ArrayExpress update—simplifying data submissions

Kolesnikov, N. N.; Hastings, Emma; Keays, Maria; Melnichuk, Olga; Tang, Y. Amy; Williams, Eleanor; Dylag, Miroslaw; Kurbatova, Natalja; Brandizi, Marco; Burdett, Tony; Mégy, Karyn; Pilicheva, Ekaterina; Rustici, Gabriella; Tikhonov, Andrew; Parkinson, Helen; Petryszak, Robert; Sarkans, Ugis; Brāzma, Alvis

doi:10.1093/nar/gku1057

Cited by 675 publications

(535 citation statements)

References 14 publications

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“…The microarray data and RNAseq data were collected from Gene Expression Omnibus (Edgar et al, 2002) and ArrayExpress (Kolesnikov et al, 2015). The qPCR data were extracted from research articles with sources listed in Supplemental Table S2.…”

Section: Collection Of Gene Expression Datamentioning

confidence: 99%

Regulation of Sucrose Transporters and Phloem Loading in Response to Environmental Cues

et al. 2017

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Suc transporters (SUTs) play a key role in the allocation and partitioning of photosynthetically fixed carbon in plants. While a function could be assigned to many members of the SUT family, almost no information is available on their regulation. Here, the transcriptional regulation of SUTs in response to various environmental stimuli in the leaves of five dicots (Arabidopsis Extensive data on expression of SUTs in relation to changes of environmental conditions were obtained through a global analysis of 168 transcriptomics data sets. Results were validated by quantitative PCR measurements and extended by the measurement of photosynthesis rate and phloem sugar content to draw insight on the correlation of SUT expression and sugar export from leaves. For the apoplasmic phloem loaders, a clear difference in transcriptional regulation in response to different environmental stimuli was observed. The consistent patterns of SUT expression under abiotic stress indicates which types of SUTs are involved in the regulation of leaf sugar status and in stress signaling. Furthermore, it is shown that down-regulation of phloem loading is likely to be caused by transcriptional regulation of SUTs, while up-regulation depends on post-transcriptional regulation. In poplar, expression of PtaSUT4 was found to consistently respond to environmental stimuli, suggesting a significant role in the regulation of sugar export from leaves in this passive symplasmic phloem loader.

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Section: Collection Of Gene Expression Datamentioning

confidence: 99%

Regulation of Sucrose Transporters and Phloem Loading in Response to Environmental Cues

et al. 2017

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“…We collected and manually annotated 3,434 microarray samples for 24 mouse hematopoietic lineage cells originating from 261 studies present in ArrayExpress (Materials and Methods and SI Appendix, Fig. S2 and Table S1) (1). These data contain samples from both unstimulated cells and cells treated with various stimuli.…”

Section: Gene Expression and Correlation Data In Mouse Hematopoieticmentioning

confidence: 99%

“…Over time, large amounts of such data have accumulated in public databases such as ArrayExpress and Gene Expression Omnibus (1,2). In addition to their original purpose, these datasets contain an enormous potential for the study of biological networks, such as signaling pathways and regulatory interactions.…”

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confidence: 99%

Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

Vandenbon

Dinh

Mikami

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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High-throughput gene expression data are one of the primary resources for exploring complex intracellular dynamics in modern biology. The integration of large amounts of public data may allow us to examine general dynamical relationships between regulators and target genes. However, obstacles for such analyses are studyspecific biases or batch effects in the original data. Here we present Immuno-Navigator, a batch-corrected gene expression and coexpression database for 24 cell types of the mouse immune system. We systematically removed batch effects from the underlying gene expression data and showed that this removal considerably improved the consistency between inferred correlations and prior knowledge. The data revealed widespread cell type-specific correlation of expression. Integrated analysis tools allow users to use this correlation of expression for the generation of hypotheses about biological networks and candidate regulators in specific cell types. We show several applications of Immuno-Navigator as examples. In one application we successfully predicted known regulators of importance in naturally occurring Treg cells from their expression correlation with a set of Treg-specific genes. For one high-scoring gene, integrin β8 (Itgb8), we confirmed an association between Itgb8 expression in forkhead box P3 (Foxp3)-positive T cells and Treg-specific epigenetic remodeling. Our results also suggest that the regulation of Tregspecific genes within Treg cells is relatively independent of Foxp3 expression, supporting recent results pointing to a Foxp3-independent component in the development of Treg cells.

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“…Tools for flexible data selection would permit the design of suitable coexpression analyses, before further assessments. Data collection in the form of a flat file exported from reference resources is widespread in bioinformatics, including for microarray data [22,50]. Data sharing between different resources could also be solved using web services-based approaches [51].…”

Section: Comparative Meta-analysis Of Results From Different Platformsmentioning

confidence: 99%

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

et al. 2016

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Bioinformatics web-based resources and databases are precious references for most biological laboratories worldwide. However, the quality and reliability of the information they provide depends on them being used in an appropriate way that takes into account their specific features. Huge collections of gene expression data are currently publicly available, ready to support the understanding of gene and genome functionalities. In this context, tools and resources for gene co-expression analyses have flourished to exploit the 'guilty by association' principle, which assumes that genes with correlated expression profiles are functionally related. In the case of Arabidopsis thaliana, the reference species in plant biology, the resources available mainly consist of microarray results. After a general overview of such resources, we tested and compared the results they offer for gene co-expression analysis. We also discuss the effect on the results when using different data sets, as well as different data normalization approaches and parameter settings, which often consider different metrics for establishing co-expression. A dedicated example analysis of different gene pools, implemented by including/excluding mutant samples in a reference data set, showed significant variation of gene co-expression occurrence, magnitude and direction. We conclude that, as the heterogeneity of the resources and methods may produce different results for the same query genes, the exploration of more than one of the available resources is strongly recommended. The aim of this article is to show how best to integrate data sources and/or merge outputs to achieve robust analyses and reliable interpretations, thereby making use of diverse data resources an opportunity for added value.

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ArrayExpress update—simplifying data submissions

Cited by 675 publications

References 14 publications

Regulation of Sucrose Transporters and Phloem Loading in Response to Environmental Cues

Regulation of Sucrose Transporters and Phloem Loading in Response to Environmental Cues

Immuno-Navigator, a batch-corrected coexpression database, reveals cell type-specific gene networks in the immune system

Gene co-expression analyses: an overview from microarray collections inArabidopsis thaliana

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