For example, DNA, RNA and protein sequence data is frequently well beyond the scope of traditional data analysis. Fortunately, big data analytical techniques can be used to gather highly precise insights from these data sets.
For example, machine learning technologies allow researchers to “train” artificial intelligence systems to analyze and group biological data in interesting and informative ways. This can potentially result in analyses that the researchers themselves would not have considered before. However, this process requires significant computing power, which would not be possible without a seamless and robust IT setup. Furthermore, it is often impractical to house such systems using on-premises hardware.
To help deliver better technology to research teams, cloud computing has been adopted as a major part of the bioinformatics ecosystem. Using the cloud, teams can access computing resources exactly when they need them with no restrictions on where they work (thus, labs can be established without needing to think about where to house all the necessary IT resources). Additionally, the cloud can help teams scale their experiments with minimal lead time. This results in better and faster outcomes from both a research and business perspective.