GEMMA — A Grid environment for microarray management and analysis in bone marrow stem cells experiments

Beltrame, Francesco; Papadimitropoulos, Adam; Porro, Ivan; Scaglione, Silvia; Schenone, Andrea; Torterolo, Livia; Viti, Federica

doi:10.1016/j.future.2006.07.008

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…Starting from user requirements and available components, it has been decided to build the service using BMportal, the biomedical portal framework available at Biolab [ 25 , 26 ], that offers basic services in different biomedical fields that can be combined into larger services covering complex procedures, as for the use case here presented.…”

Section: Methodsmentioning

confidence: 99%

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

et al. 2009

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BackgroundComplex microarray gene expression datasets can be used for many independent analyses and are particularly interesting for the validation of potential biomarkers and multi-gene classifiers. This article presents a novel method to perform correlations between microarray gene expression data and clinico-pathological data through a combination of available and newly developed processing tools.ResultsWe developed Survival Online (available at ), a Web-based system that allows for the analysis of Affymetrix GeneChip microarrays by using a parallel version of dChip. The user is first enabled to select pre-loaded datasets or single samples thereof, as well as single genes or lists of genes. Expression values of selected genes are then correlated with sample annotation data by uni- or multi-variate Cox regression and survival analyses. The system was tested using publicly available breast cancer datasets and GO (Gene Ontology) derived gene lists or single genes for survival analyses.ConclusionThe system can be used by bio-medical researchers without specific computation skills to validate potential biomarkers or multi-gene classifiers. The design of the service, the parallelization of pre-processing tasks and the implementation on an HPC (High Performance Computing) environment make this system a useful tool for validation on several independent datasets.

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Section: Methodsmentioning

confidence: 99%

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

et al. 2009

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“…Two input files are used: the first one contains specific options for the execution; the second one contains the list of the microarray files used for the analysis. As a second step, in order to simplify the use of above mentioned dChip versions, the executables have been integrated within a biomedical portal [ 3 , 4 ] which provides a simple graphical user interface to run the application. Such a portal integration allows unpractised users to store their experimental data on a complex storage system and access distributed data and services in a transparent way.…”

Section: Methodsmentioning

confidence: 99%

A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

et al. 2008

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Background: Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible for serious diseases, with a great scientific impact and a wide application area. Several standalone applications had been developed in order to analyze microarray data. Two of the most known free analysis software packages are the R-based Bioconductor and dChip. The part of dChip software concerning the calculation and the analysis of gene expression has been modified to permit its execution on both cluster environments (supercomputers) and Grid infrastructures (distributed computing).

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“…New technologies such as microarray and next generation sequencing (NGS) are capable of measuring all the genes in a cell or tissue quantitatively every time the experiment is repeated, while providing a large amount of data ( 2 ). The great number of gene expression data increases the challenge of how to identify biomarkers and small sets of critical genes associated with disease and the challenge of being able to detect the disease class ( 3 , 4 ).…”

Section: Introductionmentioning

confidence: 99%

“…The yield of this method is very high and it is able to analyze a significant amount of information at a short time. This technique provides the understanding of cellular processes and networks for genome-wide expression profiles (3). Also, gene expression at various time points is a dynamic process, which can be both transiently and permanently changed, and thus, returns different behaviors at the cell and tissue level (1,4).…”

Section: Introductionmentioning

confidence: 99%

Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest

2017

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Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and small-n. Therefore, RF can be used to select and rank the genes for the diagnosis and effective treatment of cancer. Methods: The microarray gene expression data of colon, leukemia, and prostate cancers were collected from public databases. Primary preprocessing was done on them using limma package, and then, the RF classification method was implemented on datasets separately in R software. Finally, the selected genes in each of the cancers were evaluated and compared with those of previous experimental studies and their functionalities were assessed in molecular cancer processes. Result: The RF method extracted very small sets of genes while it retained its predictive performance. About colon cancer data set DIEXF , GUCA2A , CA7 , and IGHA1 key genes with the accuracy of 87.39 and precision of 85.45 were selected. The SNCA , USP20 , and SNRPA1 genes were selected for prostate cancer with the accuracy of 73.33 and precision of 66.67. Also, key genes of leukemia data set were BAG4 , ANKHD1 - EIF4EBP3 , PLXNC1 , and PCDH9 genes, and the accuracy and precision were 100 and 95.24, respectively. Conclusion: The current study results showed most of the selected genes involved in the processes and cancerous pathways were previously reported and had an important role in shifting from normal cell to abnormal.

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GEMMA — A Grid environment for microarray management and analysis in bone marrow stem cells experiments

Cited by 6 publications

References 34 publications

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

Survival Online: a web-based service for the analysis of correlations between gene expression and clinical and follow-up data

A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data

Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest

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