Research
| Title： | Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities |
|---|---|
| First author： | Li, Heng; Shi, Mijuan; Ren, Keyi; Zhang, Lei; Ye, Weidong; Zhang, Wanting; Cheng, Yingyin; Xia, Xiao-Qin |
| Journal： | BIOINFORMATICS |
| Years： | 2023 |
| DOI： | 10.1093/bioinformatics/btac777 |
| Abstract： | With the continuous development of high-throughput sequencing technology, bioinformatic analysis of omics data plays an increasingly important role in life science research. Many R packages are widely used for omics analysis, such as DESeq2, clusterProfiler and STRINGdb. And some online tools based on them have been developed to free bench scientists from programming with these R packages. However, the charts generated by these tools are usually in a fixed, non-editable format and often fail to clearly demonstrate the details the researchers intend to express. To address these issues, we have created Visual Omics, an online tool for omics data analysis and scientific chart editing. Visual Omics integrates multiple omics analyses which include differential expression analysis, enrichment analysis, protein domain prediction and protein-protein interaction analysis with extensive graph presentations. It can also independently plot and customize basic charts that are involved in omics analysis, such as various PCA/PCoA plots, bar plots, box plots, heat maps, set intersection diagrams, bubble charts and volcano plots. A distinguishing feature of Visual Omics is that it allows users to perform one-stop omics data analyses without programming, iteratively explore the form and layout of graphs online and fine-tune parameters to generate charts that meet publication requirements. |
