MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies

AbstractAnalysis of large-scale omics data along with biomedical images has gaining a huge interest in predicting phenotypic conditions towards personalized medicine. Multiple layers of investigations such as genomics, transcriptomics and proteomics, have led to high dimensionality and heterogeneity of data. Multi-omics data integration can provide meaningful contribution to early diagnosis and an accurate estimate of prognosis and treatment in cancer. Some multi-layer data structures have been developed to integrate multi-omics biological information, but none of these has been developed and evaluated to include radiomic data. We proposed to use MultiAssayExperiment (MAE) as an integrated data structure to combine multi-omics data facilitating the exploration of heterogeneous data. We improved the usability of the MAE, developing a Multi-omics Statistical Approaches (MuSA) tool that uses a Shiny graphical user interface, able to simplify the management and the analysis of radiogenomic datasets. The capabilities of MuSA were shown using public breast cancer datasets from TCGA-TCIA databases. MuSA architecture is modular and can be divided in Pre-processing and Downstream analysis. The pre-processing section allows data filtering and normalization. The downstream analysis section contains modules for data science such as correlation, clustering (i.e., heatmap) and feature selection methods. The results are dynamically shown in MuSA. MuSA tool provides an easy-to-use way to create, manage and analyze radiogenomic data. The application is specifically designed to guide no-programmer researchers through different computational steps. Integration analysis is implemented in a modular structure, making MuSA an easily expansible open-source software.

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A New Graph Database System for Multi-omics Data Integration and Mining Complex Biological Information

Computational Advances in Bio and Medical Sciences - Lecture Notes in Computer Science ◽

10.1007/978-3-030-46165-2_14 ◽

2020 ◽

pp. 171-183

Author(s):

Ishwor Thapa ◽

Hesham Ali

Keyword(s):

Data Integration ◽

Database System ◽

Biological Information ◽

Omics Data ◽

Graph Database ◽

Mining Complex ◽

Omics Data Integration

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multiomics: A user-friendly multi-omics data harmonisation R pipeline

F1000Research ◽

10.12688/f1000research.53453.1 ◽

2021 ◽

Vol 10 ◽

pp. 538

Author(s):

Tyrone Chen ◽

Al J Abadi ◽

Kim-Anh Lê Cao ◽

Sonika Tyagi

Keyword(s):

Data Integration ◽

Case Studies ◽

R Package ◽

Heterogeneous Data ◽

General Purpose ◽

Omics Data ◽

Experimental Conditions ◽

Seamless Integration ◽

Data Object ◽

Omics Data Integration

Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.

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TaxonTableTools - A comprehensive, platform-independent graphical user interface software to explore and visualise DNA metabarcoding data

10.1101/2020.08.24.264317 ◽

2020 ◽

Author(s):

Till-Hendrik Macher ◽

Arne J. Beermann ◽

Florian Leese

Keyword(s):

User Interface ◽

Graphical User Interface ◽

Biological Information ◽

Analysis Software ◽

Rarefaction Analysis ◽

Link Type ◽

Dna Metabarcoding ◽

Traditional Taxonomy ◽

Reproducible Analysis ◽

Downstream Analysis

AbstractDNA metabarcoding is increasingly used in research and application to assess biodiversity. Powerful analysis software exists to process raw data. However, when it comes to the translation of sequence read data into biological information many end users with limited bioinformatic expertise struggle with the downstream analysis and explore data only to a minor extent. Thus, there is a growing need for easy-to-use, graphical user interface (GUI) analysis software to analyse and visualise DNA metabarcoding data. We here present TaxonTableTools (TTT), a new platform independent GUI software that aims to fill this gap by providing simple and reproducible analysis and visualisation workflows. TTT uses a so-called “TaXon table” as input. This format can easily be generated within TTT from two input files: a read table and a taxonomy table that can be obtained by various published metabarcoding pipelines. TTT analysis and visualisation modules include e.g. Venn diagrams to compare taxon overlap among replicates, samples or among different analysis methods. It analyses and visualises basic statistics such as read proportion per taxon as well as more sophisticated visualisation such as interactive Krona charts for taxonomic data exploration. Various ecological analyses such as alpha or beta diversity estimates, and rarefaction analysis ordination plots can be produced directly. Data can be explored also in formats required by traditional taxonomy-based analyses of regulatory bioassessment programs. TTT comes with a manual and tutorial, is free and publicly available through GitHub (https://github.com/TillMacher/TaxonTableTools) and the Python package index (https://pypi.org/project/taxontabletools/).

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