GeneFriends: An online co-expression analysis tool to identify novel gene targets for aging and complex diseases

Abstract

BackgroundAlthough 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.ResultsWe 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.ConclusionsGeneFriends 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/.

CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments

“…CLIP-GENE was compared with methods and tools that can be used for RNA-seq mouse data. In this study we compared with DEG method (DEG), integrative analysis method (IA) [7], and GeneFriends [14] in terms of the predictive power. In addition, since the user can specify context with a set of keywords, the performance depends on the context that the user provides.…”

“…Only 3 among 27 tools (listed in Gene Prioritization Portal [13]) are designed for the mouse data [14–16]. However, we think that these tools are generally not applicable to evaluate RNA-seq data of knockout experiments.…”

“…However, we think that these tools are generally not applicable to evaluate RNA-seq data of knockout experiments. For example, even though PINTA [16] and GeneFriends [14] can prioritize genes based on the concept of the guilt-by-association or network analysis, these tools require a pre-selected gene list of a certain size: up to 200 genes in PINTA and up to 500 genes in GeneFriends. Both tools are not applicable when the number of genes are large, such as DEG results.…”

MaxLink: network-based prioritization of genes tightly linked to a disease seed set

“…text mining (Gonzalez et al, 2008) or functional annotation analysis (Liekens et al, 2011), and data sources, e.g. gene expression (van Dam et al, 2012) and PPI networks (K € ohler et al, 2008). Most of the tools require a distinct region of the genome as input (Seelow et al, 2008) to narrow down the search space.…”

The Scientific Quest for Lasting Youth: Prospects for Curing Aging

“…57 For example, we and others have developed computational methods to predict with increasing accuracy new genes associated with aging, 58 CR, 59 and complex agerelated diseases like cancer. 60 Increasing our predictive power will lead to more precise targeting of biological systems to preserve health and fight disease. Holliday 7 makes the good point that to intervene in aging we need to understand its multiple facets and how they interact with each other.…”