A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB.
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ABSTRACT:
SUBMITTER: Rayner TF
PROVIDER: S-EPMC1687205 | biostudies-literature | 2006 Nov
REPOSITORIES: biostudies-literature
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]
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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.
| S-EPMC3598649 | biostudies-literature
Project description:Analysis of trends in nanotoxicology data and the development of data driven models for nanotoxicity is facilitated by the reporting of data using a standardised electronic format. ISA-TAB-Nano has been proposed as such a format. However, in order to build useful datasets according to this format, a variety of issues has to be addressed. These issues include questions regarding exactly which (meta)data to report and how to report them. The current article discusses some of the challenges associated with the use of ISA-TAB-Nano and presents a set of resources designed to facilitate the manual creation of ISA-TAB-Nano datasets from the nanotoxicology literature. These resources were developed within the context of the NanoPUZZLES EU project and include data collection templates, corresponding business rules that extend the generic ISA-TAB-Nano specification as well as Python code to facilitate parsing and integration of these datasets within other nanoinformatics resources. The use of these resources is illustrated by a "Toy Dataset" presented in the Supporting Information. The strengths and weaknesses of the resources are discussed along with possible future developments.
| S-EPMC4660926 | biostudies-literature
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.
| S-EPMC1914088 | biostudies-literature
Project description:A non-positional (or suspension) cell microarray was developed using shape-coded SU-8 photoresist microboards for potential application in multiplex and high-throughput cell-based assays. A conventional photolithography process on glass slides produced various shapes of SU-8 micropatterns that had a lateral dimension of 200 μm and a thickness of 40 μm. The resultant micropatterns were detached from the slides by sonication and named "microboards" due to the fact that had a much larger lateral dimension than thickness. The surfaces of the SU-8 microboards were modified with collagen to promote cell adhesion, and it was confirmed that collagen-coated SU-8 microboards supported cell adhesion and proliferation. Seeding of cells into poly(ethylene glycol)(PEG) hydrogel-coated well plates containing collagen-modified microboards resulted in selective cell adhesion onto the microboards due to the non-adhesiveness of PEG hydrogel toward cells, thereby creating non-positional arrays of microboards carrying cells. Finally, two different cell types (fibroblasts and HeLa cells) were separately cultured on different shapes of microboards and subsequently mixed together to create a non-positional cell microarray consisting of multiple cell types where each cell could be easily identified by the shape of the microboard to which they had adhered. Because numerous unique shapes of microboards can be fabricated using this method by simply changing the photomask designs, high throughput and multiplex cell-based assays would be easily achieved with this system in the future.
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| S-EPMC3935197 | biostudies-literature
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.
| S-EPMC6733005 | biostudies-literature
Project description:SummarySubmission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically generate ISA-Tab metadata file stubs from raw XML metabolomics data files. The parsing packages are separated into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). Overall, the use of mzML2ISA & nmrML2ISA reduces the time needed to capture metadata substantially (capturing 90% of metadata on assay and sample levels), is much less prone to user input errors, improves compliance with minimum information reporting guidelines and facilitates more finely grained data exploration and querying of datasets.Availability and implementationmzML2ISA & nmrML2ISA are available under version 3 of the GNU General Public Licence at https://github.com/ISA-tools. Documentation is available from http://2isa.readthedocs.io/en/latest/.Contactreza.salek@ebi.ac.uk or isatools@googlegroups.com.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:A new human papillomavirus (HPV) assay using high-density DNA microarrays is described. An HPV DNA fragment from the 3' end of the E1 gene was amplified and digoxigenin labeled by PCR, and the resulting amplicons were hybridized onto type-specific oligonucleotides immobilized on high-density DNA microarrays. For detection, a simple immunohistochemical staining procedure was used with a substrate that has both colorimetric and fluorescent properties. This detection chemistry enables the rapid identification of reactive spots by regular light microscopy and semiquantification by laser scanning. Both single and multiple HPV infections are recognized by this assay, and the corresponding HPV types are easily identified. With this assay, 53 mucosal HPV types were detected and identified. A total of 45 HPV types were identified by a single type-specific probe, whereas the remaining 8 mucosal HPV types could be identified by a specific combination of probes. The simple assay format allows usage of this assay without expensive equipment, making it accessible to all diagnostic laboratories with PCR facilities.
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Project description:Fungal diseases in immunocompromised hosts pose significant threats to their prognoses. An accurate diagnosis and identification of the fungal pathogens causing the infection are critical to determine the proper therapeutic interventions, but these are often not achieved, due to difficulties with isolation and morphological identification. In an effort to ultimately carry out the simultaneous detection of all human pathogenic microbes, we developed a simple system to identify 26 clinically important fungi by using a combination of PCR amplification and DNA microarray assay (designated PCR-DM), in which PCR-amplified DNA from the internal transcribed spacer region of the rRNA gene was hybridized to a DNA microarray fabricated with species-specific probes sets using the Bubble Jet technology. PCR-DM reliably identified all 26 reference strains; hence, we applied it to cases of onychomycosis, taking advantage of the accessibility of tissue from skin. PCR-DM detected fungal DNA and identified pathogens in 92% of 106 microscopy-confirmed onychomycosis specimens. In contrast, culture was successful for only 36 specimens (34%), 3 of which had results inconsistent with the results of PCR-DM, but sequence analysis of the isolates proved that the PCR-DM result was correct. Thus, PCR-DM provides a powerful method to identify pathogenic fungi with high sensitivity and speed directly from tissue specimens, and this concept could be applied to other fungal or nonfungal infectious human diseases in less accessible anatomical sites.
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Project description:BackgroundTissue Microarrays (TMAs) allow researchers to examine hundreds of small tissue samples on a single glass slide. The information held in a single TMA slide may easily involve Gigabytes of data. To benefit from TMA technology, the scientific community needs an open source TMA data exchange specification that will convey all of the data in a TMA experiment in a format that is understandable to both humans and computers. A data exchange specification for TMAs allows researchers to submit their data to journals and to public data repositories and to share or merge data from different laboratories. In May 2001, the Association of Pathology Informatics (API) hosted the first in a series of four workshops, co-sponsored by the National Cancer Institute, to develop an open, community-supported TMA data exchange specification.MethodsA draft tissue microarray data exchange specification was developed through workshop meetings. The first workshop confirmed community support for the effort and urged the creation of an open XML-based specification. This was to evolve in steps with approval for each step coming from the stakeholders in the user community during open workshops. By the fourth workshop, held October, 2002, a set of Common Data Elements (CDEs) was established as well as a basic strategy for organizing TMA data in self-describing XML documents.ResultsThe TMA data exchange specification is a well-formed XML document with four required sections: 1) Header, containing the specification Dublin Core identifiers, 2) Block, describing the paraffin-embedded array of tissues, 3)Slide, describing the glass slides produced from the Block, and 4) Core, containing all data related to the individual tissue samples contained in the array. Eighty CDEs, conforming to the ISO-11179 specification for data elements constitute XML tags used in the TMA data exchange specification. A set of six simple semantic rules describe the complete data exchange specification. Anyone using the data exchange specification can validate their TMA files using a software implementation written in Perl and distributed as a supplemental file with this publication.ConclusionThe TMA data exchange specification is now available in a draft form with community-approved Common Data Elements and a community-approved general file format and data structure. The specification can be freely used by the scientific community. Efforts sponsored by the Association for Pathology Informatics to refine the draft TMA data exchange specification are expected to continue for at least two more years. The interested public is invited to participate in these open efforts. Information on future workshops will be posted at http://www.pathologyinformatics.org (API we site).
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