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Fly (Austin). 2010 Oct-Dec; 4(4): 344–348.
Published online 2010 October 1. doi:  10.4161/fly.4.4.13303
PMCID: PMC3174485

The FLIGHT Drosophila RNAi database

2010 update

Abstract

FLIGHT (http://flight.icr.ac.uk/) is an online resource compiling data from high-throughput Drosophila in vivo and in vitro RNAi screens. FLIGHT includes details of RNAi reagents and their predicted off-target effects, alongside RNAi screen hits, scores and phenotypes, including images from high-content screens. The latest release of FLIGHT is designed to enable users to upload, analyze, integrate and share their own RNAi screens. Users can perform multiple normalizations, view quality control plots, detect and assign screen hits and compare hits from multiple screens using a variety of methods including hierarchical clustering. FLIGHT integrates RNAi screen data with microarray gene expression as well as genomic annotations and genetic/physical interaction datasets to provide a single interface for RNAi screen analysis and datamining in Drosophila.

Key words: RNAi, database, integration, bioinformatics, phenotype

Introduction

RNA interference (RNAi) is an evolutionarily conserved mechanism of post-transcriptional gene silencing. Triggering of the RNAi pathway by short double-stranded RNAs leads to the degradation of homologous mRNA within cells.1 Thus, RNAi can elicit a loss-of-function phenotype for any gene, specified by sequence, making it a powerful tool for systematic functional profiling on a genomic scale. Since the potential to perform high-throughput screens in Drosophila cell culture was first demonstrated,2,3 RNAi has become a widely used experimental tool, with over one hundred Drosophila in vitro screens completed to date, examining a wide variety of phenotypes. Furthermore, it is now possible to perform in vivo RNAi in Drosophila,4,5 offering the potential to screen for a huge range of complex phenotypes.

This article describes an updated version of the FLIGHT RNAi database6 that catalogs phenotypic data, including images, from in vitro and in vivo RNAi screens. FLIGHT integrates RNAi screen phenotypes with genomic, gene expression and physical and genetic interaction data to facilitate data-mining and hypothesis generation. The database also provides the opportunity for users to upload their own RNAi screen scores for normalization, analysis, integration and sharing. FLIGHT continues to complement other online Drosophila RNAi resources such as FlyRNAi,7 and GenomeRNAi.8 The FLIGHT database can be accessed at http://flight.icr.ac.uk/.

Data Access

FLIGHT is organized into a number of sections, accessible by a set of tabs at the top of the page. Within the ‘Browse’ section users can access lists of RNAi screens, RNAi libraries, microarray gene expression datasets and Drosophila cell lines. By selecting a particular screen of interest the user can drill down to a see a list of all hits, and subsequently the phenotypic data for an individual hit, including scores, annotation and in some cases images. Where high-content RNAi screen images are available users can interact with the images directly, adjusting the brightness and contrast and viewing different channels separately or in combination (Fig. 1C).

Figure 1
(A) Screenshot of the FLIGHT gene page for the Drosophila Rac1 gene. Boxes highlight links to Gbrowse, RNAi phenotypes, gene expression profiles and interaction maps. (B) GBrowse representation of the Rac1 locus including RNAi reagents and microarray ...

In the ‘Search’ section, users can search for a gene, RNAi reagent, phenotype or cell line of interest. Searching by gene (using a symbol or FlyBase9 identifier) will return the appropriate FLIGHT gene page (Fig. 1A). By default the RNAi experiments in which reagents targeting the selected gene were screened are displayed, including links to the phenotypic information available for each screen (Fig. 1C). This page contains links at the top and bottom that provide users with access to a range of gene-specific data. The gene link displays a GBrowse10 representation of the gene structure and the regions targeted by RNAi reagents and microarray gene expression probes (Fig. 1B). The ‘Expression’ link provides access to graphical plots of gene expression data from several different gene expression studies on different microarray platforms (Fig. 1D). The protein link gives the sequences of all UniProt11 proteins for a particular gene. The InterPro12 and GO13 links detail lists of protein domains and gene ontology annotations associated with the gene. The homology link identifies the known homologs in three different homology datasets, Homologene,14 Ensembl Compara15 and Inparanoid.16 The interactions link reveals a network map of all the known physical and genetic interactions for the gene (Fig. 1E). The RNAi reagents link provides a list of all of the reagents with the specified gene as a computed target. From this page one can drill down and view the amplicon sequence, transcript coverage and predicted off-target effects and CAN repeats17,18 for each reagent. Finally, the ‘Alleles’ link displays a table of all the mutant alleles of a gene found in FlyBase9 and their associated phenotypes.

Batch searches.

FLIGHT has been designed to allow users to analyze the results of high-throughput experiments in the context of other large-scale datasets. Thus, searches can be carried out using lists of genes and gene lists generated within the database can be saved and combined. Batch searches are available for gene annotations, RNAi screen hits, microarray gene expression scores, gene homologs and interactions. A gene list resulting from one search can be used as the basis for any batch search, using the ‘Link’ function. For example, a user interested in Rho GTPases could start by searching the database with a relevant InterPro motif (e.g., IPR003578) to call up a list of Drosophila Rho GTPases. Using the ‘Link’ function the user can then view which of the GTPases have been identified as hits in published RNAi screens. The results of all batch searches can be saved to a tab-delimited text file by clicking the ‘Download’ button at the foot of the table or on the left hand menu (for multi-page tables). Registered users can save the list of genes from any batch search to their account. Saved lists can be edited, used to create sub-lists, combined (union or intersection) or subtracted in the ‘Account’ section. They can also be used to search the database using the ‘Link’ function.

Data Submission and Analysis

A new function in FLIGHT is the ability for registered users to submit and analyze score-based RNAi screens. To submit a screen, users must first create a project and ensure that the screening cell line is described in the database (if not it can be added). Users must then provide minimal information about the screening assay and format. Screen results (raw scores) can be linked to either FlyBase gene identifiers, or to RNAi reagents in the database. If the user has screened using a library that is not in the database then they can upload details of their library. Users must upload a unique identifier and the dsRNA primer sequences for each reagent in a standard Microsoft Excel format, templates for which are provided. The primers are then aligned to the latest build of the Drosophila genome using BLAT19 to identify all possible amplicons. Each amplicon is then analyzed for sequence-specific off-target effects using BowTie20 to identify stretches of 19 nucleotide sequence identity.17,18,21,22 When a library or screen is uploaded it is submitted to a processing queue. Users are informed by e-mail when a processing job is completed. It is also possible to follow job progress in real time using the ‘View Jobs’ page in the ‘Account’ section.

Normalization and hit detection.

Once screen results have been uploaded, scores can be normalized in the ‘Analysis’ section using any of the normalization algorithms available in the cellHTS2 R-package.23 Using a simple online interface, users can normalize multiple screens at the same time and can perform multiple different normalizations on the same screen. The normalized scores are uploaded to the database for future analysis and an HTML quality control report is generated and storedpermanently on the FLIGHT server.

Once the normalization and quality control steps have been performed users can plot the data distribution to identify and assign screen hits based on the ranking of a single parameter. Both raw and normalized screen scores can be exported from the database in tab-delimited text format at any time.

Phenotype and hit comparison.

In recent years, phenotypic clustering has proved useful within individual RNAi screens in C. elegans24,25 and comparison of screens in multiple cells lines has proven informative in Drosophila.26 One of the aims of FLIGHT is to facilitate the comparison of data from multiple RNAi screens. Consequently, the ‘Analyse’ section contains several tools for screen comparison. Firstly, scores from two related screens can be compared directly. Secondly scores from multiple screens can be compared in a global pair-wise manner as a set of scatter plots (Fig. 2). Thirdly, score distributions from multiple screens can be compared using a boxplot. Finally, scores or hits from multiple screens can be compared by hierarchical clustering.

Figure 2
Screenshot of the FLIGHT ‘Analyse’ section showing a scatter plot comparison of scores from four related RNAi screens.

When clustering genes using FLIGHT, users can apply a range of standard distance measures (Euclidian, Minkowski, Manhattan, Canberra, binary, maximum) and tree-construction algorithms (Ward, McQuitty, single, complete, average, median, centroid) across one or more RNAi screens. FLIGHT then generates an interactive, color-coded dendrogram depicting the clustering results. As genes with similar RNAi phenotypes tend to be functionally related,3 clusters of genes generated using these phenotypic profiles may represent functional groups.

Data sharing.

Registered users can create their own user groups to share unpublished data securely with collaborators. Users can create groups in the account section and invite colleagues to join by simply entering their e-mail addresses. Once the invitee has registered with FLIGHT they will then be able to join the group. Users who upload private datasets for normalization and analysis can control access rights to those datasets at a project level, granting different levels of access to individual users or groups. Furthermore, once a screen is published it can be made publically accessible in FLIGHT, using the ‘Publish’ button in the Submit section.

Database contents.

FLIGHT is continually updated but currently includes a total of 20,886 RNAi hits from 105 screens in Drosophila melanogaster curated from the scientific literature. It contains normalized microarray expression information for 24 fly cell lines (Sims D & Baum B, unpublished), including those most commonly used in RNAi screens (S2, S2R+, Kc, BG2) and details of over 73,000 Drosophila RNAi reagents. Finally, FLIGHT includes protein interactions from BioGRID,27 Reactome28 and InAct,29 along with genetic interactions from FlyBase.9

Outlook

FLIGHT is designed to meet the increasing need to manage, navigate and cross-correlate data from the rapidly increasing number of Drosophila RNAi screens. In addition, FLIGHT is designed to facilitate the cross-referencing of results from largescale RNAi screens with data from molecular profiling and functional genomic datasets. We aim to increase the range of datasets within FLIGHT, by adding not only new RNAi screens and microarray gene expression datasets, but also next generation sequencing (RNA-Seq) and genetic regulatory element datasets. We plan to expand the interaction datasets by adding interactions inferred from interactions of homologous protein in other species (interologs) and associations from literature co-citation. Finally, the shortage of pathway information in Drosophila has prohibited pathway analysis that has been informative in mammalian studies. Therefore, we plan to add pathway data28 and pathway analysis tools to FLIGHT.

Acknowledgements

We acknowledge funding from Breakthrough Breast Cancer and NHS funding to the NIHR Biomedical Research Centre. Funding to pay the Open Access publication charges for this article was provided by Breakthrough Breast Cancer.

Abbreviations

RNAi
RNA interference
dsRNA
double-stranded RNA

Footnotes

References

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