RAD: a web application to identify region associated differentially expressed genes

AbstractWith the advance of genomic sequencing techniques, chromatin accessible regions, transcription factor binding sites and epigenetic modifications can be identified at genome-wide scale. Conventional analyses focus on the gene regulation at proximal regions; however, distal regions are usually neglected, largely due to the lack of reliable tools to link the distal regions to coding genes. In this study, we introduce RAD (Region Associated Differentially expressed genes), a user-friendly web tool to identify both proximal and distal region associated differentially expressed genes. RAD maps the up- and down-regulated genes associated with any genomic regions of interest (gROI) and helps researchers to infer the regulatory function of these regions based on the distance of gROI to differentially expressed genes. RAD includes visualization of the results and statistical inference for significance.AvailabilityRAD is implemented with Python 3.7 and run on a Nginx server. RAD is freely available at http://labw.org/rad as online web service.

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RAD: a web application to identify region associated differentially expressed genes

Bioinformatics ◽

10.1093/bioinformatics/btab075 ◽

2021 ◽

Author(s):

Yixin Guo ◽

Ziwei Xue ◽

Ruihong Yuan ◽

Jingyi Jessica Li ◽

William A Pastor ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Web Application ◽

Distal Region ◽

Differentially Expressed ◽

Supplementary Information ◽

Regulatory Function ◽

Genome Wide ◽

Wide Scale ◽

Genomic Regions ◽

User Friendly

Abstract Summary With the advance of genomic sequencing techniques, chromatin accessible regions, transcription factor binding sites and epigenetic modifications can be identified at genome-wide scale. Conventional analyses focus on the gene regulation at proximal regions; however, distal regions are usually less focused, largely due to the lack of reliable tools to link these regions to coding genes. In this study, we introduce RAD (Region Associated Differentially expressed genes), a user-friendly web tool to identify both proximal and distal region associated differentially expressed genes (DEGs). With DEGs and genomic regions of interest (gROI) as input, RAD maps the up- and down-regulated genes associated with any gROI and helps researchers to infer the regulatory function of these regions based on the distance of gROI to differentially expressed genes. RAD includes visualization of the results and statistical inference for significance. Availability and implementation RAD is implemented with Python 3.7 and run on a Nginx server. RAD is freely available at https://labw.org/rad as online web service. Supplementary information Supplementary data are available at Bioinformatics online.

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Hi-C analyses with GENOVA: a case study with cohesin variants

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab040 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Robin H van der Weide ◽

Teun van den Brand ◽

Judith H I Haarhuis ◽

Hans Teunissen ◽

Benjamin D Rowland ◽

...

Keyword(s):

R Package ◽

Loop Formation ◽

Chromosome Conformation ◽

Genome Wide ◽

Score Analysis ◽

A Genome ◽

Contact Frequency ◽

Wide Scale ◽

Genomic Regions ◽

User Friendly

Abstract Conformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise chromosome conformation capture (3C) data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.

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Hi-C Analyses with GENOVA: a case study with cohesin variants

10.1101/2021.01.22.427620 ◽

2021 ◽

Author(s):

Robin H. van der Weide ◽

Teun van den Brand ◽

Judith H.I. Haarhuis ◽

Hans Teunissen ◽

Benjamin D. Rowland ◽

...

Keyword(s):

R Package ◽

Loop Formation ◽

Genome Wide ◽

Score Analysis ◽

A Genome ◽

Contact Frequency ◽

Wide Scale ◽

Specific Locus ◽

Genomic Regions ◽

User Friendly

AbstractConformation capture-approaches like Hi-C can elucidate chromosome structure at a genome-wide scale. Hi-C datasets are large and require specialised software. Here, we present GENOVA: a user-friendly software package to analyse and visualise conformation capture data. GENOVA is an R-package that includes the most common Hi-C analyses, such as compartment and insulation score analysis. It can create annotated heatmaps to visualise the contact frequency at a specific locus and aggregate Hi-C signal over user-specified genomic regions such as ChIP-seq data. Finally, our package supports output from the major mapping-pipelines. We demonstrate the capabilities of GENOVA by analysing Hi-C data from HAP1 cell lines in which the cohesin-subunits SA1 and SA2 were knocked out. We find that ΔSA1 cells gain intra-TAD interactions and increase compartmentalisation. ΔSA2 cells have longer loops and a less compartmentalised genome. These results suggest that cohesinSA1 forms longer loops, while cohesinSA2 plays a role in forming and maintaining intra-TAD interactions. Our data supports the model that the genome is provided structure in 3D by the counter-balancing of loop formation on one hand, and compartmentalization on the other hand. By differentially controlling loops, cohesinSA1 and cohesinSA2 therefore also affect nuclear compartmentalization. We show that GENOVA is an easy to use R-package, that allows researchers to explore Hi-C data in great detail.

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Advantages of using graph databases to explore chromatin conformation capture experiments

BMC Bioinformatics ◽

10.1186/s12859-020-03937-0 ◽

2021 ◽

Vol 22 (S2) ◽

Author(s):

Daniele D’Agostino ◽

Pietro Liò ◽

Marco Aldinucci ◽

Ivan Merelli

Keyword(s):

Web Application ◽

High Throughput Sequencing ◽

Cell Types ◽

Graph Database ◽

Graph Databases ◽

Sources Of Information ◽

Chromosome Conformation ◽

Wide Scale ◽

User Friendly ◽

Different Cell Types

Abstract Background High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells. Methods Here we discuss the use of a graph database for storing and analysing data achieved by performing Hi-C experiments. The main issue is the size of the produced data and, working with a graph-based representation, the consequent necessity of adequately managing a large number of edges (contacts) connecting nodes (genes), which represents the sources of information. For this, currently available graph visualisation tools and libraries fall short with Hi-C data. The use of graph databases, instead, supports both the analysis and the visualisation of the spatial pattern present in Hi-C data, in particular for comparing different experiments or for re-mapping omics data in a space-aware context efficiently. In particular, the possibility of describing graphs through statistical indicators and, even more, the capability of correlating them through statistical distributions allows highlighting similarities and differences among different Hi-C experiments, in different cell conditions or different cell types. Results These concepts have been implemented in NeoHiC, an open-source and user-friendly web application for the progressive visualisation and analysis of Hi-C networks based on the use of the Neo4j graph database (version 3.5). Conclusion With the accumulation of more experiments, the tool will provide invaluable support to compare neighbours of genes across experiments and conditions, helping in highlighting changes in functional domains and identifying new co-organised genomic compartments.

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Emerging Technologies for Genome-Wide Profiling of DNA Breakage

Frontiers in Genetics ◽

10.3389/fgene.2020.610386 ◽

2021 ◽

Vol 11 ◽

Author(s):

Matthew J. Rybin ◽

Melina Ramic ◽

Natalie R. Ricciardi ◽

Philipp Kapranov ◽

Claes Wahlestedt ◽

...

Keyword(s):

Genome Instability ◽

Dna Double Strand Breaks ◽

Single Nucleotide ◽

Strand Breaks ◽

Single Strand Breaks ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Nucleotide Resolution ◽

Genomic Regions

Genome instability is associated with myriad human diseases and is a well-known feature of both cancer and neurodegenerative disease. Until recently, the ability to assess DNA damage—the principal driver of genome instability—was limited to relatively imprecise methods or restricted to studying predefined genomic regions. Recently, new techniques for detecting DNA double strand breaks (DSBs) and single strand breaks (SSBs) with next-generation sequencing on a genome-wide scale with single nucleotide resolution have emerged. With these new tools, efforts are underway to define the “breakome” in normal aging and disease. Here, we compare the relative strengths and weaknesses of these technologies and their potential application to studying neurodegenerative diseases.

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