ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression

Parkinson, Helen; Kapushesky, Misha; Kolesnikov, N. N.; Rustici, Gabriella; Shojatalab, Mohammadreza; Abeygunawardena, Niran; Bérubé, Hugo; Dylag, Miroslaw; Emam, Ibrahim; Farne, Anna; Holloway, Ele; Lukk, Margus; Malone, James; Mani, Roby; Pilicheva, Ekaterina; Rayner, Tim; Rezwan, Faisal I.; Sharma, Ashish; Williams, Eleanor; Zheng-Bradley, Xiangqun; Adamusiak, Tomasz; Brandizi, Marco; Burdett, Tony; Coulson, Richard M. R.; Krestyaninova, Maria; Kurnosov, Pavel; Maguire, Eamonn; Neogi, Sudeshna Guha; Rocca-Serra, Philippe; Sansone, Susanna-Assunta; Sklyar, Nataliya; Zhao, Mengyao; Sarkans, Ugis; Brāzma, Alvis

doi:10.1093/nar/gkn889

Cited by 380 publications

(347 citation statements)

References 20 publications

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“…A total of 29 publicly available gene expression datasets comprising of 414 samples of the Affymetrix rice GeneChip from were collected from NCBI GEO (Barrett et al, 2009) and ArrayExpress (Parkinson et al, 2009). From these, 129 samples (45 groups) with a unifying biological theme, i.e.…”

Section: Coexpression Network Analysismentioning

confidence: 99%

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Krishnan

Gupta

Ambavaram

et al. 2017

Preprint

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Author ContributionsAK, designed the network, conducted statistical analysis, drafted the manuscript; CG, contributed text, updated figures, and created the webserver; MA, conducted rice drought and gene expression experiments; AP, designed experiments and coordinated research; all authors contributed to writing the manuscript. AbstractTranscriptional profiling is a prevalent and powerful approach for capturing the response of crop plants to environmental stresses, e.g. response of rice to drought. However, functionally . CC-BY-NC-ND 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/166694 doi: bioRxiv preprint first posted online 2 interpreting the resulting genome-wide gene expression changes is severely hampered by the large gaps in our genomic knowledge about which genes work together in cellular pathways/processes in rice. Here, we present a new web resource -RECoN -that relies on a network-based approach to go beyond currently limited annotations in delineating functional and regulatory perturbations in new rice stress transcriptome datasets generated by a researcher. To build RECoN, we first enumerated 1,744 stress-specific gene modules covering 28,421 rice genes (>72% of the genes in the genome). Each module contains a group of genes tightly coexpressed across a large number of environmental conditions and, thus, is likely to be functionally coherent. When a user provides a new differential expression profile, RECoN identifies modules substantially perturbed in their experiment and further suggests deregulated functional and regulatory mechanisms based on the enrichment of current annotations within the predefined modules. We demonstrate the utility of this resource by analyzing new drought transcriptomes of rice in three developmental stages, which revealed large-scale insights into the cellular processes and regulatory mechanisms involved in common and stage-specific drought responses. RECoN enables biologists to functionally explore new data from all abiotic stresses on a genome-scale and to uncover gene candidates, including those that are currently functionally uncharacterized, for engineering stress tolerance.

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Section: Coexpression Network Analysismentioning

confidence: 99%

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

Krishnan

Gupta

Ambavaram

et al. 2017

Preprint

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“…Development series (Schmid et al 2005), abiotic-stress series (Kilian et al 2007), light series, pathogen series and tiling array series (Laubinger et al 2008;Zeller et al 2009) microarray data were downloaded from the Weigel World web site (http://www.weigelworld.org/). Diurnal microarray data (Michael et al 2008;Mockler et al 2007) was downloaded from ArrayExpress (Accession Number: E-MEXP-1304, http:// www.ebi.ac.uk/arrayexpress/, Parkinson et al 2009). All other microarray data were downloaded from the AtGenExpressJPN web site (http://igrt0.psc.riken.jp/, Goda et al 2008).…”

Section: The Data and Software Implemented In Fioredbmentioning

confidence: 99%

The new FioreDB database provides comprehensive information on plant transcription factors and phenotypes induced by CRES-T in ornamental and model plants

Mitsuda¹,

Takiguchi²,

Shikata³

et al. 2011

Plant Biotechnology

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“…Both generic S. cerevisiae databases and methodology databases were queried, and three relevant databases were found: SGD, ArrayExpress [27] and Gene Expression Omnibus [28,29]. Of these, SGD was chosen as it contains references to more methodologies and lists a higher number of papers annotated with them.…”

Section: Other Resourcesmentioning

confidence: 99%

“…The three major goals of this repository are: to serve the scientific community as an archive for data supporting publications; to provide easy access to high-quality data in a standard format; and to facilitate the sharing of technical platforms, specifically microarray designs and experimental protocols [27].…”

Section: Arrayexpressmentioning

confidence: 99%

GREAT: Gene Regulation EvAluation Tool

Machado

Bastos

Couto

2009

Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living

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Mestrado em Tecnologias de Informação aplicadas às Ciências Biológicas e Médicas 2009Resumo A correcta compreensão de como funcionam os sistemas biológicos depende do estudo dos mecanismos que regulam a expressão genética. Estes mecanismos controlam em que momento e durante quanto tempo é utilizada a informação codificada num gene, e podem actuar em diversas etapas do processo de expressão genética. No presente trabalho, a etapa em análise é a transcrição, na qual a sequência de ADN de um gene é transformada numa sequência de ARN, que posteriormente dará origem a uma proteína.A regulação da transcrição centra-se na acção de uma classe de proteínas reguladoras denominadas factores de transcrição. Estes ligam-se à cadeia de ADN na região próxima do início de um gene (a região promotora), potenciando ou inibindo a ligação da proteína responsável pelo processo de transcrição. AbstractThe understanding of biological systems is dependent on the study of the mechanisms that regulate gene expression. These mechanisms control when and for how long the information coded in a gene is used, and can act on several of the steps in the gene expression process. In the present work, the step of interest is the transcription, where the DNA sequence of a gene is transformed into an RNA sequence, which will later be used to synthesise a protein.The knowledge about gene regulations is mainly available in the literature.Although there are currently multiple public biological databases, the majority of those contain data on biological entities but not explicitly on gene regulations.In order to provide the scientific community with data on Saccharomyces cerevisiae transcription regulations, a Portuguese public repository maintained by manual curation of scientific literature, named Yeastract, was created.Due to the increasing amount of papers published nowadays, the development of automatic tools that can help the curation process is of great importance. In the specific case of Yeastract, a tool was needed to help in the identification of papers describing gene regulations of S. cerevisiae. This tool was created with two components: one that identifies transcription factors in the papers' abstracts and verifies if they describe gene regulations; the other that evaluates if the hypothetical regulations the paper contains correspond to valid regulations from a biological point of view. This second component was named GREAT, Gene Regulation EvAluation Tool, and is the goal of my work.The tool I developed uses data obtained exclusively from public biological databases to validate the regulations. That data is used in the evaluation of three aspects:the participation of a gene and a transcription factor in the same biological process; the existence of the transcription factor binding motif in the gene promoter region; the experimental method with which the regulation was identified. The output of these features is used by a machine learning method, either regression or model trees, to calculate a confidence score to attribute to each putat...

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ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression

Cited by 380 publications

References 20 publications

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

RECoN: Rice Environment Coexpression Network for Systems Level Analysis of Abiotic-Stress Response

The new FioreDB database provides comprehensive information on plant transcription factors and phenotypes induced by CRES-T in ornamental and model plants

GREAT: Gene Regulation EvAluation Tool

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