Dataset Information

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

ABSTRACT:

Background

Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support.

Results

We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can be used for annotating and communicating microarray data in a MIAME compliant fashion.

Conclusion

MAGE-TAB will enable laboratories without bioinformatics experience or support to manage, exchange and submit well-annotated microarray data in a standard format using a spreadsheet. The MAGE-TAB format is self-contained, and does not require an understanding of MAGE-ML or XML.

SUBMITTER: Rayner TF

PROVIDER: S-EPMC1687205 | biostudies-literature | 2006 Nov

REPOSITORIES: biostudies-literature

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Publications

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

Rayner Tim F TF Rocca-Serra Philippe P Spellman Paul T PT Causton Helen C HC Farne Anna A Holloway Ele E Irizarry Rafael A RA Liu Junmin J Maier Donald S DS Miller Michael M Petersen Kjell K Quackenbush John J Sherlock Gavin G Stoeckert Christian J CJ White Joseph J Whetzel Patricia L PL Wymore Farrell F Parkinson Helen H Sarkans Ugis U Ball Catherine A CA Brazma Alvis A

BMC bioinformatics 20061106

<h4>Background</h4>Sharing of microarray data within the research community has been greatly facilitated by the development of the disclosure and communication standards MIAME and MAGE-ML by the MGED Society. However, the complexity of the MAGE-ML format has made its use impractical for laboratories lacking dedicated bioinformatics support.<h4>Results</h4>We propose a simple tab-delimited, spreadsheet-based format, MAGE-TAB, which will become a part of the MAGE microarray data standard and can b ...[more]

PMID: 17087822

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Project description:Background and motivationThe high-throughput genomics communities have been successfully using standardized spreadsheet-based formats to capture and share data within labs and among public repositories. The nanomedicine community has yet to adopt similar standards to share the diverse and multi-dimensional types of data (including metadata) pertaining to the description and characterization of nanomaterials. Owing to the lack of standardization in representing and sharing nanomaterial data, most of the data currently shared via publications and data resources are incomplete, poorly-integrated, and not suitable for meaningful interpretation and re-use of the data. Specifically, in its current state, data cannot be effectively utilized for the development of predictive models that will inform the rational design of nanomaterials.ResultsWe have developed a specification called ISA-TAB-Nano, which comprises four spreadsheet-based file formats for representing and integrating various types of nanomaterial data. Three file formats (Investigation, Study, and Assay files) have been adapted from the established ISA-TAB specification; while the Material file format was developed de novo to more readily describe the complexity of nanomaterials and associated small molecules. In this paper, we have discussed the main features of each file format and how to use them for sharing nanomaterial descriptions and assay metadata.ConclusionThe ISA-TAB-Nano file formats provide a general and flexible framework to record and integrate nanomaterial descriptions, assay data (metadata and endpoint measurements) and protocol information. Like ISA-TAB, ISA-TAB-Nano supports the use of ontology terms to promote standardized descriptions and to facilitate search and integration of the data. The ISA-TAB-Nano specification has been submitted as an ASTM work item to obtain community feedback and to provide a nanotechnology data-sharing standard for public development and adoption.

Project description:BACKGROUND: With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the specific analysis. RESULTS: Two microarray data sets based on a 17k cDNA microarray system were used, consisting of 82 normal colon mucosa and 72 colorectal cancer tissues. Each data set was prepared from either total RNA or amplified mRNA, and the difference of RNA source between these two data sets was detected by ANOVA (Analysis of variance) model. A simple integration method was introduced which was based on the distributions of gene expression ratios among different microarray data sets. The method transformed gene expression ratios into the form of a reference data set on a gene by gene basis. Hierarchical clustering analysis, density and box plots, and mixture scores with correlation coefficients revealed that the two data sets were well intermingled, indicating that the proposed method minimized the experimental bias. In addition, any RNA source effect was not detected by the proposed transformation method. In the mixed data set, two previously identified subgroups of normal and tumor were well separated, and the efficiency of integration was more prominent in tumor groups than normal groups. The transformation method was slightly more effective when a data set with strong homogeneity in the same experimental group was used as a reference data set. CONCLUSION: Proposed method is simple but useful to combine several data sets from different experimental conditions. With this method, biologically useful information can be detectable by applying various analytic methods to the combined data set with increased sample size.

Project description:Breast cancer is the second leading cause of cancer-associated mortality among women worldwide, and the prevalence and mortality rates associated with this disease are high in Western countries. The melanoma-associated antigen (MAGE) family proteins are well-known tumor-specific antigens; this family includes >60 proteins that serve an important part in cell cycle withdrawal, neuronal differentiation and apoptosis. The aim of the present study was to identify a biomarker within the MAGE family that is specific for breast cancer. In the present study, the prognostic role of MAGE mRNA expression was investigated in patients with breast cancer using the Kaplan-Meier plotter database. The prognostic value of MAGE members in the different intrinsic subtypes of breast cancer was further investigated, as well as the clinicopathological features of the disease. The results of the present study indicated that patients with breast cancer that had high mRNA expression levels of MAGEA5, MAGEA8, MAGEB4 and MAGEB6 had an improved relapse-free survival, whereas those with high mRNA expression levels of MAGEB18 and MAGED4 did not. These results suggested that MAGEA5, MAGEA8, MAGEB4 and MAGEB6 may have roles as tumor suppressors in the occurrence and development of breast cancer, whereas MAGEB18 and MAGED4 may possess carcinogenic potential. MAGED2, MAGED3 and MAGEF1 had different effects depending on the type of breast cancer. In particular, high MAGEC3 mRNA expression was associated with worse RFS in lymph node-positive breast cancer, but with improved RFS in lymph node-negative breast cancer. In patients with wild-type TP53 and patients with different pathological grades of breast cancer, MAGEE2, MAGEH1 and MAGEL2 were more worthy of attention as potential prognostic factors. The results of the present study may help to elucidate the role of MAGE family members in the development of breast cancer, and may promote further research that identifies MAGE-targeting reagents for the treatment of breast cancer.

Dataset Information

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

Background

Results

Conclusion

Publications

A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.

Similar Datasets

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