The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
A draft map of the human proteome
Open accessAbstract
The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.
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.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“this situation has changed with the development of more sensitive and powerful high-resolution mass spectrometry (MS) technologies that allow in-depth coverage of nearly complete proteomes of mammalian cells 13–17.”
From Cell type- and brain region-resolved mouse brain proteome (2015) · cites this paper as [17]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Because only 9 of the 953 genes lack confirmed expression in any of the 34 neuro-related tissues tracked by IDG KMC (for example, GTEx40,41, HPA42 and HPM43), these data suggest that the remaining 80% of this set have the potential to be associated with human neurobehavioural phenotypes, paving the way for new research avenues in this direction (see Supplementary Table S8).”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [43]
The cited source says what the claim attributes to it.
In the citing paper
“Recent efforts to describe the composition and spatial distribution of proteins have advanced in draft maps of the human proteome 40,41 and the Human Protein Atlas 2 , respectively.”
From How many human proteoforms are there? (2018) · cites this paper as [40]
In the citing paper
“Since protein expression levels span around 4 to 5 orders of magnitude (13, 16, 37, 38)”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [37]
In the citing paper
“Recently, the protein products of 17,294 genes were identified and mapped in the draft of human proteome using 30 human samples of tissues and cells (Kim et al., 2014).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Kim et al., 2014
In the citing paper
“Although, highly abundant “housekeeping” proteins from 2,350 genes constitute approximately 75% of total protein mass, low-abundance proteins constitute the majority of the protein species in human (Kim et al., 2014).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Kim et al., 2014
In the citing paper
“This draft proteome demonstrated the feasibility of building a complete human proteome encompassing over 200 cell types and all body fluids.”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Kim et al., 2014
In the citing paper
“Despite this limitation, it is now possible to reliably obtain quantitative observations for tens of thousands of peptides derived from up to 10,000 proteins per sample22, and very recently important steps have been taken toward profiling the entire human proteome23,24.”
From Decoding neuroproteomics: integrating the genome, translatome and functional anatomy (2014) · cites this paper as [24]
In the citing paper
“As a result, proteins can be resolved and quantified relatively and absolutely, culminating in a near-comprehensive mass spectrometry-based expression map of the human proteome (Kim et al., 2014, Wilhelm et al., 2014).”
From A Multi-layered Quantitative In Vivo Expression Atlas of the Podocyte Unravels Kidney Disease Candidate Genes (2018) · cites this paper as Kim et al., 2014
In the citing paper
“In addition, cell line expression data was added from HPA and the Human Proteome Map (15) and expression data was collected for orthologous genes from 17 different species.”
From TCRD and pharos 2021: mining the human proteome for disease biology (2021) · cites this paper as [15]
In the citing paper
“The E3 ligase expression pattern in many E3 ligases can be validated by the immunohistochemistry (IHC) data from the Human Protein Atlas (THPA) (https://www.proteinatlas.org/) or the human tissue proteomic data [95].”
From PROteolysis TArgeting Chimeras (PROTACs) as emerging anticancer therapeutics (2020) · cites this paper as [95]
In the citing paper
“Prior initiatives such as those describing first drafts of the human proteome in 2014 ( 33 , 34 ) and ongoing work under the aegis of the Human Protein Atlas and the Human Proteome Project ( 35 ) have accomplished a great deal over the past several years,”
From The Human Proteoform Project: Defining the human proteome (2021) · cites this paper as [33]
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
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
Mass spectrometry-based proteomics
Aebersold, R., & Mann, M. (2003) · Nature · doi:10.1038/nature01511 · Cited by 6,955
Eng, J. K., McCormack, A. L., & Yates, J. R. (1994) · Journal of the American Society for Mass Spectrometry · doi:10.1016/1044-0305(94)80016-2 · Cited by 6,484
In-gel digestion for mass spectrometric characterization of proteins and proteomes
Shevchenko, A., Tomas, H., Havli, J., Olsen, J. V., & Mann, M. (2006) · Nature Protocols · doi:10.1038/nprot.2006.468 · Cited by 4,947
TANDEM: matching proteins with tandem mass spectra
Craig, R., & Beavis, R. C. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth092 · Cited by 2,546
Towards a knowledge-based Human Protein Atlas
Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., Zwahlen, M., Kampf, C., Wester, K., Hober, S., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1210-1248 · Cited by 2,440
Semi-supervised learning for peptide identification from shotgun proteomics datasets
Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2007) · Nature Methods · doi:10.1038/nmeth1113 · Cited by 2,424
The Proteomics Identifications (PRIDE) database and associated tools: status in 2013
Vizcaíno, J. A., Côté, R. G., Csordas, A., Dianes, J. A., Fabregat, A., Foster, J. M., Griss, J., Alpi, E., Birim, M., Contell, J., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1262 · Cited by 1,918
A tissue-specific atlas of mouse protein phosphorylation and expression
Huttlin, E. L., Jedrychowski, M. P., Elias, J. E., Goswami, T., Rad, R., Beausoleil, S. A., Villén, J., Haas, W., Sowa, M. E., & Gygi, S. P. (2010) · Cell · doi:10.1016/j.cell.2010.12.001 · Cited by 1,778
Flagged first, then by citations
Cited by
Sorted by citations
Items 801–900 of 1,002
Engineering the Nanoparticle-Protein Interface for Cancer Therapeutics
Saie, A. A., Ray, M., Mahmoudi, M., & Rotello, V. M. (2015) · Cancer treatment and research · doi:10.1007/978-3-319-16555-4_11 · Cited by 29
Profiling of post-translational modifications by chemical and computational proteomics
Yang, F., & Wang, C. (2020) · Chemical Communications · doi:10.1039/d0cc05447j · Cited by 31
Higher-order phosphatase–substrate contacts terminate the integrated stress response
Yan, Y., Harding, H. P., & Ron, D. (2021) · Nature Structural & Molecular Biology · doi:10.1038/s41594-021-00666-7 · Cited by 28
Towards integrative structural mass spectrometry: Benefits from hybrid approaches
Marcoux, J., & Cianférani, S. (2015) · Methods · doi:10.1016/j.ymeth.2015.05.024 · Cited by 29
Marchi, T. D., Timmermans, A. M., Smid, M., Look, M. P., Stingl, C., Opdam, M., Linn, S. C., Sweep, F. C. G. J., Span, P. N., Kliffen, M., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.6521 · Cited by 29
Vedove, F. D., Fava, C., Jiang, H., Zanconato, G., Quilley, J., Brunelli, M., Guglielmi, V., Vattemi, G. N. A., & Minuz, P. (2016) · Journal of Hypertension · doi:10.1097/hjh.0000000000000942 · Cited by 29
Moreno‐Morcillo, M., Francisco‐Velilla, R., Buh, A. E., Fernández-Chamorro, J., Ramón‐Maiques, S., & Martı́nez-Salas, E. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz1126 · Cited by 29
Sex-specific chromatin landscapes in an ultra-compact chordate genome
Navrátilová, P., Danks, G., Long, A., Butcher, S., Manak, J. R., & Thompson, E. M. (2017) · Epigenetics & Chromatin · doi:10.1186/s13072-016-0110-4 · Cited by 29
Proteomic repository data submission, dissemination, and reuse: key messages
Pérez‐Riverol, Y. (2022) · Expert Review of Proteomics · doi:10.1080/14789450.2022.2160324 · Cited by 25
Systematic Analysis of Drug Targets Confirms Expression in Disease-Relevant Tissues
Kumar, V. D., Sanséau, P., Simola, D. F., Hurle, M. R., & Agarwal, P. (2016) · Scientific Reports · doi:10.1038/srep36205 · Cited by 26
New‐generation mass spectrometry expands the toolbox of cell and developmental biology
Lombard‐Banek, C., Portero, E. P., Onjiko, R. M., & Nemes, P. (2017) · genesis · doi:10.1002/dvg.23012 · Cited by 28
High rate of adaptation of mammalian proteins that interact with Plasmodium and related parasites
Ebel, E. R., Telis, N., Venkataram, S., Petrov, D. A., & Enard, D. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007023 · Cited by 27
The Unforeseen Non-Coding RNAs in Head and Neck Cancer
Irimie, A. I., Zimța, A., Ciocan, C. A., Mehterov, N. H., Dudea, D., Braicu, C., & Berindan‐Neagoe, I. (2018) · Genes · doi:10.3390/genes9030134 · Cited by 28
Engineering ClpS for selective and enhanced N-terminal amino acid binding
Tullman, J. A., Callahan, N., Ellington, B., Kelman, Z., & Marino, J. P. (2019) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-019-09624-2 · Cited by 28
Genetic Interactions between the Members of the SMN-Gemins Complex in Drosophila
Borg, R. M., Bordonné, R., Vassallo, N., & Cauchi, R. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0130974 · Cited by 26
Functional Annotation of Proteome Encoded by Human Chromosome 22
Pinto, S. M., Manda, S. S., Kim, M., Taylor, K., Selvan, L. D. N., Balakrishnan, L., Subbannayya, T., Yan, F., Prasad, T. S. K., Gowda, H., et al. (2014) · Journal of Proteome Research · doi:10.1021/pr401169d · Cited by 28
Self-citationSorting of a multi-subunit ubiquitin ligase complex in the endolysosome system
Yang, X., Arines, F. M., Zhang, W., & Li, M. (2018) · eLife · doi:10.7554/elife.33116 · Cited by 28
msCRUSH: Fast Tandem Mass Spectral Clustering Using Locality Sensitive Hashing
Wang, L., Li, S., & Tang, H. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00448 · Cited by 30
The Cannabis Proteome Draft Map Project
Jenkins, C., & Orsburn, B. C. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21030965 · Cited by 26
Emerging Roles of BRD7 in Pathophysiology
Park, S. W., & Lee, J. M. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21197127 · Cited by 29
Rocha, A. L. S., Pai, V. J., Perkins, G. A., Chang, T., Ma, J., de Souza, E. V., Chu, Q., Vaughan, J. M., Diedrich, J. K., Ellisman, M. H., et al. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168559 · Cited by 31
Functional genomics of human brain development and implications for autism spectrum disorders
Ziats, M. N., Grosvenor, L. P., & Rennert, O. M. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.153 · Cited by 27
Insights into the Genetic Foundations of Human Communication
Graham, S. A., Deriziotis, P., & Fisher, S. E. (2015) · Neuropsychology Review · doi:10.1007/s11065-014-9277-2 · Cited by 28
Karlsson, C. A. Q., Järnum, S., Winstedt, L., Kjellman, C., Björck, L., Linder, A., & Malmström, J. A. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra117.000525 · Cited by 27
Small GTPases in platelet membrane trafficking
Walsh, T. G., Li, Y., Wersäll, A., & Poole, A. W. (2018) · Platelets · doi:10.1080/09537104.2018.1535703 · Cited by 28
Mathiomica: an integrative platform for dynamic omics
Mias, G. I., Yusufaly, T., Roushangar, R., Brooks, L. R. K., Singh, V. V., & Christou, C. (2016) · Scientific Reports · doi:10.1038/srep37237 · Cited by 27
PRMT5 Enables Robust STAT3 Activation via Arginine Symmetric Dimethylation of SMAD7
Cai, C., Gu, S., Yu, Y., Zhu, Y., Zhang, H., Yuan, B., Shen, L., Yang, B., & Feng, X. (2021) · Advanced Science · doi:10.1002/advs.202003047 · Cited by 27
Vyas, H., Alcheikh, A., Lowe, G., Stevenson, W. S., Morgan, N. V., & Rabbolini, D. J. (2022) · Platelets · doi:10.1080/09537104.2022.2071853 · Cited by 27
The utility of proteases in proteomics, from sequence profiling to structure and function analysis
Sun, B., Liu, Z., Liu, J., Zhao, S., Wang, L., & Wang, F. (2022) · PROTEOMICS · doi:10.1002/pmic.202200132 · Cited by 26
Pimienta, G., Fok, V., Haslip, M., Nagy, M., Takyar, S. S., & STEITZ, J. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124638 · Cited by 27
Toward the complete proteome of Synechocystis sp. PCC 6803
Gao, L., Wang, J., Ge, H., Fang, L., Zhang, Y., Huang, X., & Wang, Y. (2015) · Photosynthesis Research · doi:10.1007/s11120-015-0140-y · Cited by 27
An Integrative Proteomic Approach Identifies Novel Cellular SMYD2 Substrates
Ahmed, H., Duan, S., Arrowsmith, C. H., Baršytė-Lovejoy, D., & Schapira, M. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00220 · Cited by 27
Song, D., Yue, L., Zhang, J., Ma, S., Zhao, W., Guo, F., Fan, Y., Yang, H., Liu, Q., Zhang, D., et al. (2016) · Cancer Biology & Therapy · doi:10.1080/15384047.2016.1156262 · Cited by 27
Mining Exosomal Genes for Pancreatic Cancer Targets
Makler, A., & Narayanan, R. (2017) · Cancer Genomics & Proteomics · doi:10.21873/cgp.20028 · Cited by 27
Schaffer, L. V., Shortreed, M. R., Cesnik, A. J., Frey, B. L., Solntsev, S. K., Scalf, M., & Smith, L. M. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04221 · Cited by 27
Hao, Y., Colak, R., Teyra, J., Corbi-Verge, C., Ignatchenko, A., Hahne, H., Wilhelm, M., Kuster, B., Braun, P., Kaida, D., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.06.031 · Cited by 23
Choudhari, J. K., Pant, S., Jha, R., Pathak, R. K., & Singh, D. B. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-023-02049-4 · Cited by 32
Nandal, S., & Burt, T. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18020448 · Cited by 28
GeLC-MS: A Sample Preparation Method for Proteomics Analysis of Minimal Amount of Tissue
Makridakis, M., & Vlahou, A. (2017) · Methods in molecular biology · doi:10.1007/7651_2017_76 · Cited by 26
Multi-omics Characterization of Interaction-mediated Control of Human Protein Abundance levels
Sousa, A., Gonçalves, E., Mirauta, B. A., Ochoa, D., Stegle, O., & Beltrão, P. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001280 · Cited by 27
Nataf, S., Guillen, M., & Pays, L. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02704 · Cited by 25
Panda, A., Yadav, A., Yeerna, H., Singh, A., Biehl, M. L., Lux, M., Schulz, A., Klecha, T., Doniach, S., Khiabanian, H., et al. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa485 · Cited by 25
Hao, F., Fei, X., Ren, X., Xiao, J. X., Chen, Y., & Wang, J. (2020) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.15790 · Cited by 26
A simple organic solvent precipitation method to improve detection of low molecular weight proteins
Periasamy, P., Rajandran, S., Ziegman, R., Casey, M., Nakamura, K., Kore, H., Datta, K. K., & Gowda, H. (2021) · PROTEOMICS · doi:10.1002/pmic.202100152 · Cited by 26
Self-citationAPPRIS principal isoforms and MANE Select transcripts define reference splice variants
Pozo, F., Rodrı́guez, J. M., Gómez, L. M., Vázquez, J., & Tress, M. L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac473 · Cited by 27
Malka, Y., Alkan, F., Ju, S., Körner, P.-R., Pataskar, A., Shulman, E. D., Loayza‐Puch, F., Champagne, J., Wenzel, C., Faller, W. J., et al. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.09.036 · Cited by 26
Deep Proteogenomics of a Photosynthetic Cyanobacterium
Spät, P., Krauspe, V., Hess, W. R., Maček, B., & Nalpas, N. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00065 · Cited by 26
Gridling, M., Ficarro, S. B., Breitwieser, F. P., Song, L., Parapatics, K., Colinge, J., Haura, E. B., Marto, J. A., Superti‐Furga, G., Bennett, K. L., et al. (2014) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-14-0152 · Cited by 26
The potential clinical impact of the release of two drafts of the human proteome
Ezkurdia, I., Calvo, E., del Pozo, Á., Vázquez, J., Valencia, A., & Tress, M. L. (2015) · Expert Review of Proteomics · doi:10.1586/14789450.2015.1103186 · Cited by 26
Issues in performance evaluation for host–pathogen protein interaction prediction
Abbasi, W. A., & Minhas, F. (2016) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720016500116 · Cited by 28
Personalized Medicine in Respiratory Disease
Priyadharshini, V. S., & Teran, L. M. (2015) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2015.11.008 · Cited by 27
Navas-Carrillo, D., Marín, F. R., Valdés, M., & Orenes‐Piñero, E. (2016) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2016.1241214 · Cited by 26
Cox, E. D., Hwang, W., Uzoma, I. C., Hu, J., Guzzo, C. M., Jeong, J. S., Matunis, M. J., Qian, J. X., Zhu, H., & Blackshaw, S. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.063719 · Cited by 24
Annotation of the Domestic Pig Genome by Quantitative Proteogenomics
Marx, H., Hahne, H., Ulbrich, S. E., Schnieke, A., Rottmann, O., Frishman, D., & Küster, B. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00184 · Cited by 26
An amino acid domino effect orchestrates ClpP's conformational states
Stahl, M. M. S., & Sieber, S. A. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2017.08.007 · Cited by 25
Lombard‐Banek, C., Moody, S. A., & Nemes, P. (2016) · Angewandte Chemie · doi:10.1002/ange.201510411 · Cited by 27
CK-666 and CK-869 differentially inhibit Arp2/3 iso-complexes
Cao, L., Huang, S. C. M., Basant, A., Mladenov, M., & Way, M. (2024) · EMBO Reports · doi:10.1038/s44319-024-00201-x · Cited by 26
Current understanding of functional peptides encoded by lncRNA in cancer
Tian, H., Tang, L., Yang, Z., Xiang, Y., Min, Q., Yin, M., You, H., Xiao, Z., & Shen, J. (2024) · Cancer Cell International · doi:10.1186/s12935-024-03446-7 · Cited by 25
Cis to trans: small ORF functions emerging through evolution
Baena-Angulo, C., Platero, A. I., & Couso, J. P. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.10.012 · Cited by 25
Politis, A., & Schmidt, C. (2017) · Journal of Proteomics · doi:10.1016/j.jprot.2017.04.019 · Cited by 24
Advances in the Chromosome-Centric Human Proteome Project: looking to the future
Paik, Y., Omenn, G. S., Hancock, W. S., Lane, L., & Overall, C. M. (2017) · Expert Review of Proteomics · doi:10.1080/14789450.2017.1394189 · Cited by 25
Integrated Transcriptomic and Proteomic Analysis of Primary Human Umbilical Vein Endothelial Cells
Madugundu, A. K., Na, C. H., Nirujogi, R. S., Renuse, S., Kim, K. P., Burns, K. H., Wilks, C., Langmead, B., Ellis, S., Collado‐Torres, L., et al. (2019) · PROTEOMICS · doi:10.1002/pmic.201800315 · Cited by 25
Self-citationPlant Proteome Databases and Bioinformatic Tools: An Expert Review and Comparative Insights
Subba, P., Kotimoole, C. N., & Prasad, T. S. K. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0024 · Cited by 25
Self-citationAppadurai, R., Uversky, V. N., & Srivastava, A. K. (2019) · The Journal of Membrane Biology · doi:10.1007/s00232-019-00069-2 · Cited by 25
Menschaert, G., Wang, X., Jones, A. R., Ghali, F., Fenyö, D., Olexiouk, V., Zhang, B., Deutsch, E. W., Ternent, T., & Vizcaíno, J. A. (2018) · Genome biology · doi:10.1186/s13059-017-1377-x · Cited by 23
Floeth, M., Elges, S., Gerß, J., Schwöppe, C., Keßler, T., Herold, T., Wardelmann, E., Berdel, W. E., Lenz, G., Mikesch, J., et al. (2020) · British Journal of Haematology · doi:10.1111/bjh.16853 · Cited by 24
Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis
Bakochi, A., Mohanty, T., Pyl, P. T., Gueto-Tettay, C. A., Malmström, L., Linder, A., & Malmström, J. A. (2021) · eLife · doi:10.7554/elife.64159 · Cited by 27
Živković, D., Dafun, A. S., Menneteau, T., Schahl, A., Lise, S., Kervarrec, C., Rêgo, A. T., da Fonseca, P. C. A., Chavent, M. G., Pineau, C., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2116826119 · Cited by 29
Medina, J. S. G., Sienkiewicz, K., Narayanan, S. A., Overbey, E. G., Grigorev, K. A., Ryon, K. A., Burke, M., Proszynski, J., Tierney, B. T., Schmidt, C. M., et al. (2024) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbae007 · Cited by 30
Upadhyay, A., Amanullah, A., Chhangani, D., Mishra, R., Prasad, A., & Mishra, A. (2015) · Molecular Neurobiology · doi:10.1007/s12035-015-9379-8 · Cited by 24
Nirujogi, R. S., Wright, J. D., Manda, S. S., Zhong, J., Na, C. H., Meyerhoff, J. L., Benton, B. J., Jabbour, R. E., Willis, K. L., Kim, M., et al. (2014) · PROTEOMICS · doi:10.1002/pmic.201400371 · Cited by 25
Self-citationvan Unen, J., Yin, T., Wu, Y., Mastop, M., Gadella, T. W. J., & Goedhart, J. (2016) · Scientific Reports · doi:10.1038/srep36825 · Cited by 23
Konstantakou, E. G., Velentzas, A. D., Anagnostopoulos, A. K., Litou, Z. I., Konstandi, O. A., Giannopoulou, A. F., Anastasiadou, E., Voutsinas, G. E., Tsangaris, G. T., & Stravopodis, D. J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171512 · Cited by 26
Wilkes, E. H., Casado, P., Rajeeve, V., & Cutillas, P. R. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o116.064360 · Cited by 25
Bioinformatics and genomic databases
Chen, J., & Coppola, G. (2018) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63233-3.00007-5 · Cited by 26
A Sulfoxide‐Based Isobaric Labelling Reagent for Accurate Quantitative Mass Spectrometry
Stadlmeier, M., Bogena, J., Wallner, M., Wühr, M., & Carell, T. (2018) · Angewandte Chemie International Edition · doi:10.1002/anie.201708867 · Cited by 24
Uversky, V. N. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely023 · Cited by 26
New technologies to analyse protein function: an intrinsic disorder perspective
Uversky, V. N. (2020) · F1000Research · doi:10.12688/f1000research.20867.1 · Cited by 26
O’Neill, J. R., Pak, H., Pairo‐Castineira, E., Save, V., Paterson‐Brown, S., Nenutil, R., Vojtěšek, B., Overton, I. M., Scherl, A., & Hupp, T. R. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.065078 · Cited by 24
Only one health, and so many omics
Pećina‐Šlaus, N., & Pecina, M. M. (2015) · Cancer Cell International · doi:10.1186/s12935-015-0212-2 · Cited by 25
On the Roles of Protein Intrinsic Disorder in the Origin of Life and Evolution
Uversky, V. N. (2024) · Life · doi:10.3390/life14101307 · Cited by 24
Proteoform medicine: characterizing and targeting protein forms in human disease
Korchak, J. A., Yi, S. S., Kelleher, N. L., Sahni, N., & Sheynkman, G. (2026) · Nature Reviews Genetics · doi:10.1038/s41576-025-00915-1 · Cited by 26
Mouse genome annotation by the RefSeq project
McGarvey, K. M., Goldfarb, T., Cox, E. D., Farrell, C. M., Gupta, T., Joardar, V., Kodali, V. K., Murphy, M. R., O’Leary, N. A., Pujar, S., et al. (2015) · Mammalian Genome · doi:10.1007/s00335-015-9585-8 · Cited by 24
A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe
Berto, S., Perdomo-Sabogal, Á., Wiegreffe, D., Qin, J., & Nowick, K. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00031 · Cited by 23
Uddin, M. J., Codner, D., Hasan, S. M. M., Scherer, S. W., O’Rielly, D. D., & Rahman, P. (2015) · Scientific Reports · doi:10.1038/srep10314 · Cited by 25
Narumi, R., Masuda, K., Tomonaga, T., Adachi, J., Ueda, H. R., & Shimizu, Y. (2018) · Synthetic and Systems Biotechnology · doi:10.1016/j.synbio.2018.02.004 · Cited by 25
Protein Bait Hypothesis: circRNA-Encoded Proteins Competitively Inhibit Cognate Functional Isoforms
Zhao, H., Zhou, Q., & Li, X. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.04.002 · Cited by 25
Alternative splicing and liver disease
Baralle, M., & Baralle, F. E. (2021) · Annals of Hepatology · doi:10.1016/j.aohep.2021.100534 · Cited by 25
Han, P., Cao, P., Yue, J., Kong, K., Hu, S., Deng, Y., Li, L., Li, F., & Zhao, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.837248 · Cited by 24
Multi‐Omics Studies in Historically Excluded Populations: The Road to Equity
Yang, G., Mishra, M., & Perera, M. A. (2022) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2818 · Cited by 24
Ali, M. M., Naz, S., Ashraf, S., Knapp, S., & Ul‐Haq, Z. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.123428 · Cited by 25
Szulik, M. W., Valdez, S., Walsh, M., Davis, K., Bia, R. D., Horiuchi, E., O’Very, S., Laxman, A. K., Nicolova, L., Eberhardt, D., et al. (2023) · Basic Research in Cardiology · doi:10.1007/s00395-023-00991-6 · Cited by 26
Ly, T., Endo, A., Brenes, A. J., Gierliński, M., Afzal, V., Pawellek, A., & Lamond, A. I. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14392.1 · Cited by 21
Genome-wide analysis identified 17 new loci influencing intraocular pressure in Chinese population
Huang, L., Chen, Y., Lin, Y., Tam, P. O. S., Cheng, Y., Shi, Y., Gong, B., Lü, F., Yang, J., Wang, H., et al. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9430-2 · Cited by 24
When metabolism meets physiology: Harvey and HarvettaPreprint
Thiele, I., Sahoo, S., Heinken, A., Heirendt, L., Aurich, M. K., Noronha, A., & Fleming, R. M. T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/255885 · Cited by 24
Oncogenic KRAS creates an aspartate metabolism signature in colorectal cancer cells
Doubleday, P. F., Fornelli, L., Ntai, I., & Kelleher, N. L. (2021) · The FEBS Journal · doi:10.1111/febs.16111 · Cited by 23
Zhou, N., Peng, L., Luo, Q., Yin, T., Sun, H., Zhang, Y., Shi, X., Peng, X., Bao, J., Ning, Y., et al. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf1119 · Cited by 35
Yagoub, D., Tay, A. P., Chen, Z., Hamey, J. J., Cai, C., Chia, S. Z., Hart‐Smith, G., & Wilkins, M. R. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00734 · Cited by 23
Yang, L., Lian, X., Zhang, W., Guo, J., Wang, Q., Li, Y., Chen, Y., Yin, X., Yang, P., Lan, F., et al. (2015) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b00480 · Cited by 23
Pancreatic Islet Protein Complexes and Their Dysregulation in Type 2 Diabetes
Pedersen, H., Guðmundsdóttir, V., & Brunak, S. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00043 · Cited by 22
Metadata
- Authors
- Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., Thomas, J. K., Muthusamy, B., Leal-Rojas, P., Kumar, P., Sahasrabuddhe, N. A., Balakrishnan, L., Advani, J., George, B., Renuse, S., Selvan, L. D. N., Patil, A. H., Nanjappa, V., Radhakrishnan, A., Prasad, S., Subbannayya, T., Raju, R., Kumar, M., Sreenivasamurthy, S. K., Marimuthu, A., Sathe, G. J., Chavan, S., Datta, K. K., Subbannayya, Y., Sahu, A., Yelamanchi, S. D., Jayaram, S., Rajagopalan, P., Sharma, J., Murthy, K. R., Syed, N., Goel, R., Khan, A. A., Ahmad, S., Dey, G., Mudgal, K., Chatterjee, A., Huang, T.-C., Zhong, J., Wu, X., Shaw, P. G., Freed, D., Zahari, M. S., Mukherjee, K. K., Shankar, S., Mahadevan, A., Lam, H., Mitchell, C. J., Shankar, S. K., Satishchandra, P., Schroeder, J. T., Sirdeshmukh, R., Maitra, A., Leach, S. D., Drake, C. G., Halushka, M. K., Prasad, T. S. K., Hruban, R. H., Kerr, C. L., Bader, G. D., Iacobuzio-Donahue, C. A., Gowda, H., & Pandey, A.
- Affiliations
- Johns Hopkins University (US)
- Institute of Bioinformatics (IN)
- University of Toronto (CA)
- Imperial College London (GB)
- Post Graduate Institute of Medical Education and Research (IN)
- National Institute of Mental Health and Neurosciences (IN)
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- Gowda, Harsha
- Pandey, Akhilesh
- ORCID
- Kim, Min-Sik
- Pinto, Sneha M.
- Getnet, Derese
- Nirujogi, Raja Sekhar
- Manda, Srikanth S.
- Chaerkady, Raghothama
- Madugundu, Anil K.
- Kelkar, Dhanashree S.
- Isserlin, Ruth
- Muthusamy, Babylakshmi
- Kumar, Praveen
- Balakrishnan, Lavanya
- Advani, Jayshree
- George, Bijesh
- Renuse, Santosh
- Selvan, Lakshmi Dhevi N.
- Patil, Arun H.
- Nanjappa, Vishalakshi
- Prasad, Samarjeet
- Subbannayya, Tejaswini
- Raju, Rajesh
- Kumar, Manish
- Sreenivasamurthy, Sreelakshmi K.
- Sathe, Gajanan J.
- Chavan, Sandip
- Datta, Keshava K.
- Subbannayya, Yashwanth
- Yelamanchi, Soujanya D.
- Jayaram, Savita
- Sharma, Jyoti
- Syed, Nazia
- Khan, Aafaque A.
- Ahmad, Sartaj
- Dey, Gourav
- Mudgal, Keshav
- Chatterjee, Aditi
- Huang, Tai-Chung
- Zhong, Jun
- Wu, Xinyan
- Freed, Donald
- Mukherjee, Kanchan K.
- Shankar, Subramanian
- Mahadevan, Anita
- Lam, Henry
- Schroeder, John T.
- Maitra, Anirban
- Leach, Steven D.
- Drake, Charles G.
- Halushka, Marc K.
- Prasad, T. S. Keshava
- Hruban, Ralph H.
- Bader, Gary D.
- Gowda, Harsha
- Pandey, Akhilesh
- Venue
- Nature
- Published
- 2014-05-28
- Volume
- 509
- Issue
- 7502
- Pages
- 575-581
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4403737
- OpenAlex
- W2056499061
- Cited by
- 2,198
- References
- 45
- Citations by year
- 2026: 59 · 2025: 93 · 2024: 110 · 2023: 150 · 2022: 151 · 2021: 201 · 2020: 177 · 2019: 176 · 2018: 203 · 2017: 244 · 2016: 253 · 2015: 276 · 2014: 102
- FWCI
- 125.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Omeka S (Universidad de La Frontera) (submittedVersion, cc-by-nc-nd)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (HHS-N268201000032C)
- National Cancer Institute (U24CA160036)
- National Institute of General Medical Sciences (T32GM007814, U54GM103520, P41GM103504)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- human proteome, proteome map, proteogenomics, non-coding RNA, upstream open reading frames, protein-coding genes, human tissue, hematopoietic cells, fetal tissue, tissue-specific proteins, long intergenic non-coding RNA, mRNA-protein correlation, Human Protein Atlas
- MeSH terms
- Adult, Cells, Cultured, Fetus, Fourier Analysis, Hematopoietic Stem Cells, Humans, Organ Specificity, Pseudogenes, Mass Spectrometry, Protein Biosynthesis, Reproducibility of Results, Genome, Human, Open Reading Frames, Protein Isoforms, Internet, Untranslated Regions, Proteome, Gene Expression Profiling, Protein Transport, Protein Sorting Signals, RNA, Untranslated, Databases, Protein, Proteomics, Molecular Sequence Annotation
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
