%0 Journal Article
%T An Overview of Multiple Sequence Alignments and Cloud Computing in Bioinformatics
%A Jurate Daugelaite
%A Aisling O' Driscoll
%A Roy D. Sleator
%J ISRN Biomathematics
%D 2013
%R 10.1155/2013/615630
%X 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. Next-generation sequencing technologies are changing the biology landscape, flooding the databases with massive amounts of raw sequence data. MSA of ever-increasing sequence data sets is becoming a significant bottleneck. 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. 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 this review, multiple sequence alignments are discussed, with a specific focus on the ClustalW and Clustal Omega algorithms. Cloud computing technologies and concepts are outlined, and the next generation of cloud base MSA algorithms is introduced. 1. Introduction Multiple sequence alignments (MSA) are an essential and widely used computational procedure for biological sequence analysis in molecular biology, computational biology, and bioinformatics. 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 [1]. Biologically good and accurate alignments can have significant meaning, showing relationships and homology between different sequences, and can provide useful information, which can be used to further identify new members of protein families. The accuracy of MSA is of critical importance due to the fact that many bioinformatics techniques and procedures are dependent on MSA results [1]. Due to MSA significance, many MSA algorithms have been developed. Unfortunately, constructing accurate multiple sequence alignments is a computationally intense and biologically complex task, and as such, no current MSA tool is likely to generate a biologically perfect result. Therefore, this area of research is very active, aiming to develop a method which can align thousands of sequences that are lengthy and produce high-quality alignments and in a reasonable time [2, 3]. Alignment speed and computational complexity are negatively affected when the number of sequences to be aligned increases. The recent advances in high throughput sequencing technologies means that this sequence output is growing at an exponential rate, the
%U http://www.hindawi.com/journals/isrn.biomathematics/2013/615630/