BACKGROUND: The demand for processing ever increasing amounts of genomic data has raised new challenges for the implementation of highly scalable and efficient computational systems. In this paper we propose SparkBLAST, a parallelization of a sequence alignment application (BLAST) that employs cloud computing for the provisioning of computational resources and Apache Spark as the coordination framework. As a proof of concept, some radionuclide-resistant bacterial genomes were selected for similarity analysis. RESULTS: Experiments in Google and Microsoft Azure clouds demonstrated that SparkBLAST outperforms an equivalent system implemented on Hadoop in terms of speedup and execution times. CONCLUSIONS: The superior performance of SparkBLAST is mainly due to the in-memory operations available through the Spark framework, consequently reducing the number of local I/O operations required for distributed BLAST processing.

SUMMARY One of the main advantages of using a scientific workflow management system (SWfMS) is to orchestrate data flows among scientific activities and register provenance of the whole workflow execution. Nevertheless, the execution control of distributed activities in high performance computing environments by SWfMS presents challenges such as steering control and provenance gathering. Such challenges may become a complex task to be accomplished in bioinformatics experiments, particularly in Many Task Computing scenarios. This paper presents a data parallelism solution for a bioinformatics experiment supported by Hydra, a middleware that bridges SWfMS and high performance computing to enable workflow parallelization with provenance gathering. Hydra Many Task Computing parallelization strategies can be registered and reused. Using Hydra, provenance may also be uniformly gathered. We have evaluated Hydra using an Orthologous Gene Identification workflow. Experimental results show that a systematic approach for distributing parallel activities is viable, sparing scientist time and diminishing operational errors, with the additional benefits of distributed provenance support. Copyright © 2011 John Wiley & Sons, Ltd.

Large scale bioinformatics experiments are usually composed by a set of data flows generated by a chain of activities (programs or services) that may be modeled as scientific workflows. Current Scientific Workflow Management Systems (SWfMS) are used to orchestrate these workflows to control and monitor the whole execution. It is very common in bioinformatics experiments to process very large datasets. In this way, data parallelism is a common approach used to increase performance and reduce overall execution time. However, most of current SWfMS still lack on supporting parallel executions in high performance computing (HPC) environments. Additionally keeping track of provenance data in distributed environments is still an open, yet important problem. Recently, Hydra middleware was proposed to bridge the gap between the SWfMS and the HPC environment, by providing a transparent way for scientists to parallelize workflow executions while capturing distributed provenance. This paper analyzes data parallelism scenarios in bioinformatics domain and presents an extension to Hydra middleware through a specific cartridge that promotes data parallelism in bioinformatics workflows. Experimental results using workflows with BLAST show performance gains with the additional benefits of distributed provenance support.

BACKGROUND: Remote homology detection is a challenging problem in Bioinformatics. Arguably, profile Hidden Markov Models (pHMMs) are one of the most successful approaches in addressing this important problem. pHMM packages present a relatively small computational cost, and perform particularly well at recognizing remote homologies. This raises the question of whether structural alignments could impact the performance of pHMMs trained from proteins in the Twilight Zone, as structural alignments are often more accurate than sequence alignments at identifying motifs and functional residues. Next, we assess the impact of using structural alignments in pHMM performance. RESULTS: We used the SCOP database to perform our experiments. Structural alignments were obtained using the 3DCOFFEE and MAMMOTH-mult tools; sequence alignments were obtained using CLUSTALW, TCOFFEE, MAFFT and PROBCONS. We performed leave-one-family-out cross-validation over super-families. Performance was evaluated through ROC curves and paired two tailed t-test. CONCLUSION: We observed that pHMMs derived from structural alignments performed significantly better than pHMMs derived from sequence alignment in low-identity regions, mainly below 20%. We believe this is because structural alignment tools are better at focusing on the important patterns that are more often conserved through evolution, resulting in higher quality pHMMs. On the other hand, sensitivity of these tools is still quite low for these low-identity regions. Our results suggest a number of possible directions for improvements in this area.

SUMMARY: Growth of genome data and analysis possibilities have brought new levels of difficulty for scientists to understand, integrate and deal with all this ever-increasing information. In this scenario, GARSA has been conceived aiming to facilitate the tasks of integrating, analyzing and presenting genomic information from several bioinformatics tools and genomic databases, in a flexible way. GARSA is a user-friendly web-based system designed to analyze genomic data in the context of a pipeline. EST and GGS data can be analyzed using the system since it accepts (1) chromatograms, (2) download of sequences from GenBank, (3) Fasta files stored locally or (4) a combination of all three. Quality evaluation of chromatograms, vector removing and clusterization are easily performed as part of the pipeline. A number of local and customizable Blast and CDD analyses can be performed as well as Interpro, complemented with phylogeny analyses. GARSA is being used for the analyses of Trypanosoma vivax (GSS and EST), Trypanosoma rangeli (GSS, EST and ORESTES), Bothrops jararaca (EST), Piaractus mesopotamicus (EST) and Lutzomyia longipalpis (EST). AVAILABILITY: The GARSA system is freely available under GPL license (http://www.biowebdb.org/garsa/). For download requests visit http://www.biowebdb.org/garsa/ or contact Dr Alberto Dávila.