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

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

10.1101/2020.08.03.234302 ◽

2020 ◽

Author(s):

Yixin Guo ◽

Ziwei Xue ◽

Ruihong Yuan ◽

William A. Pastor ◽

Wanlu Liu

Keyword(s):

Differentially Expressed Genes ◽

Web Application ◽

Distal Region ◽

Differentially Expressed ◽

Regulatory Function ◽

Genome Wide ◽

Wide Scale ◽

Genomic Regions ◽

User Friendly ◽

Online Web

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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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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Fast detection of differential chromatin domains with SCIDDO

Bioinformatics ◽

10.1093/bioinformatics/btaa960 ◽

2020 ◽

Author(s):

Peter Ebert ◽

Marcel H Schulz

Keyword(s):

Differentially Expressed Genes ◽

Differentially Expressed ◽

Chromatin State ◽

Supplementary Information ◽

Differential Analysis ◽

Chromatin Domains ◽

Chromatin Dynamics ◽

Histone Marks ◽

Chromatin Immunoprecipitation Sequencing ◽

Downstream Analysis

Abstract Motivation The generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a standard approach to dissect the complexity of the epigenome. Interpretation and differential analysis of histone datasets remains challenging due to regulatory meaningful co-occurrences of histone marks and their difference in genomic spread. To ease interpretation, chromatin state segmentation maps are a commonly employed abstraction combining individual histone marks. We developed the tool SCIDDO as a fast, flexible, and statistically sound method for the differential analysis of chromatin state segmentation maps. Results We demonstrate the utility of SCIDDO in a comparative analysis that identifies differential chromatin domains (DCD) in various regulatory contexts and with only moderate computational resources. We show that the identified DCDs correlate well with observed changes in gene expression and can recover a substantial number of differentially expressed genes. We showcase SCIDDO’s ability to directly interrogate chromatin dynamics such as enhancer switches in downstream analysis, which simplifies exploring specific questions about regulatory changes in chromatin. By comparing SCIDDO to competing methods, we provide evidence that SCIDDO’s performance in identifying differentially expressed genes (DEG) via differential chromatin marking is more stable across a range of cell-type comparisons and parameter cut-offs. Availability The SCIDDO source code is openly available under github.com/ptrebert/sciddo Supplementary information Supplementary data are available at Bioinformatics online.

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PathScore: a web tool for identifying altered pathways in cancer data

10.1101/067090 ◽

2016 ◽

Cited By ~ 2

Author(s):

Stephen G. Gaffney ◽

Jeffrey P. Townsend

Keyword(s):

Web Application ◽

Somatic Mutations ◽

Supplementary Information ◽

Web Tool ◽

Cancer Data ◽

Link Type ◽

Novel Approach ◽

Supplementary Material ◽

User Friendly ◽

Pathway Effect

ABSTRACTSummaryPathScore quantifies the level of enrichment of somatic mutations within curated pathways, applying a novel approach that identifies pathways enriched across patients. The application provides several user-friendly, interactive graphic interfaces for data exploration, including tools for comparing pathway effect sizes, significance, gene-set overlap and enrichment differences between projects.Availability and ImplementationWeb application available at pathscore.publichealth.yale.edu. Site implemented in Python and MySQL, with all major browsers supported. Source code available at github.com/sggaffney/pathscore with a GPLv3 [email protected] InformationAdditional documentation can be found at http://pathscore.publichealth.yale.edu/faq.

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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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Genome-wide analysis of differentially expressed genes and the modulation of PEDV infection in Vero E6 cells

Microbial Pathogenesis ◽

10.1016/j.micpath.2018.02.004 ◽

2018 ◽

Vol 117 ◽

pp. 247-254 ◽

Cited By ~ 8

Author(s):

Hewei Zhang ◽

Qinfang Liu ◽

Weiwei Su ◽

Jianke Wang ◽

Yaru Sun ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Differentially Expressed ◽

Genome Wide Analysis ◽

Genome Wide

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YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells

Bioinformatics ◽

10.1093/bioinformatics/btz402 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4525-4527 ◽

Cited By ~ 10

Author(s):

Alex X Lu ◽

Taraneh Zarin ◽

Ian S Hsu ◽

Alan M Moses

Keyword(s):

Image Analysis ◽

Web Application ◽

Single Cells ◽

Yeast Cells ◽

Supplementary Information ◽

Supplementary Data ◽

User Friendly ◽

Microscopy Images

Abstract Summary We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. Availability and implementation YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. Supplementary information Supplementary data are available at Bioinformatics online.

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DiscoRhythm: an easy-to-use web application and R package for discovering rhythmicity

Bioinformatics ◽

10.1093/bioinformatics/btz834 ◽

2019 ◽

Cited By ~ 2

Author(s):

Matthew Carlucci ◽

Algimantas Kriščiūnas ◽

Haohan Li ◽

Povilas Gibas ◽

Karolis Koncevičius ◽

...

Keyword(s):

Web Application ◽

Statistical Significance ◽

R Package ◽

Biological Data ◽

Supplementary Information ◽

Statistical Knowledge ◽

Health And Disease ◽

Phase Amplitude ◽

Almost All ◽

User Friendly

Abstract Motivation Biological rhythmicity is fundamental to almost all organisms on Earth and plays a key role in health and disease. Identification of oscillating signals could lead to novel biological insights, yet its investigation is impeded by the extensive computational and statistical knowledge required to perform such analysis. Results To address this issue, we present DiscoRhythm (Discovering Rhythmicity), a user-friendly application for characterizing rhythmicity in temporal biological data. DiscoRhythm is available as a web application or an R/Bioconductor package for estimating phase, amplitude, and statistical significance using four popular approaches to rhythm detection (Cosinor, JTK Cycle, ARSER, and Lomb-Scargle). We optimized these algorithms for speed, improving their execution times up to 30-fold to enable rapid analysis of -omic-scale datasets in real-time. Informative visualizations, interactive modules for quality control, dimensionality reduction, periodicity profiling, and incorporation of experimental replicates make DiscoRhythm a thorough toolkit for analyzing rhythmicity. Availability and Implementation The DiscoRhythm R package is available on Bioconductor (https://bioconductor.org/packages/DiscoRhythm), with source code available on GitHub (https://github.com/matthewcarlucci/DiscoRhythm) under a GPL-3 license. The web application is securely deployed over HTTPS (https://disco.camh.ca) and is freely available for use worldwide. Local instances of the DiscoRhythm web application can be created using the R package or by deploying the publicly available Docker container (https://hub.docker.com/r/mcarlucci/discorhythm). Supplementary information Supplementary data are available at Bioinformatics online.

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