GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components

López-Fernández, Hugo; Reboiro-Jato, Miguel; Glez-Peña, Daniel; Laza, Rosalía; Pavón, Reyes; Fdez-Riverola, Florentino

doi:10.1371/journal.pone.0204474

Cited by 6 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SEDA is implemented in Java 8 using the GC4S library for GUI development [ 12 ] and a custom framework for CLI development.…”

Section: Methodsmentioning

confidence: 99%

SEDA 2024 update: enhancing the SEquence DAtaset builder for seamless integration into automated data analysis pipelines

Reboiro-Jato,

Pérez-Rodríguez,

Da Silva

et al. 2024

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background The initial version of SEDA assists life science researchers without programming skills with the preparation of DNA and protein sequence FASTA files for multiple bioinformatics applications. However, the initial version of SEDA lacks a command-line interface for more advanced users and does not allow the creation of automated analysis pipelines. Results The present paper discusses the updates of the new SEDA release, including the addition of a complete command-line interface, new functionalities like gene annotation, a framework for automated pipelines, and improved integration in Linux environments. Conclusion SEDA is an open-source Java application and can be installed using the different distributions available (https://www.sing-group.org/seda/download.html) as well as through a Docker image (https://hub.docker.com/r/pegi3s/seda). It is released under a GPL-3.0 license, and its source code is publicly accessible on GitHub (https://github.com/sing-group/seda). The software version at the time of submission is archived at Zenodo (version v1.6.0, http://doi.org/10.5281/zenodo.10201605).

show abstract

“…SEDA is implemented in Java 8 using the GC4S library for GUI development [ 12 ] and a custom framework for CLI development.…”

Section: Methodsmentioning

confidence: 99%

SEDA 2024 update: enhancing the SEquence DAtaset builder for seamless integration into automated data analysis pipelines

Reboiro-Jato,

Pérez-Rodríguez,

Da Silva

et al. 2024

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because they are written in R (18)(19)(20), these tools must rely on R's somewhat limited capabilities for interactive applications. In contrast to R, Java, MOG's platform, has been used to develop numerous software with interfaces that are interactive and userfriendly (102,(130)(131)(132)(133), and MOG provides the researcher with specialized GUIs and methods for exploratory data analysis. MOG's GUI allows direct interactivity with the data and the visualizations, so that a researcher can easily explore data from different perspectives.…”

Section: K Stage-wise Analysis Of Cancer Datamentioning

confidence: 99%

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

Singh

Hur

Dorman

et al. 2019

Preprint

View full text Add to dashboard Cite

The diverse and growing omics data in public domains provide researchers with a tremendous opportunity to extract hidden knowledge. However, the challenge of providing domain experts with easy access to these big data has resulted in the vast majority of archived data remaining unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory data analysis of massive datasets by scientific researchers. Using MOG, a researcher can interactively visualize and statistically analyze the data, in the context of its metadata. Researchers can interactively hone-in on groups of experiments or genes based on attributes such as expression values, statistical results, metadata terms, and ontology annotations. MOG's statistical tools include coexpression, differential expression, and differential correlation analysis, with permutation test-based options for significance assessments. Multithreading and indexing enable efficient data analysis on a personal computer, with no need for writing code. Data can be visualized as line charts, box plots, scatter plots, and volcano plots. A researcher can create new MOG projects from any data or analyze an existing one. An R-wrapper lets a researcher select and send smaller data subsets to R for additional analyses. A researcher can save MOG projects with a history of the exploratory progress and later reopen or share them. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, in which we assembled a list of novel putative biomarker genes in different tumors, and microarray and metabolomics from A. thaliana.Singh et al. | bioRχiv | July 11, 2019 | 1-18 in complex biological networks (6,8,(24)(25)(26)(27)(28). Here, we present MetaOmGraph (MOG), a software written in Java, to interactively explore and visualize large datasets. MOG overcomes the challenges posed by the size and complexity of big datasets by efficient handling of the data files, using a combination of data indexing and buffering schemes. Further, by incorporating metadata, MOG adds extra dimensions to the analyses and provides flexibility in data exploration. Researchers can explore their own data on their local machines. At any stage of the analysis, a researcher can save her/his progress. Saved MOG projects can be shared, reused, and included in publications. MOG is user-centered software, designed for all types of data, but specialized for expression data. MATERIALS AND METHODSA. Overview. MOG is a standalone program that can run on any operating system capable of running Java (Linux, Mac and Windows). Access to MOG easy. MOG runs on the researcher's computer and thus the researcher does not need to rely on internet accessibility, and is not slowed down by the latency of transfer of big data. Furthermore, the data in a researcher's project is secure, remaining on the researcher's computer, particularly important for confidential data such as human RNA-Seq. MOG's modular structure has been carefully designed to enable developers to easily implement new s...

show abstract

“…A Java interface was developed to run each software using the so-called binaries executor, which acts as a gateway between DEWE and those external applications. The gui module, which was created in Java Swing using the freely available extensions SwingX and GC4S [37] (https://www.sing-group.org/gc4s/ ), provides GUI components to display results and collect user inputs. The JSparklines library was used for enhanced data table visualisation [38].…”

Section: Dewe Implementationmentioning

confidence: 99%

DEWE: A novel tool for executing differential expression RNA-Seq workflows in biomedical research

López-Fernández

Blanco-Míguez

Fdez-Riverola

et al. 2019

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

Background: Transcriptomics profiling aims to identify and quantify all transcripts present within a cell type or tissue at a particular state, and thus provide information on the genes expressed in specific experimental settings, differentiation or disease conditions. RNA-Seq technology is becoming the standard approach for such studies, but available analysis tools are often hard to install, configure and use by users without advanced bioinformatics skills. Methods: Within reason, DEWE aims to make RNA-Seq analysis as easy for non-proficient users as for experienced bioinformaticians. DEWE supports two well-established and widely used differential expression analysis workflows: using Bowtie2 or HISAT2 for sequence alignment; and, both applying StringTie for quantification, and Ballgown and edgeR for differential expression analysis. Also, it enables the tailored execution of individual tools as well as helps with the management and visualisation of differential expression results. Results: DEWE provides a user-friendly interface designed to reduce the learning curve of less knowledgeable users while enabling analysis customisation and software extension by advanced users. Docker technology helps overcome installation and configuration hurdles. In addition, DEWE produces high quality and publication-ready outputs in the form of tab-delimited files and figures, as well as helps researchers with further analyses, such as pathway enrichment analysis. Conclusions: The abilities of DEWE are exemplified here by practical application to a comparative analysis of monocytes and monocyte-derived dendritic cells, a study of clinical relevance. DEWE installers and documentation are freely available at https://www.sing-group.org/dewe.

show abstract

GC4S: A bioinformatics-oriented Java software library of reusable graphical user interface components

Cited by 6 publications

References 31 publications

SEDA 2024 update: enhancing the SEquence DAtaset builder for seamless integration into automated data analysis pipelines

SEDA 2024 update: enhancing the SEquence DAtaset builder for seamless integration into automated data analysis pipelines

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

DEWE: A novel tool for executing differential expression RNA-Seq workflows in biomedical research

Contact Info

Product

Resources

About