The Image Data Resource: A Bioimage Data Integration and Publication Platform.
Ontology highlight
ABSTRACT:
SUBMITTER: Williams E
PROVIDER: S-EPMC5536224 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundData integration is an escalating problem in bioinformatics. We have developed a web tool and warehousing system, Booly, that features a simple yet flexible data model coupled with the ability to perform powerful comparative analysis, including the use of Boolean logic to merge datasets together, and an integrated aliasing system to decipher differing names of the same gene or protein. Furthermore, Booly features a collaborative sharing system and a public repository so that users can retrieve new datasets while contributors can easily disseminate new content.ResultsWe illustrate the uses of Booly with several examples including: the versatile creation of homebrew datasets, the integration of heterogeneous data to identify genes useful for comparing avian and mammalian brain architecture, and generation of a list of Food and Drug Administration (FDA) approved drugs with possible alternative disease targets.ConclusionsThe Booly paradigm for data storage and analysis should facilitate integration between disparate biological and medical fields and result in novel discoveries that can then be validated experimentally. Booly can be accessed at http://booly.ucsd.edu.
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Project description:Unlabelled: The public availability of high throughput molecular data provides new opportunities for researchers to advance discovery, replication and validation efforts. One common challenge in leveraging such data is the diversity of measurement approaches and platforms and a lack of utilities enabling cross-platform comparisons among data sources for analysis. We present a method to map DNA methylation data from bisulfite sequencing approaches to CpG sites measured with the widely used Illumina methylation bead-array platforms. Correlations and median absolute deviations support the validity of using bisulfite sequencing data in combination with Illumina bead-array methylation data.Availability and implementationhttps://github.com/Christensen-Lab-Dartmouth/methyLiftover includes source, documentation and data references.Contactbrock.c.christensen@dartmouth.eduSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundModern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis.ResultsWe have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de.ConclusionsDIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.
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Project description:The size and scope of microarray experiments continue to increase. However, datasets generated on different platforms or at different centres contain biases. Improved techniques are needed to remove platform- and batch-specific biases. One experimental control is the replicate hybridization of a subset of samples at each site or on each platform to learn the relationship between the two platforms. To date, no algorithm exists to specifically use this type of control. LTR is a linear-modelling-based algorithm that learns the relationship between different microarray batches from replicate hybridizations. LTR was tested on a new benchmark dataset of 20 samples hybridized to different Affymetrix microarray platforms. Before LTR, the two platforms were significantly different; application of LTR removed this bias. LTR was tested with six separate data pre-processing algorithms, and its effectiveness was independent of the pre-processing algorithm. Sample-size experiments indicate that just three replicate hybridizations can significantly reduce bias. An R library implementing LTR is available.
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Project description: Not available
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Project description:MotivationCombining gene expression (GE) profiles generated from different platforms enables previously infeasible studies due to sample size limitations. Several cross-platform normalization methods have been developed to remove the systematic differences between platforms, but they may also remove meaningful biological differences among datasets. In this work, we propose a novel approach that removes the platform, not the biological differences. Dubbed as 'MatchMixeR', we model platform differences by a linear mixed effects regression (LMER) model, and estimate them from matched GE profiles of the same cell line or tissue measured on different platforms. The resulting model can then be used to remove platform differences in other datasets. By using LMER, we achieve better bias-variance trade-off in parameter estimation. We also design a computationally efficient algorithm based on the moment method, which is ideal for ultra-high-dimensional LMER analysis.ResultsCompared with several prominent competing methods, MatchMixeR achieved the highest after-normalization concordance. Subsequent differential expression analyses based on datasets integrated from different platforms showed that using MatchMixeR achieved the best trade-off between true and false discoveries, and this advantage is more apparent in datasets with limited samples or unbalanced group proportions.Availability and implementationOur method is implemented in a R-package, 'MatchMixeR', freely available at: https://github.com/dy16b/Cross-Platform-Normalization.Supplementary informationSupplementary data are available at Bioinformatics online.
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