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BackgroundHomology inference helps on identifying similarities, as well as differences among organisms, which provides a better insight on how closely related one might be to another. In addition, comparative genomics pipelines are widely adopted tools designed using different bioinformatics applications and algorithms. In this article, we propose a methodology to build improved orthologous databases with the potential to aid on protozoan target identification, one of the many tasks which benefit from comparative genomics tools.MethodsOur analyses are based on OrthoSearch, a comparative genomics pipeline originally designed to infer orthologs through protein-profile comparison, supported by an HMM, reciprocal best hits based approach. Our methodology allows OrthoSearch to confront two orthologous databases and to generate an improved new one. Such can be later used to infer potential protozoan targets through a similarity analysis against the human genome.ResultsThe protein sequences of Cryptosporidium hominis, Entamoeba histolytica and Leishmania infantum genomes were comparatively analyzed against three orthologous databases: (i) EggNOG KOG, (ii) ProtozoaDB and (iii) Kegg Orthology (KO). That allowed us to create two new orthologous databases, “KO + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB”, with 16,938 and 27,701 orthologous groups, respectively.Such new orthologous databases were used for a regular OrthoSearch run. By confronting “KO + EggNOG KOG” and “KO + EggNOG KOG + ProtozoaDB” databases and protozoan species we were able to detect the following total of orthologous groups and coverage (relation between the inferred orthologous groups and the species total number of proteins): Cryptosporidium hominis: 1,821 (11 %) and 3,254 (12 %); Entamoeba histolytica: 2,245 (13 %) and 5,305 (19 %); Leishmania infantum: 2,702 (16 %) and 4,760 (17 %).Using our HMM-based methodology and the largest created orthologous database, it was possible to infer 13 orthologous groups which represent potential protozoan targets; these were found because of our distant homology approach.We also provide the number of species-specific, pair-to-pair and core groups from such analyses, depicted in Venn diagrams.ConclusionsThe orthologous databases generated by our HMM-based methodology provide a broader dataset, with larger amounts of orthologous groups when compared to the original databases used as input. Those may be used for several homology inference analyses, annotation tasks and protozoan targets identification.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-1090-0) contains supplementary material, which is available to authorized users.
Scientific Workflows (SWFs) are widely used to model processes in e-Science. SWFs are executed by means of Workflow Management Systems (WMSs), which orchestrate the workload on top of computing infrastructures. The advent of cloud computing infrastructures has opened the door of using ondemand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilization and elasticity. In this article we present an ad-hoc solution for managing workflows exploiting the capabilities of cloud orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (clusters) to minimize costs and response time. The work does not propose yet another WMS, but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The article shows several configuration experiments from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The article computes running time and cost suggesting best practices.
Scientific Workflows (SWFs) are widely used to model processes in e-Science. SWFs are executed by means of Workflow Management Systems (WMSs), which orchestrate the workload on top of computing infrastructures. The advent of cloud computing infrastructures has opened the door of using ondemand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilization and elasticity. In this article we present an ad-hoc solution for managing workflows exploiting the capabilities of cloud orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (clusters) to minimize costs and response time. The work does not propose yet another WMS, but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The article shows several configuration experiments from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The article computes running time and cost suggesting best practices.
Abstract. Many scientific experiments deal with data-intensive applications and the orchestration of computational workflow activities. These can benefit from data parallelism exploited in parallel systems to minimize execution time. Due to its complexity, robustness and efficiency to exploit data parallelism, grid infrastructures are widely used in some e-Science areas like bioinformatics. Workflow techniques are very important to in-silico bioinformatics experiments, allowing the e-scientist to describe and enact experimental process in a structured, repeatable and verifiable way. The main purpose of this paper is to describe our experience with Tavena Workbench and PeDRo, which are part of my Grid project. Taverna is provided with a workflow toolset and enactor, allowing the specification of processing units, data transfer and execution constraints. As a data entry tool, PeDRo provides a model, a controlled vocabulary and field validations for Web Services descriptions, leveraging the knowledge associated to the workflows. The main contribution of this work is a summary of some considerations drawn by our experience with the use of these tools, emphasizing its advantages and negative aspects, together with proposals for some future improvements.
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