Computing for bioinformatics

Computational Biology and Bioinformatics are facing a Big Data trend in these years due to many reasons: an increasing availability of biology and clinical data because biomedical studies are becoming larger and thus involve an increasing number of biological samples; an increasing volume of omics data due to the high resolution of experimental platforms such as microarray, mass spectrometry, and next generation sequencing; an increasing volume of digitalized healthcare data, such as laboratory tests, administrative data, data collected during visits to doctors, that are coupled to omics and clinical data; increasing streams of data collected by body sensors and IoT (Internet of Things) devices, that require near-real time processing. This Big Data trend poses new challenges for computing in bioinformatics related to the efficient preprocessing, analysis and integration of omics and clinical data. This article surveys main computing approaches, including parallel and distributed computing architectures, parallel programming languages, novel programming approach for distributed architectures and Cloud, that are currently used in Computational Biology and Bioinformatics.