WebGestalt: an integrated system for exploring gene sets in various biological contexts

Zhang, Bing; Kirov, Stefan; Snoddy, Jay

doi:10.1093/nar/gki475

Cited by 1,637 publications

(1,537 citation statements)

References 33 publications

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“…A gene set of 158 AS genes (Fig. 2F, File S1) was analyzed with webgestalt (Zhang et al ., 2005; Wang et al ., 2013) and IPA (Ingenuity ® Systems, www.ingenuity.com). The most significant gene ontology (GO) biological processes enriched with AS genes were macromolecule metabolic processing and its subcategory RNA processing, including functions such as mRNA processing and RNA splicing (Table 1).…”

Section: Resultsmentioning

confidence: 99%

Global genome splicing analysis reveals an increased number of alternatively spliced genes with aging

et al. 2015

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SummaryAlternative splicing (AS) is a key regulatory mechanism for the development of different tissues; however, not much is known about changes to alternative splicing during aging. Splicing events may become more frequent and widespread genome‐wide as tissues age and the splicing machinery stringency decreases. Using skin, skeletal muscle, bone, thymus, and white adipose tissue from wild‐type C57BL6/J male mice (4 and 18 months old), we examined the effect of age on splicing by AS analysis of the differential exon usage of the genome. The results identified a considerable number of AS genes in skeletal muscle, thymus, bone, and white adipose tissue between the different age groups (ranging from 27 to 246 AS genes corresponding to 0.3–3.2% of the total number of genes analyzed). For skin, skeletal muscle, and bone, we included a later age group (28 months old) that showed that the number of alternatively spliced genes increased with age in all three tissues (P < 0.01). Analysis of alternatively spliced genes across all tissues by gene ontology and pathway analysis identified 158 genes involved in RNA processing. Additional analysis of AS in a mouse model for the premature aging disease Hutchinson–Gilford progeria syndrome was performed. The results show that expression of the mutant protein, progerin, is associated with an impaired developmental splicing. As progerin accumulates, the number of genes with AS increases compared to in wild‐type skin. Our results indicate the existence of a mechanism for increased AS during aging in several tissues, emphasizing that AS has a more important role in the aging process than previously known.

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Section: Resultsmentioning

confidence: 99%

Global genome splicing analysis reveals an increased number of alternatively spliced genes with aging

et al. 2015

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“…The residues at positions 82 and 373 are conserved in 58/96 and 78/88 species respectively. Proteins with ≥20% leucine content were analysed using WebGestalt [2] to identify biological processes that are significantly enriched for proteins with a high percentage leucine content. A hypergeometric analysis was applied with stringent Bonferonni correction to adjust for multiple testing.…”

Section: Supplementarymentioning

confidence: 99%

“…Liver failure in infancy is challenging to diagnose and manage, and has a high mortality rate [2]. First symptoms usually develop in early infancy and include poor feeding, failure to thrive, vomiting, jaundice, distended abdomen and hypoactivity.…”

Section: Introductionmentioning

confidence: 99%

Identification of a mutation in LARS as a novel cause of infantile hepatopathy

Casey

McGettigan

Lynam‐Lennon

et al. 2012

Molecular Genetics and Metabolism

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AbbreviationsSNP, single nucleotide polymorphism; PDB, protein data-bank; siRNA, small-interfering ribonucleic acid; LARS, gene encoding the cytoplasmic leucyl-tRNA synthetase; MMP, mitochondrial membrane potential; ROS, reactive oxygen species; LeuRS, leucyl-tRNA synthetase enzyme; aaRS, aminoacyl-tRNA synthetase; MELAS, mitochondrial encephalomyopathy lactic acidosis and stroke-like symptoms; LARS2, gene encoding the mitochondrial leucyl-tRNA synthetase; BCAAs, branched chain amino acids.

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“…First, we used three pathway tools that classified genes based on KEGG pathways: Webgestalt ''KEGG'' [Zhang et al, 2005], GATHER [Chang and Nevins, 2006], and DAVID [Huang da et al, 2007]. Second, we investigated two tools that used other pathway classification methods: PANTHER and Webgestalt ''BioCarta'' [Zhang et al, 2005].…”

Section: Pathway Toolsmentioning

confidence: 99%

“…In this study we assessed the usefulness of different pathway classification tools (Webgestalt ''KEGG'' and ''BioCarta'' [Zhang et al, 2005], GATHER [Chang and Nevins, 2006] and DAVID [Huang da et al, 2007], PANTHER ) to detect biologically overrepresented pathways in GWAS datasets. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology.…”

Section: Introductionmentioning

confidence: 99%

Using genome‐wide pathway analysis to unravel the etiology of complex diseases

Elbers

Eijk

Franke

et al. 2009

Genetic Epidemiology

167

137

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Several genome-wide association studies (GWAS) have been published on various complex diseases. Although, new loci are found to be associated with these diseases, still only very little of the genetic risk for these diseases can be explained. As GWAS are still underpowered to find small main effects, and gene-gene interactions are likely to play a role, the data might currently not be analyzed to its full potential. In this study, we evaluated alternative methods to study GWAS data. Instead of focusing on the single nucleotide polymorphisms (SNPs) with the highest statistical significance, we took advantage of prior biological information and tried to detect overrepresented pathways in the GWAS data. We evaluated whether pathway classification analysis can help prioritize the biological pathways most likely to be involved in the disease etiology. In this study, we present the various benefits and limitations of pathway-classification tools in analyzing GWAS data. We show multiple differences in outcome between pathway tools analyzing the same dataset. Furthermore, analyzing randomly selected SNPs always results in significantly overrepresented pathways, large pathways have a higher chance of becoming statistically significant and the bioinformatics tools used in this study are biased toward detecting well-defined pathways. As an example, we analyzed data from two GWAS on type 2 diabetes (T2D): the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC). Occasionally the results from the DGI and the WTCCC GWAS showed concordance in overrepresented pathways, but discordance in the corresponding genes. Thus, incorporating gene networks and pathway classification tools into the analysis can point toward significantly overrepresented molecular pathways, which cannot be picked up using traditional single-locus analyses. However, the limitations discussed in this study, need to be addressed before these methods can be widely used. Genet. Epidemiol. 33:419-431, 2009.

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WebGestalt: an integrated system for exploring gene sets in various biological contexts

Cited by 1,637 publications

References 33 publications

Global genome splicing analysis reveals an increased number of alternatively spliced genes with aging

Global genome splicing analysis reveals an increased number of alternatively spliced genes with aging

Identification of a mutation in LARS as a novel cause of infantile hepatopathy

Using genome‐wide pathway analysis to unravel the etiology of complex diseases

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