The significant advantage of using experimental techniques such as microarray, mass spectrometry (MS), and next generation sequencing (NGS), is that they produce an overwhelming amount of experimental omics data. All of these technologies come with the challenges of determining how the raw omics data should be efficiently processed or normalized and, subsequently, how can the data adequately be summarised or integrated, in order to be stored and shared, as well as to enable machine learning and/or statistical analysis. Omics data analysis involves the execution of several steps, each one implemented through different algorithms, that demand for a lot of computation power. The main problem is the automation of the overall analysis process, to increase the throughput and to reduce manual intervention (e.g., users have to manually supervise some steps of the analysis process). In this scenario, parallel and distributed computing technologies (i.e., Message Passing Interface (MPI), GPU computing, and Hadoop Map-Reduce), are essential to speed up and automatize the whole workflow of omics data analysis. Parallel and distributed computing enable the development of bioinformatics pipeline able to achieve scalable, efficient and reliable computing performance on clusters as well as on cloud computing.