Detecting distant homologies on protozoans metabolic pathways using scientific workflows

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

doi:10.1504/ijdmb.2010.033520

Cited by 5 publications

(7 citation statements)

References 47 publications

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“…OrthoSearch (Cruz et al, 2010) is a scientific workflow developed under VisTrails Scientific Workflow Management System (Callahan et al, 2006). It was used to infer orthologous groups between protozoa and COG/KOG.…”

Section: Orthology Identificationmentioning

confidence: 99%

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An Orthology-Based Analysis of Pathogenic Protozoa Impacting Global Health: An Improved Comparative Genomics Approach with Prokaryotes and Model Eukaryote Orthologs

Cuadrat

Cruz²,

Tschoeke³

et al. 2014

OMICS: A Journal of Integrative Biology

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A key focus in 21 st century integrative biology and drug discovery for neglected tropical and other diseases has been the use of BLAST-based computational methods for identification of orthologous groups in pathogenic organisms to discern orthologs, with a view to evaluate similarities and differences among species, and thus allow the transfer of annotation from known/curated proteins to new/non-annotated ones. We used here a profile-based sensitive methodology to identify distant homologs, coupled to the NCBI's COG (Unicellular orthologs) and KOG (Eukaryote orthologs), permitting us to perform comparative genomics analyses on five protozoan genomes. OrthoSearch was used in five protozoan proteomes showing that 3901 and 7473 orthologs can be identified by comparison with COG and KOG proteomes, respectively. The core protozoa proteome inferred was 418 Protozoa-COG orthologous groups and 704 Protozoa-KOG orthologous groups: (i) 31.58% (132/418) belongs to the category J (translation, ribosomal structure, and biogenesis), and 9.81% (41/418) to the category O (post-translational modification, protein turnover, chaperones) using COG; (ii) 21.45% (151/704) belongs to the categories J, and 13.92% (98/704) to the O using KOG. The phylogenomic analysis showed four well-supported clades for Eukarya, discriminating Multicellular [(i) human, fly, plant and worm] and Unicellular [(ii) yeast, (iii) fungi, and (iv) protozoa] species. These encouraging results attest to the usefulness of the profile-based methodology for comparative genomics to accelerate semi-automatic re-annotation, especially of the protozoan proteomes. This approach may also lend itself for applications in global health, for example, in the case of novel drug target discovery against pathogenic organisms previously considered difficult to research with traditional drug discovery tools.

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Section: Orthology Identificationmentioning

confidence: 99%

“…While the blast-based OrthoMCL software (Li et al, 2003) was used to infer orthologs among protozoa species, OrthoSearch (Cruz et al, 2010) was used to infer ProtozoaUnicellular and Protozoa-Eukaryote orthologs. The latter software was originally designed as a scientific workflow for orthology inference using an HMM-based approach.…”

mentioning

confidence: 99%

An Orthology-Based Analysis of Pathogenic Protozoa Impacting Global Health: An Improved Comparative Genomics Approach with Prokaryotes and Model Eukaryote Orthologs

Cuadrat

Cruz²,

Tschoeke³

et al. 2014

OMICS: A Journal of Integrative Biology

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“…In this sub-section, we describe how we used OrthoSearch [10], a genomic workflow, oriented to analyze large volumes of biological data on genomic databases like COG/KOG [30], to detect distant homologs genes in the parasites that cause neglected tropical diseases (NTD). OrthoSearch was chosen due to two key characteristics.…”

Section: Case Study: a Scientific Workflow To Detect Homologiesmentioning

confidence: 99%

“…OrthoSearch is clearly a data-massive application, both computationally and from a workflow perspective [10]. In order to compare the overhead of Matriohska we prepared six configurations (1 to 32 instances) to execute both with the fragment of OrthoSearch and a similar pipeline with the same sequence of bioinformatics packages.…”

Section: Initial Evaluation Of Matriohskamentioning

confidence: 99%

Collecting cloud provenance metadata with Matriohska

Cruz¹,

Barbosa

Zavaleta

et al. 2014

Proceedings of the 29th Annual ACM Symposium on Applied Computing

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Scientific Workflows are abstractions used to model in silico scientific experiments. Cloud environments are still incipient in collecting and recording prospective and retrospective provenance. This paper presents an approach to support collecting metadata provenance of in silico scientific experiments executed in public clouds. The strategy was implemented as a distributed and modular architecture named Matriohska. This paper also presents a provenance data model compatible with PROV specification. We also show preliminary results that describe how provenance metadata was captured from the components running in the cloud.

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“…Besides a simple categorization effort [ 26 ], we will follow Dalquen’s proposition [ 29 ]. Briefly, three distinct approaches are available: (i) the one which use multiple sequence alignment (MSA) scores along with reciprocal best hits, such as OrthoSearch [ 30 ], OrthoMCL [ 24 ] and InParanoid [ 31 ]; (ii) that which rely on evolutionary distance calculus, as RSD [ 32 , 33 ]; (iii) and that based on phylogenetic trees reconstruction, as SPIMAP [ 34 ].…”

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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Detecting distant homologies on protozoans metabolic pathways using scientific workflows

Cited by 5 publications

References 47 publications

An Orthology-Based Analysis of Pathogenic Protozoa Impacting Global Health: An Improved Comparative Genomics Approach with Prokaryotes and Model Eukaryote Orthologs

An Orthology-Based Analysis of Pathogenic Protozoa Impacting Global Health: An Improved Comparative Genomics Approach with Prokaryotes and Model Eukaryote Orthologs

Collecting cloud provenance metadata with Matriohska

Improved orthologous databases to ease protozoan targets inference

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