MSA are completed where homologous sequences are compared in order to perform phylogenetic reconstruction, protein secondary and tertiary structure analysis, and protein function prediction analysis. Multiple sequence alignments (MSA) are an essential and widely used computational procedure for biological sequence analysis in molecular biology, computational biology, and bioinformatics. Cloud computing technologies and concepts are outlined, and the next generation of cloud base MSA algorithms is introduced. In this review, multiple sequence alignments are discussed, with a specific focus on the ClustalW and Clustal Omega algorithms. Combining MSA algorithms with cloud computing technologies is therefore likely to improve the speed, quality, and capability for MSA to handle large numbers of sequences. In order to realise the promise of MSA for large-scale sequence data sets, it is necessary for existing MSA algorithms to be run in a parallelised fashion with the sequence data distributed over a computing cluster or server farm. MSA of ever-increasing sequence data sets is becoming a significant bottleneck. Next-generation sequencing technologies are changing the biology landscape, flooding the databases with massive amounts of raw sequence data.

Multiple sequence alignment (MSA) of DNA, RNA, and protein sequences is one of the most essential techniques in the fields of molecular biology, computational biology, and bioinformatics.

Clustal 2