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DGET Documentation

 

URL

http://www.flyrnai.org/tools/dget/web

 

 

DGET- Drosophila Gene Expression Tool

DGET Overview

DGET was designed to facilitate the retrieval and analysis of RNA-Seq based expression data in a high-throughput fashion.  The tool can be compared with expression features at FlyMine and FlyBase. FlyMine provides array-based expression data for high-throughput analysis. FlyBase provides both array-base and RNA-Seq based expression data one gene a time. As a supplement to FlyMine and FlyBase, DGET allows batch searches, filtration and enrichment analyses for a list of genes. In addition, at DGET users can search for genes with similar or opposite expression profiles and can build a synexpression network using any of the datasets.

Cite DGET

A preprint version of the DGET manuscript is available at http://biorxiv.org/content/early/2016/09/15/075358

Datasets currently covered at DGET

  1. Cell Lines, by modEncode

Cherbas, L. et al (2011). "The transcriptional diversity of 25 Drosophila cell lines." Genome Res 21(2): 301-314.

  1. Developmental Stages, by modEncode

Graveley, B. R. et al (2011). "The developmental transcriptome of Drosophila melanogaster." Nature 471(7339): 473-479.

  1. Tissues, by modEncode

Graveley, B. R. et al (2011). "The developmental transcriptome of Drosophila melanogaster." Nature 471(7339): 473-479.

  1. Treatment, by modEncode

Graveley, B. R. et al (2011). "The developmental transcriptome of Drosophila melanogaster." Nature 471(7339): 473-479.

  1. Gut sub-regions, by Spradling lab

Marianes, A. and A. C. Spradling (2013). "Physiological and stem cell compartmentalization within the Drosophila midgut." Elife 2: e00886.

  1. Gut cell types and sub-regions, by Buchon lab

Dutta, D. et al (2015). "Regional Cell-Specific Transcriptome Mapping Reveals Regulatory Complexity in the Adult Drosophila Midgut." Cell Rep 12(2): 346-358.

Data visualization

  1. Heatmap view

Plotting of heat-maps for svg download is done in R using the gplot heatmap function.

  1. Network view

Plotting of network is done using Cytoscape and a JSON representation of the network is sent with the html. The nodes are input genes while the edges are drawn for gene pairs with Pearson correlation score that meets user defined cut-off

 

Please use our bug report form to ask questions or make suggestions.

 

Publication to cite and for more information

  • Yanhui Hu, Aram Comjean, Norbert Perrimon, and Stephanie E Mohr. 2/10/2017. “The Drosophila Gene Expression Tool (DGET) for expression analyses.” BMC Bioinformatics, 18, 1, Pp. 98.Abstract
    2017_BMC Bioinform_Hu.pdf
    BACKGROUND: Next-generation sequencing technologies have greatly increased our ability to identify gene expression levels, including at specific developmental stages and in specific tissues. Gene expression data can help researchers understand the diverse functions of genes and gene networks, as well as help in the design of specific and efficient functional studies, such as by helping researchers choose the most appropriate tissue for a study of a group of genes, or conversely, by limiting a long list of gene candidates to the subset that are normally expressed at a given stage or in a given tissue. RESULTS: We report DGET, a Drosophila Gene Expression Tool ( www.flyrnai.org/tools/dget/web/ ), which stores and facilitates search of RNA-Seq based expression profiles available from the modENCODE consortium and other public data sets. Using DGET, researchers are able to look up gene expression profiles, filter results based on threshold expression values, and compare expression data across different developmental stages, tissues and treatments. In addition, at DGET a researcher can analyze tissue or stage-specific enrichment for an inputted list of genes (e.g., 'hits' from a screen) and search for additional genes with similar expression patterns. We performed a number of analyses to demonstrate the quality and robustness of the resource. In particular, we show that evolutionary conserved genes expressed at high or moderate levels in both fly and human tend to be expressed in similar tissues. Using DGET, we compared whole tissue profile and sub-region/cell-type specific datasets and estimated a potential source of false positives in one dataset. We also demonstrated the usefulness of DGET for synexpression studies by querying genes with expression profile similar to the mesodermal master regulator Twist. CONCLUSION: Altogether, DGET provides a flexible tool for expression data retrieval and analysis with short or long lists of Drosophila genes, which can help scientists to design stage- or tissue-specific in vivo studies and do other subsequent analyses.