Cluster Locator, online analysis and visualization of gene clustering

Obregón, Flavio Pazos; Soto, Pablo; Lavín, José L.; Cortázar, Ana R.; Barrio, Rosa; Aransay, Ana M.; Cantera, Rafael

doi:10.1093/bioinformatics/bty336

Cited by 23 publications

(24 citation statements)

References 15 publications

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“…2). We then formally analyzed the distribution of genes along the chromosomes using Cluster Locator (http://clusterlocator.bnd.edu.uy/) [44] and the D. melanogaster reference genome (Flybase Release 6.17) with a Max-gap = 5 parameter. Two-sided Kolmogorov-Smirnov tests were used to determine if the genes entered in the list were uniformly distributed along the chromosome arms and whether the numbers of realized clusters is different to 1000 randomly generated gene lists.…”

Section: Resultsmentioning

confidence: 99%

Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks

2019

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Background Mitochondria perform many key roles in their eukaryotic hosts, from integrating signaling pathways through to modulating whole organism phenotypes. The > 1 billion years of nuclear and mitochondrial gene co-evolution has necessitated coordinated expression of gene products from both genomes that maintain mitochondrial, and more generally, eukaryotic cellular function. How mitochondrial DNA (mtDNA) variation modifies host fitness has proved a challenging question but has profound implications for evolutionary and medical genetics. In Drosophila, we have previously shown that recently diverged mtDNA haplotypes within-species can have more impact on organismal phenotypes than older, deeply diverged haplotypes from different species. Here, we tested the effects of mtDNA haplotype variation on gene expression in Drosophila under standardized conditions. Using the Drosophila Genetic Reference Panel (DGRP), we constructed a panel of mitonuclear genotypes that consists of factorial variation in nuclear and mtDNA genomes, with mtDNAs originating in D. melanogaster (2x haplotypes) and D. simulans (2x haplotypes). Results We show that mtDNA haplotype variation unequivocally alters nuclear gene expression in both females and males, and mitonuclear interactions are pervasive modifying factors for gene expression. There was appreciable overlap between the sexes for mtDNA-sensitive genes, and considerable transcriptional variation attributed to particular mtDNA contrasts. These genes are generally found in low-connectivity gene co-expression networks, occur in gene clusters along chromosomes, are often flanked by non-coding RNA, and are under-represented among housekeeping genes. Finally, we identify the giant ( gt ) transcription factor motif as a putative regulatory sequence associated with mtDNA-sensitive genes. Conclusions There are predictive conditions for nuclear genes that are influenced by mtDNA variation. Electronic supplementary material The online version of this article (10.1186/s12864-019-6061-y) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks

2019

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“…Remarkably, although STRING analysis of the vtRNA1-1 associated 37 protein coding genes did not find enriched process or pathways, 36 of the 37 correlated genes are located at chromosome 5q region. Using the Cluster Locator algorithm ( Pazos Obregón et al , 2018 ), we found a statistically significant non-random clustering behavior for the chromosomal location of the genes co-regulated with vtRNA1-1 (p-value < 1×10 -10 ). Indeed, when the analysis was extended to 173 protein coding genes with Spearman correlation rs ≥ 0.3, 139 genes of 173 were situated at chromosome 5q region (p-value < 1×10 -10 ).…”

Section: Resultsmentioning

confidence: 99%

“…The pathway enrichment analysis was done using STRING software for the Gene Ontology Biological Process and KEGG pathways categories ( Szklarczyk et al , 2017 ). The cluster localization analysis was performed with two approaches, the Cluster Locator algorithm ( Pazos Obregón et al , 2018 ) and the Enrichr software ( Kuleshov et al , 2016 ) ( Extended Data : Tables S9 and S10).…”

Section: Methodsmentioning

confidence: 99%

Pan-cancer chromatin analysis of the human vtRNA genes uncovers their association with cancer biology

Fort

Duhagon

2021

F1000Res

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Background: The vault RNAs (vtRNAs) are a class of 84-141-nt eukaryotic non-coding RNAs transcribed by RNA polymerase III, associated to the ribonucleoprotein complex known as vault particle. Of the four human vtRNA genes, vtRNA1-1, vtRNA1-2 and vtRNA1-3, clustered at locus 1, are integral components of the vault particle, while vtRNA2-1 is a more divergent homologue located in a second locus. Gene expression studies of vtRNAs in large cohorts have been hindered by their unsuccessful sequencing using conventional transcriptomic approaches. Methods: VtRNA expression in The Cancer Genome Atlas (TCGA) Pan-Cancer cohort was estimated using the genome-wide DNA methylation and chromatin accessibility data (ATAC-seq) of their genes as surrogate variables. The association between vtRNA expression and patient clinical outcome, immune subtypes and transcriptionally co-regulated gene programs was analyzed in the dataset. Results: VtRNAs promoters are enriched in transcription factors related to viral infection. VtRNA2-1 is likely the most independently regulated homologue. VtRNA1-1 has the most accessible chromatin, followed by vtRNA1-2, vtRNA2-1 and vtRNA1-3. VtRNA1-1 and vtRNA1-3 chromatin status does not significantly change in cancer tissues. Meanwhile, vtRNA2-1 and vtRNA1-2 expression is widely deregulated in neoplastic tissues and its alteration is compatible with a broad oncogenic role for vtRNA1-2, and both tumor suppressor and oncogenic functions for vtRNA2-1. Yet, vtRNA1-1, vtRNA1-2 and vtRNA2-1 promoter DNA methylation predicts a shorter patient overall survival cancer-wide. In addition, gene ontology analyses of vtRNAs co-regulated genes identify a chromosome regulatory domain, epithelial differentiation, immune and thyroid cancer gene sets for specific vtRNAs. Furthermore, vtRNA expression patterns are associated with cancer immune subtypes and vtRNA1-2 expression is positively associated with cell proliferation and wound healing. Conclusions: Our study presents the landscape of vtRNA chromatin status cancer-wide, identifying co-regulated gene networks and ontological pathways associated with the different vtRNA genes that may account for their diverse roles in cancer.

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“…The pathway enrichment analysis was done using STRING software for the Gene Ontology Biological Process and KEGG pathways categories (Szklarczyk et al, 2017). The cluster localization analysis was performed with two approaches, the Cluster Locator algorithm (Pazos Obregón et al, 2018) and the Enrichr software (Kuleshov et al, 2016).…”

Section: Pathway Enrichment and Cluster Chromosome Localization Analymentioning

confidence: 99%

Pan-Cancer chromatin analysis of the human vtRNA genes uncovers their association with cancer biology

Fort

Duhagon

2020

Preprint

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The vault RNAs (vtRNAs) are a class of 84-141 nt eukaryotic non-coding RNAs transcribed by RNA polymerase III, named for their association with the conserved vault particle, a riboprotein complex whose function remains poorly understood. Of the 4 human vtRNA genes, the three clustered at locus 1, i.e. vtRNA1-1, vtRNA1-2 and vtRNA1-3, are integral components of the vault particle, while vtRNA2-1 is a more divergent homologue located in a second locus. Gene expression studies of vtRNAs in large cancer cohorts have been hindered by the failure of vtRNA sequencing using conventional transcriptomic approaches. However, since the vtRNAs transcription is regulated by DNA methylation, the analysis of the chromatin status of their promoters is a suitable surrogate approach to study their expression. Here we infer the landscape of vtRNA expression in cancer from the genome-wide DNA methylation (Illumina Infinium Human Methylation 450 K BeadChip) and chromatin accessibility (ATAC-seq) data of The Cancer Genome Atlas (TCGA). On average, vtRNA1-1 has the most accessible chromatin, followed by vtRNA1-2, vtRNA2-1 and vtRNA1-3. The correlation of the chromatin status of the vtRNA promoters and the binding sites of a common core of transcription factors stands for their transcriptional co-regulation by factors related to viral infection. Yet, vtRNA2-1 is the most independently regulated vtRNA homologue across tissue types. VtRNA1-1 and vtRNA1-3 chromatin status does not significantly change in cancer, though vtRNA1-3 promoter has repressive chromatin marks in a few cancer types. However, vtRNA2-1 and vtRNA1-2 expression are widely deregulated in neoplastic tissues and is compatible with a broad oncogenic role of vtRNA1-2, and both tumor suppressor and oncogenic functions of vtRNA2-1 depending of tissue contexts. Yet, vtRNA1-1, vtRNA1-2 and vtRNA2-1 promoter DNA methylation predicts a shorter patient overall survival cancer-wide. In addition, gene ontology analyses of co-regulated genes identifies a chromosome 5 regulatory domain controlling vtRNA1-1 and neighboring genes, and epithelial differentiation, immune and thyroid cancer gene sets for vtRNA1-2, vtRNA2-1 and vtRNA1-3 respectively. Furthermore, vtRNA expression patterns are associated with cancer immune subtypes. Finally, vtRNA1-2 expression is positively associated with cell proliferation and wound healing, in agreement with its oncogenic expression profile. Overall, our study presents the landscape of vtRNA expression cancer-wide, identifying co-regulated gene networks and ontological pathways associated with the different vtRNA genes that may account for their diverse roles in cancer.

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Cluster Locator, online analysis and visualization of gene clustering

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 23 publications

References 15 publications

Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks

Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks

Pan-cancer chromatin analysis of the human vtRNA genes uncovers their association with cancer biology

Pan-Cancer chromatin analysis of the human vtRNA genes uncovers their association with cancer biology

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