Evidence map›Paper›PMID 28107819›Full record

ArticleBMC bioinformatics2017

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants.

Hatem Elshazly, Yassine Souilmi, Peter J Tonellato, Dennis P Wall, Mohamed Abouelhoda

Abstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

5 authors.

Hatem ElshazlyCenter for Informatics Sciences, Nile University, Juhayna Square, Sheikh Zayed, Giza, Egypt.
Yassine SouilmiDepartment of Biology, Mohamed Vth University in Rabat, 4 Ibn Battouta Avenue, BP: 1014RP, Rabat, Morocco.
Peter J TonellatoDepartment of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA, 02115, USA.
Dennis P WallDepartment of Pediatrics and Psychiatry (by courtesy), Division of Systems Medicine & Program in Biomedical Informatics, Stanford University, Stanford, CA, 94305, USA.
Mohamed AbouelhodaCenter for Informatics Sciences, Nile University, Juhayna Square, Sheikh Zayed, Giza, Egypt. mabouelhoda@yahoo.com.

Funding

Characterizing the genetic systems of autism through multi-disease analysisR01MH090611 · NIMH · STANFORD UNIVERSITY · PI WALL, DENNIS PAUL · 2010 to 2014
$2.8M
PREDICTIVE OPTIMAL ANTICLOTTING TREATMENT FOR SEGMENTED PATIENT POPULATIONSR01LM011566 · NLM · UNIVERSITY OF MISSOURI-COLUMBIA · PI TONELLATO, PETER J. · 2013 to 2016
$1.0M
NIMH NIH HHS R01 MH090611NLM NIH HHS R01 LM011566
6 · The paper itself

Abstract

backgroundNext Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices.

resultsIn this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers.

conclusionsMC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

Indexed as

Cloud ComputingHigh-Throughput Nucleotide SequencingDatabases, GeneticGenome, HumanGenomicsHumansInternetSoftwareWorkflowCloud computingMulticloudPersonalized medicineSequence analysisVariant analysis

Identifiers

PMID28107819
PMCPMC5248509

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LicenceCC BY
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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.