Web API for biology with a workflow navigation system

Kwon, Yeondae; Shigemoto, Yasumasa; Kuwana, Yoshikazu; Sugawara, Hideaki

doi:10.1093/nar/gkp300

Cited by 29 publications

(21 citation statements)

References 7 publications

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“…It should be noted that due to the complexity of biological data-mining situations, in its current state, the analysis of large gene lists with the current gene set enrichment tools is still more of an exploratory data-mining procedure rather than a pure and exact statistical solution. The best analytical conclusions are made with the aid of the investigator's bio-knowledge, integrated annotation databases, computing algorithms and the enrichment p-values derived from statistical methods [28]. In our study, the occurrence of stress-related genes were robust and are extensive among the responses, especially cold, heat and biotic stresses, although the p-values calculated showed less significant results.…”

Section: Discussionmentioning

confidence: 74%

Transcriptome analysis of germinating maize kernels exposed to smoke-water and the active compound KAR1

et al. 2010

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BackgroundSmoke released from burning vegetation functions as an important environmental signal promoting the germination of many plant species following a fire. It not only promotes the germination of species from fire-prone habitats, but several species from non-fire-prone areas also respond, including some crops. The germination stimulatory activity can largely be attributed to the presence of a highly active butenolide compound, 3-methyl-2H-furo[2,3-c]pyran-2-one (referred to as karrikin 1 or KAR1), that has previously been isolated from plant-derived smoke. Several hypotheses have arisen regarding the molecular background of smoke and KAR1 action.ResultsIn this paper we demonstrate that although smoke-water and KAR1 treatment of maize kernels result in a similar physiological response, the gene expression and the protein ubiquitination patterns are quite different. Treatment with smoke-water enhanced the ubiquitination of proteins and activated protein-degradation-related genes. This effect was completely absent from KAR1-treated kernels, in which a specific aquaporin gene was distinctly upregulated.ConclusionsOur findings indicate that the array of bioactive compounds present in smoke-water form an environmental signal that may act together in germination stimulation. It is highly possible that the smoke/KAR1 'signal' is perceived by a receptor that is shared with the signal transduction system implied in perceiving environmental cues (especially stresses and light), or some kind of specialized receptor exists in fire-prone plant species which diverged from a more general one present in a common ancestor, and also found in non fire-prone plants allowing for a somewhat weaker but still significant response. Besides their obvious use in agricultural practices, smoke and KAR1 can be used in studies to gain further insight into the transcriptional changes during germination.

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Section: Discussionmentioning

confidence: 74%

Transcriptome analysis of germinating maize kernels exposed to smoke-water and the active compound KAR1

et al. 2010

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“…For example, most DDBJ [16] service parameters were declared in their WSDL as plain strings. Therefore, we have annotated the parameters with (semantic) datatypes used in the INB service registry in the Access layer implemented for this repository.…”

Section: Resultsmentioning

confidence: 99%

“…As a proof of concept, MAPI provides support for BioMOBY WS [14], WSDL – described SOAP WS (for example, from European Bioinformatics Institute, EBI [15] and DNA Data Bank of Japan, DDBJ [16]), Taverna [17] workflows, WS from INB [18] and ACGT [19] projects.…”

Section: Introductionmentioning

confidence: 99%

MAPI: a software framework for distributed biomedical applications

Karlsson

Trelles

2013

J Biomed Sem

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BackgroundThe amount of web-based resources (databases, tools etc.) in biomedicine has increased, but the integrated usage of those resources is complex due to differences in access protocols and data formats. However, distributed data processing is becoming inevitable in several domains, in particular in biomedicine, where researchers face rapidly increasing data sizes. This big data is difficult to process locally because of the large processing, memory and storage capacity required.ResultsThis manuscript describes a framework, called MAPI, which provides a uniform representation of resources available over the Internet, in particular for Web Services. The framework enhances their interoperability and collaborative use by enabling a uniform and remote access. The framework functionality is organized in modules that can be combined and configured in different ways to fulfil concrete development requirements.ConclusionsThe framework has been tested in the biomedical application domain where it has been a base for developing several clients that are able to integrate different web resources. The MAPI binaries and documentation are freely available at http://www.bitlab-es.com/mapi under the Creative Commons Attribution-No Derivative Works 2.5 Spain License. The MAPI source code is available by request (GPL v3 license).

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“…Reference data, such as the relevant genome sequence, can be retrieved from DDBJ databases by the Simple Object Access Protocol (SOAP). 37 Users can confirm the error rate by read position and can trim low-quality bases from the reads. The numbers of mapped reads, genome coverage, depth, and maximum contig length are reported.…”

Section: Methodsmentioning

confidence: 99%

DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

et al. 2013

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High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at .

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Web API for biology with a workflow navigation system

Cited by 29 publications

References 7 publications

Transcriptome analysis of germinating maize kernels exposed to smoke-water and the active compound KAR1

Transcriptome analysis of germinating maize kernels exposed to smoke-water and the active compound KAR1

MAPI: a software framework for distributed biomedical applications

DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

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