Multi-Omics Data Mining Techniques: Algorithms and Software

Abstract

AbstractWith the aid of cost-effective next-generation sequencing technologies, the datasets with multiple dimensions, called multi-omics or integrated omics, have been dramatically accumulated. Because of the limitation of application individual omics, multi-omics efforts have been playing the lead in bioinformatics and biomedical research—from simple computation to data mining. As multi-omics, a merger of biology, informatics, data science and computational sciences, has incredible high complexity, the multi-omics data mining techniques are indigestible to researchers new to this field. The present review is to provide an overview of the current state of the field. On the one hand, we do our best to summarize the algorithms and software designed for the horizontal or vertical integration of the omics data from the various high-throughput sequencing platforms. For each method, we give a complete survey on software and their algorithms that are frequently used coupled with a brief discussion about the principles for applying these computational strategies and considerations, especially in cancer research. On the other hand, we also give a summary of the user-friendly tools suited for multi-omics data interpretation, analysis, and visualization. To our knowledge, this is the most complete and updated summary of publicly available resources about multi-omics data mining. We hope the readers can get inspiration here for their own multi-omics data analysis.