A draft map of the human proteome

By Min-Sik Kim, S. Pinto, D. Getnet, R. Nirujogi, Srikanth S. Manda, Raghothama Chaerkady, Anil K. Madugundu, Dhanashree S. Kelkar, Ruth Isserlin, Shobhit Jain and 62 more. Nature, 2014

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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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/1f50f651b32791d4d9e44d99dda8fa8c92bd35e7

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Citations in the corpus

The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins (2016, background): “HPM Cell Type and Tissue Protein Expression Profiles (34)”
Untargeted metabolomics strategies – Challenges and Emerging Directions (2016, background): “The annotated human metabolome is considered to be less complex than the proteome [2, 3], yet the diverse chemical structures exhibit a wide range of concentration, solubility, polarity, and volatility [4].”
Human protein interaction networks across tissues and diseases (2015, methodology): “Liu et al. (2014) used proteomic data (Kim et al., 2014) to analyze tissue interactomes.”
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update (2016, background): “We created gene set libraries from HumanCyc (15), a Nucleic Acids Research, 2016, Vol. 44, Web Server issue W91 metabolic pathway resource stored in BioPAX format (16); gene and small-molecule perturbations from the LINCS L1000 data set; NCI-Nature pathways (17); protein complexes from the NURSA pro”
A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, background): “(10) and a number of further data sets), the number of protein”
AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors (2018, background): “(33) and bgee database (34) of 22 animal species as well as the human protein expressions from Human Protein Map (35).”
Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016, background): “Recent technological advancement has permitted high-throughput measurement of human genome, epigenome, metabolome, transcriptome and proteome at the population level (G. T. Consortium, 2015; Kim et al., 2014; Roadmap Epigenomics et al., 2015; Shin et al., 2014; Wellcome Trust Case Control, 2007; Zil”
The UniProtKB guide to the human proteome (2016, background): “Similarly, high-throughput mass spectrometry-based proteomic studies recently allowed the production of draft maps of the human proteome (2, 3) and conserved macromolecular complexes in metazoans (4).”
Generative Modeling with Conditional Autoencoders: Building an Integrated Cell (2017, background): “However, determining cellular organization is challenged by the multitude of diﬀerent molecular complexes and organelles that comprise living cells and drive their behaviors (Kim et al., 2014).”