Evidence map›Paper›PMID 23281941›Full record

ArticleBMC bioinformatics2012

Personalized cloud-based bioinformatics services for research and education: use cases and the elasticHPC package.

Mohamed El-Kalioby, Mohamed Abouelhoda, Jan Krüger, Robert Giegerich, Alexander Sczyrba, Dennis P Wall, Peter Tonellato

Abstract read
In one paragraph

Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Development of user-friendly and interactive data collection system for cerebral palsy.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2016
    Article
  4. Parallel computing in genomic research: advances and applications.Advances and applications in bioinformatics and chemistry : AABC · 2015
    Review
  5. Article
  6. Review
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mohamed El-KaliobyCentre for Informatics Sciences, Nile University, Giza, Egypt. mohamed.elkalioby@nileu.edu.eg
Mohamed Abouelhoda
Jan Krüger
Robert Giegerich
Alexander Sczyrba
Dennis P Wall
Peter Tonellato

Funding

Method for Prediction of Efficacy of Genetic-Based Prediction Models of PersonaliR01LM010130 · NLM · HARVARD MEDICAL SCHOOL · PI TONELLATO, PETER J. · 2009 to 2010
$967k
NLM NIH HHS R01 LM010130
6 · The paper itself

Abstract

backgroundBioinformatics services have been traditionally provided in the form of a web-server that is hosted at institutional infrastructure and serves multiple users. This model, however, is not flexible enough to cope with the increasing number of users, increasing data size, and new requirements in terms of speed and availability of service. The advent of cloud computing suggests a new service model that provides an efficient solution to these problems, based on the concepts of "resources-on-demand" and "pay-as-you-go". However, cloud computing has not yet been introduced within bioinformatics servers due to the lack of usage scenarios and software layers that address the requirements of the bioinformatics domain.

resultsIn this paper, we provide different use case scenarios for providing cloud computing based services, considering both the technical and financial aspects of the cloud computing service model. These scenarios are for individual users seeking computational power as well as bioinformatics service providers aiming at provision of personalized bioinformatics services to their users. We also present elasticHPC, a software package and a library that facilitates the use of high performance cloud computing resources in general and the implementation of the suggested bioinformatics scenarios in particular. Concrete examples that demonstrate the suggested use case scenarios with whole bioinformatics servers and major sequence analysis tools like BLAST are presented. Experimental results with large datasets are also included to show the advantages of the cloud model.

conclusionsOur use case scenarios and the elasticHPC package are steps towards the provision of cloud based bioinformatics services, which would help in overcoming the data challenge of recent biological research. All resources related to elasticHPC and its web-interface are available at http://www.elasticHPC.org.

Indexed as

Computational BiologyInformation ServicesInternetSoftwareEducationResearch

Identifiers

PMID23281941
PMCPMC3521398

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.