GeneFriends: a human RNA-seq-based gene and transcript co-expression database
Open accessAbstract
Co-expression networks have proven effective at assigning putative functions to genes based on the functional annotation of their co-expressed partners, in candidate gene prioritization studies and in improving our understanding of regulatory networks. The growing number of genome resequencing efforts and genome-wide association studies often identify loci containing novel genes and there is a need to infer their functions and interaction partners. To facilitate this we have expanded GeneFriends, an online database that allows users to identify co-expressed genes with one or more user-defined genes. This expansion entails an RNA-seq-based co-expression map that includes genes and transcripts that are not present in the microarray-based co-expression maps, including over 10,000 non-coding RNAs. The results users obtain from GeneFriends include a co-expression network as well as a summary of the functional enrichment among the co-expressed genes. Novel insights can be gathered from this database for different splice variants and ncRNAs, such as microRNAs and lincRNAs. Furthermore, our updated tool allows candidate transcripts to be linked to diseases and processes using a guilt-by-association approach. GeneFriends is freely available from http://www.GeneFriends.org and can be used to quickly identify and rank candidate targets relevant to the process or disease under study.
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Co-expression analysis on RNA-seq data can assign putative roles to splice variants and lincRNAs and identify diseases in which they might play a part.
“Co-expression analysis on RNA-seq data can assign putative roles to these splice variants and lincRNAs [2], and identify diseases in which they might play a part [2].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [2]
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Gene co-expression analysis for functional classification and gene–disease predictions
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Self-citationUsing Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model
Wang, Y., Yang, S., Zhao, J., Du, W., Liang, Y., Wang, C., Zhou, F., Tian, Y., & Ma, Q. (2019) · Scientific Reports · doi:10.1038/s41598-019-40780-7 · Cited by 26 · see citing works
- Authors
- van Dam, S., Craig, T., & de Magalhães, J. P.
- Affiliations
- University of Liverpool (GB)
- ORCID
- Craig, Thomas
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-10-31
- Volume
- 43
- Issue
- D1
- Pages
- D1124-D1132
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4383890
- OpenAlex
- W2103632489
- Cited by
- 115
- References
- 70
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 17 · 2021: 13 · 2020: 9 · 2019: 18 · 2018: 7 · 2017: 7 · 2016: 11 · 2015: 11
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Directorate for Biological Sciences
- Biotechnology and Biological Sciences Research Council (BB/K016741/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene co-expression network, RNA-seq, non-coding RNA, microRNA, long intergenic non-coding RNA, guilt by association, functional enrichment, candidate gene prioritization, splice variants, regulatory networks
- MeSH terms
- Humans, Sequence Analysis, RNA, Internet, Gene Expression Profiling, RNA, Untranslated, Databases, Genetic, Gene Regulatory Networks
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