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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.
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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]
The check wasn’t confident either way, so it gives no verdict — never disputed.
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]
Experimental — accuracy not yet measuredIn 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]
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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
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