100177 · Cited by 435

TreeFam: 2008 update

Ruan, J., Li, H., Chen, Z., Coghlan, A., Coin, L. J. M., Guo, Y., Heriche, J.-K., Hu, Y., Kristiansen, K., Li, R., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm1005 · Cited by 332

Mutations in CHD7, Encoding a Chromatin-Remodeling Protein, Cause Idiopathic Hypogonadotropic Hypogonadism and Kallmann Syndrome

Kim, H.-G., Kurth, I., Lan, F., Meliciani, I., Wenzel, W., Eom, S. H., Kang, G. B., Rosenberger, G., Tekin, M., Ozata, M., et al. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.005 · Cited by 338

A threading-based method (FINDSITE) for ligand-binding site prediction and functional annotation

Brylinski, M., & Skolnick, J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0707684105 · Cited by 333

In silico method for modelling metabolism and gene product expression at genome scale

Lerman, J. A., Hyduke, D. R., Latif, H., Portnoy, V. A., Lewis, N. E., Orth, J. D., Schrimpe-Rutledge, A. C., Smith, R. D., Adkins, J. N., Zengler, K., et al. (2012) · Nature Communications · doi:10.1038/ncomms1928 · Cited by 266

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning

Gao, M., Nakajima An, D., Parks, J. M., & Skolnick, J. (2022) · Nature Communications · doi:10.1038/s41467-022-29394-2 · Cited by 252

Association of the Intestinal Microbiome with the Development of Neovascular Age-Related Macular Degeneration

Zinkernagel, M. S., Zysset-Burri, D. C., Keller, I., Berger, L. E., Leichtle, A. B., Largiadèr, C. R., Fiedler, G. M., & Wolf, S. (2017) · Scientific Reports · doi:10.1038/srep40826 · Cited by 229

Data Portal for the Library of Integrated Network-based cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data

Koleti, A., Terryn, R., Stathias, V., Chung, C., Cooper, D. J., Turner, J. P., Vidović, D., Forlin, M., Kelley, T. T., D’Urso, A., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1063 · Cited by 193

Proteomic and Bioinformatic Characterization of the Biogenesis and Function of Melanosomes

Chi, A., Valencia, J. C., Hu, Z.-Z., Watabe, H., Yamaguchi, H., Mangini, N. J., Huang, H., Canfield, V. A., Cheng, K. C., Yang, F., et al. (2006) · Journal of Proteome Research · doi:10.1021/pr060363j · Cited by 199

PepBank - a database of peptides based on sequence text mining and public peptide data sources.

Shtatland, T., Guettler, D., Kossodo, M., Pivovarov, M., & Weissleder, R. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-280 · Cited by 181

Insights into disease-associated mutations in the human proteome through protein structural analysis

Gao, M., Zhou, H., & Skolnick, J. (2015) · Structure · doi:10.1016/j.str.2015.03.028 · Cited by 113

Critical assessment of genome-scale metabolic networks: the need for a unified standard

Ravikrishnan, A., & Raman, K. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv003 · Cited by 87

A map of human protein interactions derived from co‐expression of human mRNAs and their orthologs

Ramani, A. K., Li, Z., Hart, G. T., Carlson, M. W., Boutz, D. R., & Marcotte, E. M. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.19 · Cited by 78

Initial characterization of the human central proteome

Burkard, T. R., Planyavsky, M., Kaupe, I., Breitwieser, F. P., Bürckstümmer, T., Bennett, K. L., Superti-Furga, G., & Colinge, J. (2011) · BMC Systems Biology · doi:10.1186/1752-0509-5-17 · Cited by 67

TM6SF2 and MAC30, new enzyme homologs in sterol metabolism and common metabolic disease

Sanchez-Pulido, L., & Ponting, C. P. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00439 · Cited by 63

DESTINI: A deep-learning approach to contact-driven protein structure prediction

Gao, M., Zhou, H., & Skolnick, J. (2019) · Scientific Reports · doi:10.1038/s41598-019-40314-1 · Cited by 61

Human metabolic network reconstruction and its impact on drug discovery and development

Ma, H., & Goryanin, I. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.02.002 · Cited by 59

Annotation inconsistencies beyond sequence similarity-based function prediction – phylogeny and genome structure

Promponas, V. J., Iliopoulos, I., & Ouzounis, C. A. (2015) · Standards in Genomic Sciences · doi:10.1186/s40793-015-0101-2 · Cited by 43

Subspecies-level genome comparison of Lactobacillus delbrueckii

Baek, M.-G., Kim, K. W., & Yi, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-29404-3 · Cited by 35

A novel sequence alignment algorithm based on deep learning of the protein folding code

Gao, M., & Skolnick, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa810 · Cited by 18

Refining the domain architecture model of the replication origin firing factor Treslin/TICRR

Ferreira, P., Sanchez-Pulido, L., Marko, A., Ponting, C. P., & Boos, D. (2022) · Life Science Alliance · doi:10.26508/lsa.202101088 · Cited by 9