Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the OMnalysis

Punit Tyagi; Mangesh Bhide

doi:10.7717/peerj.12415

Development of a bioinformatics platform for analysis of quantitative transcriptomics and proteomics data: the OMnalysis

PeerJ. 2021 Nov 9:9:e12415. doi: 10.7717/peerj.12415. eCollection 2021.

Authors

Punit Tyagi^{1

2}, Mangesh Bhide^{1

3}

Affiliations

¹ Laboratory of Biomedical Microbiology and Immunology, University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia.
² Department of Animal and Food Science, The Autonomous University of Barcelona, Barcelona, Spain.
³ Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.

Abstract

Background: In the past decade, RNA sequencing and mass spectrometry based quantitative approaches are being used commonly to identify the differentially expressed biomarkers in different biological conditions. Data generated from these approaches come in different sizes (e.g., count matrix, normalized list of differentially expressed biomarkers, etc.) and shapes (e.g., sequences, spectral data, etc.). The list of differentially expressed biomarkers is used for functional interpretation and retrieve biological meaning, however, it requires moderate computational skills. Thus, researchers with no programming expertise find difficulty in data interpretation. Several bioinformatics tools are available to analyze such data; however, they are less flexible for performing the multiple steps of visualization and functional interpretation.

Implementation: We developed an easy-to-use Shiny based web application (named as OMnalysis) that provides users with a single platform to analyze and visualize the differentially expressed data. The OMnalysis accepts the data in tabular form from edgeR, DESeq2, MaxQuant Perseus, R packages, and other similar software, which typically contains the list of differentially expressed genes or proteins, log of the fold change, log of the count per million, the P value, q-value, etc. The key features of the OMnalysis are multiple image type visualization and their dimension customization options, seven multiple hypothesis testing correction methods to get more significant gene ontology, network topology-based pathway analysis, and multiple databases support (KEGG, Reactome, PANTHER, biocarta, NCI-Nature Pathway Interaction Database PharmGKB and STRINGdb) for extensive pathway enrichment analysis. OMnalysis also fetches the literature information from PubMed to provide supportive evidence to the biomarkers identified in the analysis. In a nutshell, we present the OMnalysis as a well-organized user interface, supported by peer-reviewed R packages with updated databases for quick interpretation of the differential transcriptomics and proteomics data to biological meaning.

Availability: The OMnalysis codes are entirely written in R language and freely available at https://github.com/Punit201016/OMnalysis. OMnalysis can also be accessed from - http://lbmi.uvlf.sk/omnalysis.html. OMnalysis is hosted on a Shiny server at https://omnalysis.shinyapps.io/OMnalysis/. The minimum system requirements are: 4 gigabytes of RAM, i3 processor (or equivalent). It is compatible with any operating system (windows, Linux or Mac). The OMnalysis is heavily tested on Chrome web browsers; thus, Chrome is the preferred browser. OMnalysis works on Firefox and Safari.

Keywords: Bioinformatics tool; Data exploration; Functional profiling; Omics; Proteomics; RNA-seq; Shiny; Transcriptomics.

Grants and funding

This research is funded by the European Union’s Horizon 2020 Research and Innovation Programme H2020-MSCA- ITN-2017- EJD: Marie Skłodowska-Curie Innovative Training Networks (European Joint Doctorate) –Grant agreement no : 765423 – MANNA. Punit Tyagi is a doctoral fellow in the European Joint Doctorate Degree Programme (funded by MANNA -Molecular Animal Nutrition) between the University of Veterinary Medicine and Pharmacy in Košice (Slovakia) and Autonomous University of Barcelona (Spain). The example transcriptomic data were generated from the projects APVV 18-0259, VEGA 1/0439/18, and VEGA 1/0105/19. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.