GeneFriends: An online co-expression analysis tool to identify novel gene targets for aging and complex diseases
Open accessAbstract
Background: Although many diseases have been well characterized at the molecular level, the underlying mechanisms are often unknown. Nearly half of all human genes remain poorly studied, yet these genes may contribute to a number of disease processes. Genes involved in common biological processes and diseases are often co-expressed. Using known disease-associated genes in a co-expression analysis may help identify and prioritize novel candidate genes for further study. Results: We have created an online tool, called GeneFriends, which identifies co-expressed genes in over 1,000 mouse microarray datasets. GeneFriends can be used to assign putative functions to poorly studied genes. Using a seed list of disease-associated genes and a guilt-by-association method, GeneFriends allows users to quickly identify novel genes and transcription factors associated with a disease or process. We tested GeneFriends using seed lists for aging, cancer, and mitochondrial complex I disease. We identified several candidate genes that have previously been predicted as relevant targets. Some of the genes identified are already being tested in clinical trials, indicating the effectiveness of this approach. Co-expressed transcription factors were investigated, identifying C/ebp genes as candidate regulators of aging. Furthermore, several novel candidate genes, that may be suitable for experimental or clinical follow-up, were identified. Two of the novel candidates of unknown function that were co-expressed with cancer-associated genes were selected for experimental validation. Knock-down of their human homologs (C1ORF112 and C12ORF48) in HeLa cells slowed growth, indicating that these genes of unknown function, identified by GeneFriends, may be involved in cancer. Conclusions: GeneFriends is a resource for biologists to identify and prioritize novel candidate genes involved in biological processes and complex diseases. It is an intuitive online resource that will help drive experimentation. GeneFriends is available online at:http://genefriends.org/.
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GBA approaches are widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease.
“GBA approaches are also widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease [26, 121–126] (Figure 1).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [126]
The Background and ‘Prediction of novel candidate genes in aging and complex diseases’ sections describe guilt-by-association as a way to identify candidate disease genes from their co-expression with known disease genes. The paper assesses the number and statistical significance of those connections; it does not specify the claimed rule that a substantial proportion of genes within a module must be disease-associated. Page numbers are unavailable in our copy. (LLM, conf=0.82)
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References 90 · Cited by 72
Research works printed but not linked: 2
- Loeffen JL, Smeitink JA, Trijbels JM, Janssen AJ, Triepels RH, et al: Isolated complex I deficiency in children: clinical, biochemical and genetic aspects. Hum Mutat 2000, 15:123-134.
- Tinari N, Kuwabara I, Huflejt ME, Shen PF, Iacobelli S, et al: Glycoprotein 90 K/MAC-2BP interacts with galectin-1 and mediates galectin-1-induced cell aggregation. Int J Cancer 2001, 91:167-172.
- Authors
- van Dam, S., Cordeiro, R., Craig, T., van Dam, J., Wood, S. H., & de Magalhães, J. P.
- Affiliations
- University of Liverpool (GB)
- Venue
- BMC Genomics
- Published
- 2012-10-06
- Volume
- 13
- Issue
- 1
- Pages
- 535-535
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC3495651
- OpenAlex
- W2152602212
- Cited by
- 72
- References
- 90
- Citations by year
- 2025: 2 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 10 · 2020: 4 · 2019: 6 · 2018: 5 · 2017: 6 · 2016: 7 · 2015: 6 · 2014: 13
- FWCI
- 2.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research Explorer (The University of Manchester) (submittedVersion, cc-by)
- Funders
- Wellcome Trust (ME050495MES)
- Biotechnology and Biological Sciences Research Council (BB/H008497/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- guilt by association, candidate gene identification, disease-associated genes, aging, cancer, transcription factors, C/EBP, web-based analysis tool, gene function annotation
- MeSH terms
- Aging, Animals, Humans, Neoplasms, Computational Biology, Internet, Oligonucleotide Array Sequence Analysis, Mitochondrial Diseases, Databases, Genetic, Electron Transport Complex I, Mice
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