Wekemo Bioincloud: A user-friendly platform for meta-omics data analyses

Abstract

The increasing application of meta-omics approaches to investigate the structure, function, and intercellular interactions of microbial communities has led to a surge in available data. However, this abundance of human and environmental microbiome data has exposed new scalability challenges for existing bioinformatics tools. In response, we introduce Wekemo Bioincloud-a specialized platform for -omics studies. This platform offers a comprehensive analysis solution, specifically designed to alleviate the challenges of tool selection for users in the face of expanding data sets. As of now, Wekemo Bioincloud has been regularly equipped with 22 workflows and 65 visualization tools, establishing itself as a user-friendly and widely embraced platform for studying diverse data sets. Additionally, the platform enables the online modification of vector outputs, and the registration-independent personalized dashboard system ensures privacy and traceability. Wekemo Bioincloud is freely available at https://www.bioincloud.tech/.

Keywords: Wekemo Bioincloud; bioinformatics; meta‐omics; microbiome; user‐friendly platform.

Figure 1

The framework of 22 analysis workflows for Wekemo Bioincloud. The platform now provides diverse workflows for meta‐omics data and accommodates both standardized and personalized analyses for various research. CDS, coding sequences; mRNA, messenger RNA; rRNA, ribosomal RNA; TMT, tandem mass tag.

Figure 2

Example outputs generated by Wekemo Bioincloud Tools. The tools now contain 65 subfunctions, serving various purposes, including (1) the presentation of contribution, richness, composition of features or genes; (2) group comparison; (3) analysis of differences in data structure; (4) statistical analyses, such as analysis of variance tests, Kruskal–Wallis tests, and so forth; (5) enrichment of functional or metabolic pathways; (6) identification of differentially expressed genes; (7) construction of phylogenetic relationship; (8) correlation tests; (9) visualization pipelines, such as amplicon sequencing pipeline, metagenome taxonomy annotation pipeline, and so forth; (10) others, such as primer design, and so forth.

Figure 3

Metagenomic data analyses using Wekemo Bioincloud platform. The clean data could be analyzed with three different workflows, the reference‐based, the de novo, and the binning. The main software and visualization during assembly, binning, taxonomic analyses, and functional analyses were presented, making it suitable for various purposes or scenarios. The functional analyses include 12 diversity databases. ARDB, Antibiotic Resistance Genes Database; BacMet, Antibacterial Biocide and Metal Resistance Genes; CARD, Comprehensive Antibiotic Resistance Database; CAZy, Carbohydrate‐Active EnZymes; COGs, Clusters of Orthologous Groups of proteins; EC, Enzyme Commission; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MetaCyc, Metabolic Pathway; MGE, Mobile Genetic Element; QS, Quorum Sensing; VFDB, Virulence Factors Database.

Figure 4

Metabolomic data analyses using the Wekemo Bioincloud platform. After employing three ways (nontargeted screening, targeted screening with high throughput and targeted screening) for compound detection, data preprocessing, statistical analyses, feature selection, and functional analyses are implemented in three workflows. Nontargeted screening provides the highest metabolite coverage, whereas targeted screening yields the lowest coverage. OPLS‐DA, orthogonal partial least‐squares discriminant analysis; PLS‐DA, partial least‐squares discriminant analysis.