AbstractThe rapid advancements of high-throughput “omics” technologies have brought huge amount of data to process during and after experiments. Multi-omic analysis facilitates a deeper interrogation of a dataset, and discovery of interesting genes, proteins, lipids, glycans, or metabolites, or pathways related to the corresponding phenotypes in a study. Many individual software tools have been developed to analyze and visualize the data. However, integrating multiple omics data analysis strategies and approaches in a single data processing pipeline is still a challenge task. OmicsOne is a software developed in R, Python and Jupyter Notebook that can achieve statistical analysis, machine learning, and data visualization on multi-‘omics’ data by taking the advantages of integrating the useful tools from individual software packages. OmicsOne can simplify “omics” data analysis, and delineate molecules, or pathways associated to interested phenotypes.
Computational prediction of inter-species relationships through omics data analysis and machine learning
