scholarly journals Intervene: a tool for intersection and visualization of multiple gene or genomic region sets

2017 ◽  
Author(s):  
Aziz Khan ◽  
Anthony Mathelier
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Genomic Region ◽  
Web Interface ◽  
Multiple Gene ◽  
Link Type ◽  
Common Task ◽  
Computer Scientists ◽  
Genomic Regions ◽  
Publication Quality

AbstractBackgroundA common task for scientists relies on comparing lists of genes or genomic regions derived from high-throughput sequencing experiments. While several tools exist to intersect and visualize sets of genes, similar tools dedicated to the visualization of genomic region sets are currently limited.ResultsTo address this gap, we have developed the Intervene tool, which provides an easy and automated interface for the effective intersection and visualization of genomic region or list sets, thus facilitating their analysis and interpretation. Intervene contains three modules: venn to generate Venn diagrams of up to six sets, upset to generate UpSet plots of multiple sets, and pairwise to compute and visualize intersections of multiple sets as clustered heat maps. Intervene, and its interactive web ShinyApp companion, generate publication-quality figures for the interpretation of genomic region and list sets.ConclusionsIntervene and its web application companion provide an easy command line, and an interactive web interface to compute intersections of multiple genomic and list sets. They also have the capacity to plot intersections using easy-to-interpret visual approaches. Intervene is developed and designed to meet the needs of both computer scientists and biologists. The source code is freely available at https://bitbucket.org/CBGR/intervene, with the web application available at https://asntech.shinyapps.io/intervene.

scholarly journals re-Searcher: GUI-based bioinformatics tool for simplified genomics data mining of VCF files

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11333
Author(s):  
Daniyar Karabayev ◽  
Askhat Molkenov ◽  
Kaiyrgali Yerulanuly ◽  
Ilyas Kabimoldayev ◽  
Asset Daniyarov ◽  
...  
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Data Types ◽  
Standard Format ◽  
Standard Data ◽  
Additional Information ◽  
Link Type ◽  
Sequencing Platforms ◽  
User Friendly

Background High-throughput sequencing platforms generate a massive amount of high-dimensional genomic datasets that are available for analysis. Modern and user-friendly bioinformatics tools for analysis and interpretation of genomics data becomes essential during the analysis of sequencing data. Different standard data types and file formats have been developed to store and analyze sequence and genomics data. Variant Call Format (VCF) is the most widespread genomics file type and standard format containing genomic information and variants of sequenced samples. Results Existing tools for processing VCF files don’t usually have an intuitive graphical interface, but instead have just a command-line interface that may be challenging to use for the broader biomedical community interested in genomics data analysis. re-Searcher solves this problem by pre-processing VCF files by chunks to not load RAM of computer. The tool can be used as standalone user-friendly multiplatform GUI application as well as web application (https://nla-lbsb.nu.edu.kz). The software including source code as well as tested VCF files and additional information are publicly available on the GitHub repository (https://github.com/LabBandSB/re-Searcher).

scholarly journals CoralP: Flexible visualization of the human phosphatome

2019 ◽  
Author(s):  
Amit Min ◽  
Erika Deoudes ◽  
Marielle L. Bond ◽  
Eric S. Davis ◽  
Douglas H. Phanstiel
Keyword(s):  
Web Application ◽  
Protein Phosphatases ◽  
Source Code ◽  
Protein Family ◽  
Biological Processes ◽  
Important Class ◽  
Pharmaceutical Development ◽  
Link Type ◽  
Phosphoryl Groups ◽  
Publication Quality

Protein phosphatases and kinases play critical roles in a host of biological processes and diseases via the removal and addition of phosphoryl groups. While kinases have been extensively studied for decades, recent findings regarding the specificity and activities of phosphatases have generated an increased interest in targeting phosphatases for pharmaceutical development. This increased focus has created a need for methods to visualize this important class of proteins within the context of the entire phosphatase protein family. Here, we present CoralP, an interactive web application for the generation of customizable, publication-quality representations of human phosphatome data. Phosphatase attributes can be encoded through edge colors, node colors, and node sizes. CoralP is the first and currently the only tool designed for phosphatome visualization and should be of great use to the signaling community. The source code and web application are available at https://github.com/PhanstielLab/coralp and http://phanstiel-lab.med.unc.edu/coralp respectively.

scholarly journals riboviz: analysis and visualization of ribosome profiling datasets

2017 ◽  
Author(s):  
Oana Carja ◽  
Tongji Xing ◽  
Joshua B. Plotkin ◽  
Premal Shah
Keyword(s):  
Web Application ◽  
High Throughput Sequencing ◽  
Ribosome Profiling ◽  
Analysis Pipeline ◽  
Processing Pipeline ◽  
Link Type ◽  
A Cell ◽  
Biological Discovery ◽  
Public Datasets

AbstractUsing high-throughput sequencing to monitor translation in vivo, ribosome profiling can provide critical insights into the dynamics and regulation of protein synthesis in a cell. Since its introduction in 2009, this technique has played a key role in driving biological discovery, and yet it requires a rigorous computational toolkit for widespread adoption. We developed a processing pipeline and browser-based visualization, riboviz, that allows convenient exploration and analysis of riboseq datasets. In implementation, riboviz consists of a comprehensive and flexible backend analysis pipeline that allows the user to analyze their private unpublished dataset, along with a web application for comparison with previously published public datasets.Availability and implementationJavaScript and R source code and extra documentation are freely available from https://github.com/shahpr/RiboViz, while the web-application is live at www.riboviz.org.

scholarly journals Heat*seq: an interactive web tool for high-throughput sequencing experiment comparison with public data

2016 ◽  
Author(s):  
Guillaume Devailly ◽  
Anna Mantsoki ◽  
Anagha Joshi
Keyword(s):  
High Throughput ◽  
Web Application ◽  
High Throughput Sequencing ◽  
Single Gene ◽  
Public Domain ◽  
Web Tool ◽  
The Public ◽  
Link Type ◽  
A Genome ◽  
Public Data

SummaryBetter protocols and decreasing costs have made high-throughput sequencing experiments now accessible even to small experimental laboratories. However, comparing one or few experiments generated by an individual lab to the vast amount of relevant data freely available in the public domain might be limited due to lack of bioinformatics expertise. Though several tools, including genome browsers, allow such comparison at a single gene level, they do not provide a genome-wide view. We developed Heat*seq, a web-tool that allows genome scale comparison of high throughput experiments (ChIP-seq, RNA-seq and CAGE) provided by a user, to the data in the public domain. Heat*seq currently contains over 12,000 experiments across diverse tissue and cell types in human, mouse and drosophila. Heat*seq displays interactive correlation heatmaps, with an ability to dynamically subset datasets to contextualise user experiments. High quality figures and tables are produced and can be downloaded in multiple formats.AvailabilityWeb application:www.heatstarseq.roslin.ed.ac.uk/. Source code:https://github.com/[email protected];[email protected]

scholarly journals Correlation AnalyzeR: functional predictions from gene co-expression correlations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Henry E. Miller ◽  
Alexander J. R. Bishop
Keyword(s):  
Web Application ◽  
Bone Cancer ◽  
R Package ◽  
Limited Range ◽  
Web Interface ◽  
Generating Functional ◽  
Link Type ◽  
Genome Wide ◽  
User Friendly

Abstract Background Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills. Results We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene–gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses. Conclusions Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR.

scholarly journals GenFam: A web application and database for gene family-based classification and functional enrichment analysis

2018 ◽  
Author(s):  
Renesh Bedre ◽  
Kranthi Mandadi
Keyword(s):  
Gene Family ◽  
Web Application ◽  
High Throughput Sequencing ◽  
Source Code ◽  
Enrichment Analysis ◽  
Gene Families ◽  
Functional Enrichment ◽  
Multiple Testing Correction ◽  
Web Based ◽  
Link Type

ABSTRACTGenome-scale studies using high-throughput sequencing (HTS) technologies generate substantial lists of differentially expressed genes under different experimental conditions. These gene lists need to be further mined to narrow down biologically relevant genes and associated functions in order to guide downstream functional genetic analyses. A popular approach is to determine statistically overrepresented genes in a user-defined list through enrichment analysis tools, which rely on functional annotations of genes based on Gene Ontology (GO) terms. Here, we propose a new approach, GenFam, which allows classification and enrichment of genes based on their gene family, thus simplifying identification of candidate gene families and associated genes that may be relevant to the query. GenFam and its integrated database comprises of three-hundred and eighty-four unique gene families and supports gene family classification and enrichment analyses for sixty plant genomes. Four comparative case studies with plant species belonging to different clades and families were performed using GenFam which demonstrated its robustness and comprehensiveness over preexisting functional enrichment tools. To make it readily accessible for plant biologists, GenFam is available as a web-based application where users can input gene IDs and export enrichment results in both tabular and graphical formats. Users can also customize analysis parameters by choosing from the various statistical enrichment tests and multiple testing correction methods. Additionally, the web-based application, source code and database are freely available to use and download. Website: http://mandadilab.webfactional.com/home/. Source code and database: http://mandadilab.webfactional.com/home/dload/.

scholarly journals A decoupled, modular and scriptable architecture for tools to curate data platforms

2020 ◽  
Author(s):  
Moritz Langenstein ◽  
Henning Hermjakob ◽  
Manuel Bernal Llinares
Keyword(s):  
Web Application ◽  
Production Systems ◽  
Source Code ◽  
Black Box ◽  
Command Line ◽  
Web Interface ◽  
Link Type ◽  
Data Platform ◽  
The Web

AbstractMotivationCuration is essential for any data platform to maintain the quality of the data it provides. Existing databases, which require maintenance, and the amount of newly published information that needs to be surveyed, are growing rapidly. More efficient curation is often vital to keep up with this growth, requiring modern curation tools. However, curation interfaces are often complex and difficult to further develop. Furthermore, opportunities for experimentation with curation workflows may be lost due to a lack of development resources, or a reluctance to change sensitive production systems.ResultsWe propose a decoupled, modular and scriptable architecture to build curation tools on top of existing platforms. Instead of modifying the existing infrastructure, our architecture treats the existing platform as a black box and relies only on its public APIs and web application. As a decoupled program, the tool’s architecture gives more freedom to developers and curators. This added flexibility allows for quickly prototyping new curation workflows as well as adding all kinds of analysis around the data platform. The tool can also streamline and enhance the curator’s interaction with the web interface of the platform. We have implemented this design in cmd-iaso, a command-line curation tool for the identifiers.org registry.AvailabilityThe cmd-iaso curation tool is implemented in Python 3.7+ and supports Linux, macOS and Windows. Its source code and documentation are freely available from https://github.com/identifiers-org/cmd-iaso. It is also published as a Docker container at https://hub.docker.com/r/identifiersorg/[email protected]

scholarly journals iSeqQC: A Tool for Expression-Based Quality Control in RNA Sequencing

2019 ◽  
Author(s):  
Gaurav Kumar ◽  
Adam Ertel ◽  
George Feldman ◽  
Joan Kupper ◽  
Paolo Fortina
Keyword(s):  
Quality Control ◽  
High Throughput Sequencing ◽  
Correlation Coefficients ◽  
Batch Effects ◽  
Web Interface ◽  
Link Type ◽  
Phenotypic Group ◽  
Statistical Approaches ◽  
Fast Light ◽  
Insight Into

ABSTRACTQuality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise the data. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers or batch effects. Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced by batch effects due to laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized either through command-line (Github: https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.

scholarly journals AB0210 ACREULAR: AN R PACKAGE FOR THE CALCULATION AND VISUALISATION OF ACR/EULAR RELATED RHEUMATOID ARTHRITIS MEASURES

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1405.1-1406
Author(s):  
F. Morton ◽  
J. Nijjar ◽  
C. Goodyear ◽  
D. Porter
Keyword(s):  
Rheumatoid Arthritis ◽  
Functional Status ◽  
Rheumatic Diseases ◽  
Web Application ◽  
R Package ◽  
Diagnostic Classification ◽  
Microsoft Excel ◽  
Link Type ◽  
Large Joint ◽  
Programming Skills

Background:The American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) individually and collaboratively have produced/recommended diagnostic classification, response and functional status criteria for a range of different rheumatic diseases. While there are a number of different resources available for performing these calculations individually, currently there are no tools available that we are aware of to easily calculate these values for whole patient cohorts.Objectives:To develop a new software tool, which will enable both data analysts and also researchers and clinicians without programming skills to calculate ACR/EULAR related measures for a number of different rheumatic diseases.Methods:Criteria that had been developed by ACR and/or EULAR that had been approved for the diagnostic classification, measurement of treatment response and functional status in patients with rheumatoid arthritis were identified. Methods were created using the R programming language to allow the calculation of these criteria, which were incorporated into an R package. Additionally, an R/Shiny web application was developed to enable the calculations to be performed via a web browser using data presented as CSV or Microsoft Excel files.Results:acreular is a freely available, open source R package (downloadable fromhttps://github.com/fragla/acreular) that facilitates the calculation of ACR/EULAR related RA measures for whole patient cohorts. Measures, such as the ACR/EULAR (2010) RA classification criteria, can be determined using precalculated values for each component (small/large joint counts, duration in days, normal/abnormal acute-phase reactants, negative/low/high serology classification) or by providing “raw” data (small/large joint counts, onset/assessment dates, ESR/CRP and CCP/RF laboratory values). Other measures, including EULAR response and ACR20/50/70 response, can also be calculated by providing the required information. The accompanying web application is included as part of the R package but is also externally hosted athttps://fragla.shinyapps.io/shiny-acreular. This enables researchers and clinicians without any programming skills to easily calculate these measures by uploading either a Microsoft Excel or CSV file containing their data. Furthermore, the web application allows the incorporation of additional study covariates, enabling the automatic calculation of multigroup comparative statistics and the visualisation of the data through a number of different plots, both of which can be downloaded.Figure 1.The Data tab following the upload of data. Criteria are calculated by the selecting the appropriate checkbox.Figure 2.A density plot of DAS28 scores grouped by ACR/EULAR 2010 RA classification. Statistical analysis has been performed and shows a significant difference in DAS28 score between the two groups.Conclusion:The acreular R package facilitates the easy calculation of ACR/EULAR RA related disease measures for whole patient cohorts. Calculations can be performed either from within R or by using the accompanying web application, which also enables the graphical visualisation of data and the calculation of comparative statistics. We plan to further develop the package by adding additional RA related criteria and by adding ACR/EULAR related measures for other rheumatic disorders.Disclosure of Interests:Fraser Morton: None declared, Jagtar Nijjar Shareholder of: GlaxoSmithKline plc, Consultant of: Janssen Pharmaceuticals UK, Employee of: GlaxoSmithKline plc, Paid instructor for: Janssen Pharmaceuticals UK, Speakers bureau: Janssen Pharmaceuticals UK, AbbVie, Carl Goodyear: None declared, Duncan Porter: None declared

scholarly journals 3D reconstruction of genomic regions from sparse interaction data

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Julen Mendieta-Esteban ◽  
Marco Di Stefano ◽  
David Castillo ◽  
Irene Farabella ◽  
Marc A Marti-Renom
Keyword(s):  
3D Models ◽  
Genomic Region ◽  
Gene Promoters ◽  
Chromosome Conformation ◽  
The Matrix ◽  
Chromatin Structural ◽  
A Cell ◽  
Similar Accuracy ◽  
Genomic Regions ◽  
Interaction Matrices

Abstract Chromosome conformation capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such 3C technologies is Promoter Capture Hi-C (pcHi-C) that is tailored to probe only interactions involving gene promoters. As such, pcHi-C provides sparse interaction matrices that are suitable to characterize short- and long-range enhancer–promoter interactions. Here, we introduce a new method to reconstruct the chromatin structural (3D) organization from sparse 3C-based datasets such as pcHi-C. Our method allows for data normalization, detection of significant interactions and reconstruction of the full 3D organization of the genomic region despite of the data sparseness. Specifically, it builds, with as low as the 2–3% of the data from the matrix, reliable 3D models of similar accuracy of those based on dense interaction matrices. Furthermore, the method is sensitive enough to detect cell-type-specific 3D organizational features such as the formation of different networks of active gene communities.

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