OrthoSearch

Cruz, Sérgio Manuel Serra da; Batista, Vanessa Oliveira; Dávila, Alberto M. R.; Silva, Edno; Tosta, Frederico; Vilela, Clarissa; Campos, María Luiza Machado; Cuadrat, Rafael R. C.; Tschoeke, Diogo A.; Mattoso, Marta

doi:10.1145/1363686.1363983

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…OrthoSearch [ 40 ] is a scientific workflow [ 41 ] for homology inference among species. Initially conceived as a Perl-based routine, it uses a reciprocal best hits, HMM-based approach.…”

Section: Introductionmentioning

confidence: 99%

Improved orthologous databases to ease protozoan targets inference

2015

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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.

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“…OrthoSearch [ 40 ] is a scientific workflow [ 41 ] for homology inference among species. Initially conceived as a Perl-based routine, it uses a reciprocal best hits, HMM-based approach.…”

Section: Introductionmentioning

confidence: 99%

Improved orthologous databases to ease protozoan targets inference

2015

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Many task computing for orthologous genes identification in protozoan genomes using Hydra

Coutinho

Ogasawara

Oliveira

et al. 2011

Concurrency and Computation

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SUMMARYOne 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.

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A performance/cost model for a CUDA drug discovery application on physical and public cloud infrastructures

Guerrero

Wallace

Vázquez-Poletti

et al. 2013

Concurrency and Computation

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SUMMARYVirtual Screening (VS) methods can considerably aid drug discovery research, predicting how ligands interact with drug targets. BINDSURF is an efficient and fast blind VS methodology for the determination of protein binding sites, depending on the ligand, using the massively parallel architecture of graphics processing units(GPUs) for fast unbiased prescreening of large ligand databases. In this contribution, we provide a performance/cost model for the execution of this application on both local system and public cloud infrastructures. With our model, it is possible to determine which is the best infrastructure to use in terms of execution time and costs for any given problem to be solved by BINDSURF. Conclusions obtained from our study can be extrapolated to other GPU‐based VS methodologies.Copyright © 2013 John Wiley & Sons, Ltd.

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OrthoSearch

Cited by 7 publications

References 11 publications

Improved orthologous databases to ease protozoan targets inference

Improved orthologous databases to ease protozoan targets inference

Many task computing for orthologous genes identification in protozoan genomes using Hydra

A performance/cost model for a CUDA drug discovery application on physical and public cloud infrastructures

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