Comprehensive comparison of large-scale tissue expression datasets
Open accessPublished version of a preprint
Abstract
For tissues to carry out their functions, they rely on the right proteins to be present. Several high-throughput technologies have been used to map out which proteins are expressed in which tissues; however, the data have not previously been systematically compared and integrated. We present a comprehensive evaluation of tissue expression data from a variety of experimental techniques and show that these agree surprisingly well with each other and with results from literature curation and text mining. We further found that most datasets support the assumed but not demonstrated distinction between tissue-specific and ubiquitous expression. By developing comparable confidence scores for all types of evidence, we show that it is possible to improve both quality and coverage by combining the datasets. To facilitate use and visualization of our work, we have developed the TISSUES resource (http://tissues.jensenlab.org), which makes all the scored and integrated data available through a single user-friendly web interface.
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Self-citationAlzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
The BRENDA enzyme information system–From a database to an expert system
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TISSUES 2.0: an integrative web resource on mammalian tissue expression
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Self-citation
- Authors
- Santos, A., Tsafou, K., Stolte, C., Pletscher-Frankild, S., O’Donoghue, S. I., & Jensen, L. J.
- Affiliations
- University of Copenhagen (DK)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, Australia
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2015-06-30
- Volume
- 3
- Pages
- e1054
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC4493645
- OpenAlex
- W1700570096
- Cited by
- 92
- References
- 5
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 6 · 2023: 1 · 2022: 8 · 2021: 7 · 2020: 11 · 2019: 13 · 2018: 9 · 2017: 11 · 2016: 13 · 2015: 4
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PeerJ (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Commonwealth Scientific and Industrial Research Organisation
- Novo Nordisk (NNF14CC0001)
- National Institutes of Health
- National Cancer Institute (U54CA189205)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- tissue-specific protein expression, data integration, confidence scores, literature curation, text mining, high-throughput proteomics, data quality assessment
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