ProtozoaDB: dynamic visualization and exploration of protozoan genomes

Dávila, Alberto M. R.; Mendes, Pablo N.; Wagner, Glauber; Tschoeke, Diogo A.; Cuadrat, Rafael R. C.; Liberman, Felipe; Matos, Luciana; Satake, Thiago S.; Ocaña, Kary; Triana-Chávez, Omar; Cruz, Sérgio Manuel Serra da; Jucá, Henrique C. L.; Cury, José; Silva, Fabrício Nogueira da; Gerônimo, Guilherme Arthur; Ruiz, Margarita; Ruback, Eduardo; Silva, Floriano P.; Probst, Christian M.; Grisard, Edmundo C.; Krieger, Marco Aurélio; Goldenberg, Samuel; Cavalcanti, Maria Cláudia; Moraes, Milton Ozório; Campos, María Luiza Machado; Mattoso, Marta

doi:10.1093/nar/gkm820

Cited by 17 publications

(16 citation statements)

References 14 publications

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“…In our case study, BLAST has been used to identify similar sequences between Plasmodium falciparum sequences and SWISS-PROT (http://www.expasy.ch/sprot/) database release 56.0 of 22-Jul-08 of UniProtKB/Swiss-Prot that contains 392,667 sequence entries, comprising 141,217,034 amino acids abstracted from 172,036 references. Genomics data from Plasmodium falciparum are analyzed and hosted by ProtozoaDB System [5]. ProtozoaDB integrates heterogeneous databases, (re)annotation systems, analyses tools based on distributed computing.…”

Section: Case Studymentioning

confidence: 99%

“…In most bioinformatics laboratories and genomics research centers, experiments are composed by programs or services based on solid methods and algorithms such as BLAST or MAFFT [2]. These scientific experiments are being modeled as scientific workflows [3][4][5]. Scientific workflows are usually managed by complex engines called Scientific Workflow Management Systems (SWfMS).…”

Section: Introductionmentioning

confidence: 99%

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Data parallelism in bioinformatics workflows using Hydra

Coutinho

Ogasawara

Oliveira

et al. 2010

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

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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.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data parallelism in bioinformatics workflows using Hydra

Coutinho

Ogasawara

Oliveira

et al. 2010

Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

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“…The protein dataset of five protozoa (T. cruzi, T. brucei, L. major, E. histolytica, and P. falciparum), available in Fasta format, was obtained from ProtozoaDB version 1 (Dávila et al, 2008). We used the CD-HIT program with a cutoff of 100% of identity to remove the redundancy of the sequences.…”

Section: Datasetmentioning

confidence: 99%

“…The Protozoa-COG/KOG orthologs identified by OrthoSearch were: (i) carefully analyzed aiming to use them to reannotate proteins from the five protozoa; and (ii) loaded into ProtozoaDB (Dávila et al, 2008) using another scientific workflow called OrthoLoad. The re-annotation process mentioned in item (i) was manually performed.…”

Section: Re-annotationmentioning

confidence: 99%

“…Complete proteomes of five protozoan species (T. brucei, T. cruzi, L. major, E. histolytica, and P. falciparum) were analyzed. The proteome sequences of these protozoa and in silico experimental results (including orthology data) are available in public databases such as ProtozoaDB (Dávila et al, 2008;BiowebDB, 2009) (http://protozoadb.biowebdb.org). Figure 1 illustrates the methodology used in this work and the following sections provide details about the methodology.…”

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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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Eukaryotic Genomes: From Parasites to Primates

Pevsner

2009

Bioinformatics and Functional Genomics

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ProtozoaDB: dynamic visualization and exploration of protozoan genomes

Cited by 17 publications

References 14 publications

Data parallelism in bioinformatics workflows using Hydra

Data parallelism in bioinformatics workflows using Hydra

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

Eukaryotic Genomes: From Parasites to Primates

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