Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses

•Scientific workflows are an attractive option in comparative genomics and phylogeny.•SciHmm compute-intensive genomic workflow executed in 128 cores Amazon EC2 clouds.•Muscle MSA method provided the best data quality, although other input data may point to different MSA methods.•Muscle speedup factor was 28 on 32 cores, compared to a single core computation.•Executing SciHmm before the phylogenetic analyses improved the performance up to 80%.

Over the last years, comparative genomics analyses have become more compute-intensive due to the explosive number of available genome sequences. Comparative genomics analysis is an important a prioristep for experiments in various bioinformatics domains. This analysis can be used to enhance the performance and quality of experiments in areas such as evolution and phylogeny. A common phylogenetic analysis makes extensive use of Multiple Sequence Alignment (MSA) in the construction of phylogenetic trees, which are used to infer evolutionary relationships between homologous genes. Each phylogenetic analysis aims at exploring several different MSA methods to verify which execution produces trees with the best quality. This phylogenetic exploration may run during weeks, even when executed in High Performance Computing (HPC) environments. Although there are many approaches that model and parallelize phylogenetic analysis as scientific workflows, exploring all MSA methods becomes a complex and expensive task to be performed. If scientists determine a priorithe most adequate MSA method to use in the phylogenetic analysis, it would save time, and, in some cases, financial resources. Comparative genomics analyses play an important role in optimizing phylogenetic analysis workflows. In this paper, we extend the SciHmm scientific workflow, aimed at determining the most suitable MSA method, to use it in a phylogenetic analysis. SciHmm uses SciCumulus, a cloud workflow execution engine, for parallel execution. Experimental results show that using SciHmm considerably reduces the total execution time of the phylogenetic analysis (up to 80%). Experiments also show that trees built with the MSA program elected by using SciHmm presented more quality than the remaining, as expected. In addition, the parallel execution of SciHmm shows that this kind of bioinformatics workflow has an excellent cost/benefit when executed in cloud environments.