GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome

Simovski, Boris; Vodák, Daniel; Gundersen, Sveinung; Domańska, Diana; Azab, Abdulrahman; Holden, Lars; Holden, Matthew T. G.; Grytten, Ivar; Rand, Knut Dagestad; Drabløs, Finn; Johansen, Morten; Mora, Antonio M.; Lund-Andersen, Christin; Fromm, Bastian; Eskeland, Ragnhild; Gabrielsen, Odd S.; Ferkingstad, Egil; Nakken, Sigve; Bengtsen, Mads; Nederbragt, Alexander Johan; Thorarensen, Hildur Sif; Akse, Johannes Andreas; Glad, Ingrid K.; Hovig, Eivind; Sandve, Geir Kjetil

doi:10.1093/gigascience/gix032

Cited by 23 publications

(26 citation statements)

References 38 publications

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“…Generally, the SNP densities decreased up to 1.5-times and the difference in the densities of the pre-miRNA and its flanks re-appeared. After additionally removing the rare SNPs [82] the densities decreased much stronger and their hierarchy was completely restored (seed < miRNA < pre-miRNA < flanks, Additional file 7). The common SNPs are likely older, they have been subjected to selective forces over time [83] and produces the difference between seed and the rest of miRNA.…”

Section: Resultsmentioning

confidence: 99%

Comparing miRNA structure of mirtrons and non-mirtrons

Titov

Vorozheykin

2017

Preprint

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Section: Resultsmentioning

confidence: 99%

Comparing miRNA structure of mirtrons and non-mirtrons

Titov

Vorozheykin

2017

Preprint

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“…Motif occurrences in constituent enhancers of super enhancers were identified using FIMO (find individual motif occurrences) at p-value threshold of 10 -7 and enriched pathways were identified using hypergeometric testing at a threshold of FDR-adjusted P-value <0.05. We used the GSuite hyperbrowser program (Simovski et al 2017) to perform a statistical analysis of over-representation of accessibility peaks with ENCODE datasets. To determine which ENCODE datasets exhibit the strongest similarity to accessibility regions we used the Forbes coefficient to obtain rankings of tracks, and Monte Carlo simulation to provide a statistical assessment of the robustness of the rankings of data tracks, using a null model derived from randomizing the positions of the accessibility regions relative to query tracks.…”

Section: Discussionmentioning

confidence: 99%

Mapping the landscape of chromatin dynamics during naïve CD4+ T-cell activation

Iqbal

Serralha

Kaur

et al. 2020

Preprint

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T-cell activation induces context-specific gene expression programs that promote energy generation and biosynthesis, progression through the cell cycle and ultimately cell differentiation. The aim of this study was to apply the omni ATAC-seq method to characterize the landscape of chromatin changes induced by T-cell activation in mature naïve CD4+ T-cells. Using a well-established ex vivo protocol of canonical T-cell receptor signaling, we generated genome-wide chromatin maps of naïve T-cells from pediatric donors in quiescent or recently activated states. We identified thousands of individual chromatin accessibility peaks that are associated with T-cell activation. The majority of these were localized to intronic and intergenic enhancer regions, marked by active histone modifications whilst quiescence was maintained by repressive histone marks. Regions of activation-associated gains in chromatin accessibility were enriched for well-known pioneer transcription factor motifs, and super-enhancer regions associated with distinct gene regulatory networks. These cis-regulatory elements together brought about distinct transcriptional signatures in activated cells including TNFa-NFkB signaling, hormone-responsive genes, inflammatory response genes and IL2-STAT5 signaling. Our data provides novel insights into the chromatin dynamics and motif usage of T-cell receptor signaling events in early life. The characterized pathways demonstrate the utility of chromatin profiling techniques applied to bio-banked samples for characterizing gene regulatory elements.

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“…To answer the question we implemented a hypothesis-testing framework inspired by work done in statistical genomics [37,38].…”

Section: Statistical Frameworkmentioning

confidence: 99%

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

Malone

Simovski²,

Moliné³

et al. 2020

Preprint

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The global population is at present suffering from a pandemic of Coronavirus disease 2019 , caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

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GSuite HyperBrowser: integrative analysis of dataset collections across the genome and epigenome

Cited by 23 publications

References 38 publications

Comparing miRNA structure of mirtrons and non-mirtrons

Comparing miRNA structure of mirtrons and non-mirtrons

Mapping the landscape of chromatin dynamics during naïve CD4+ T-cell activation

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs

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