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Resources for Functional Genomics Studies in Drosophila melanogaster [Genetics]
[May 31, 2014]

Resources for Functional Genomics Studies in Drosophila melanogaster [Genetics]


(Genetics Via Acquire Media NewsEdge) ABSTRACT Drosophila melanogaster has become a system of choice for functional genomic studies. Many resources, including online databases and software tools, are now available to support design or identification of relevant fly stocks and reagents or analysis and mining of existing functional genomic, transcriptomic, proteomic, etc. datasets. These include large community collections of fly stocks and plasmid clones, "meta" information sites like FlyBase and FlyMine, and an increasing number of more specialized reagents, databases, and online tools. Here, we introduce key resources useful to plan large-scale functional genomics studies in Drosophila and to analyze, integrate, and mine the results of those studies in ways that facilitate identification of highest-confidence results and generation of new hypotheses. We also discuss ways in which existing resources can be used and might be improved and suggest a few areas of future development that would further support large- and small-scale studies in Drosophila and facilitate use of Drosophila information by the research community more generally.



THE availability of powerful genetic approaches and a well-annotated genome sequence have made Drosophila melanogaster a model system of choice for a wide range of functional genomic studies. Researchers are increasingly dependent upon online resources for navigation and management of the rapidly growing datasets associated with large-scale "'omics" and other Drosophila studies. As a veritable encyclopedia of fly gene information, the online FlyBase resource is an essential online tool, providing access to curated information and resources for individual genes in an accessible format (McQuilton et al. 2012; St Pierre et al. 2014). However, throughout the course of many studies, such as large-scale screens or proteomics analyses, researchers need to turn to many other online resources for search, analysis, integration, and visualization of new or existing data. As a result, the use of a variety of software tools and online resources has become integral to Drosophila functional genomics studies, contributing to everything from experimental design to interpretation of results. Here, we present an overview of freely available noncommercial resources for functional genomics approaches in Drosophila; provide advice on how the associated online tools can be used by biologists and bioinformaticians; explore some of the roadblocks that prevent easy navigation; and suggest what changes or new resources might be of highest priority in the future. Although our focus will be on tools relevant to D. melanogaster, a number of the resources we discuss are relevant to other Drosophila and noninsect species.

Most online informatics tasks relevant to functional genomics can be divided into three basic categories. First, online resources can be used to identify or design reagents useful for a particular experimental approach, including fly stocks (Table 1), cell and molecular reagents (Table 2), and genome engineering resources (Table 3). Second, online resources that support search and view of existing information (Table 4) and enrichment approaches (Table 5) can be used to narrow down a list of candidate genes, such as to compare experimental vs. predicted results or to home in on the highest confidence results following a large-scale study. Third, a similar comparison of gene or protein lists to existing data in the curated literature or from 'omics datasets can be used to expand a list of candidate genes, such as to include all genes or proteins for which existing data suggest coexpression, binary interaction, cocomplex or colocalization, all genes encoding proteins that share a common domain or biochemical function, or all genes that share a common phenotype or predicted function (Table 4 and Table 5). Additional more specialized tasks include identification of related genes in other species, including fly genes related to human genes that have been implicated in disease (Table 5) and identification of predicted gene targets of miRNAs (Table 6).


"Meta" Sites for Fly Gene Annotation Information A few key resources include information from many sources, making these metasites generally useful for many tasks. FlyBase (flybase.org) is a comprehensive database of fly gene information (McQuilton et al. 2012; St Pierre et al. 2014). FlyBase is the go-to source for information about specific genes, including gene annotation, mutant alleles and their phenotypes, highthroughput expression data, and publications. Curation of information by experts helps to ensure quality. Methods for querying data in FlyBase have recently been presented elsewhere (Wilson et al. 2008; McQuilton et al. 2012; St Pierre et al. 2014). The National Center for Biotechnology Information (NCBI) gene database (ncbi.nlm.nih.gov/gene) (2013) and Ensemble (ensembl.org/Drosophila_melanogaster/Info/Index) (Flicek et al. 2013) from the EMBL and Wellcome Trust Sanger Institute are also major repositories of gene annotation information, including for Drosophila. It should be noted, however, that gene annotations at these sites are not independent; they are imported from FlyBase. Users might see differences between NCBI or Ensemble or FlyBase when the former sites are out of synch with the current FlyBase release. The metasite FlyMine (flymine.org) provides access to overlapping sets of information as compared with these other resources (Lyne et al. 2007). FlyMine has the added advantage of user-friendly interfaces for handling and viewing information regarding long lists of genes, but particularly in terms of reagent information, it is less comprehensive than FlyBase. In addition, .1000 genome-scale datasets from the modENCODE project are available for download online (see data.modencode.org/? Organism=D.%20melanogaster). Gene annotations will continue to change over time as these and other datasets are further analyzed and incorporated at FlyBase.

Identification of Existing Reagents Centralized fly stock and reagent collections A number of facilities in the United States and overseas have collected resources from large numbers of laboratories, creating centralized distribution centers. These include fly stock collections at the Bloomington Drosophila Stock Center (BDSC; classical alleles, mobile element insertions, Gal4 and upstream activation sequence (UAS) stocks, RNAi fly stocks, etc.) (Cook et al. 2010a), Vienna Drosophila RNAi Center (VDRC; RNAi fly stocks, Vienna Tiles Gal4 stocks, etc.), Drosophila Genetic Resource Center-Kyoto Stock Center [Kyoto PiggyBac, FlyTrap, and Cambridge protein trap insertion (CPTI) fluorescent trap stocks, Gal4 stocks, etc.] (Yamamoto 2010) and National Institute of Genetics-Japan (NIG-Japan; Cas9 fly stocks, RNAi fly stocks, etc.) (for URLs see Table 1). Centralized centers also include the San Diego Drosophila Species Stock Center and EHIME-Fly at EHIME University, which maintain collections of non-melanogaster Drosophila fly stocks (for URLs see Table 1), and the Drosophila Genomic Resource Center (DGRC), which distributes D. melanogaster resources such as cultured cells, plasmid vectors, cDNAs, etc. (Table 2). In addition, several large-scale and consortium efforts have led to the development of large-scale collections made accessible through their own websites and/or at existing distribution centers (Table 1 and Table 2).

Major categories of in vivo fly stocks Fly stocks useful for functional genomic studies (Table 1) can be generally grouped into (1) those in which the endogenous gene is mutated, disrupted, or tagged directly, e.g., classical alleles, deletions, Gene Disruption Project (GDP) transposon insertions, and "protein trap" lines (Morin et al. 2001; Bellen et al. 2004, 2011; Kelso et al. 2004; Buszczak et al. 2007; Cook et al. 2010a, 2012; Venken et al. 2011; Singari et al. 2014), and (2) those for which transgenesis into a neutral or unrelated location is used to introduce an exogenous reporter construct, expression driver, reagent, etc. (reviewed in Cook et al. 2010a; Perrimon et al. 2010). Some fly stocks, such as GDP MiMIC element insertions, fall into both categories, as they can be useful as mutant alleles and/or used to develop reporter constructs, protein fusions, etc. (Venken et al. 2011). GDP and other efforts have been focused on complete coverage at the chromosome region or gene level, such as nearly complete overlapping coverage by deletion constructs (Cook et al. 2010b, 2012) and generation of at least one disruption (GDP) or RNAi construct [VDRC, Transgenic RNAi Project (TRiP), NIG-Japan] per gene (reviewed in Perrimon et al. 2010). It is conceivable that future development will include targeted disruption of specific gene isoforms. The FlyFos collection fly stocks (transgeneome. mpi-cbg.de) facilitate RNAi "rescue" approaches using D. pseudoobscura genomic fragments (see below and Langer et al. 2010). Overexpression of open reading frames (ORFs) using the Gal4-UAS system is supported by fly stock collections at the University of Zurich in Switzerland (FlyORF; Bischof et al. 2013) and the National Centre for Biological Sciences in India [Drosophila protein interaction map (DPiM) fly stocks; Guruharsha et al. 2011]. Recent areas of growth include the large number of newly generated Gal4 fly stocks, e.g., the Janelia Farm (Pfeiffer et al. 2008) and Vienna Tiles (Jenett et al. 2012) collections, and the Drosophila Genetic Reference Panel (DGRP) for population genomics and analysis of quantitative traits (Mackay et al. 2012). Fly stocks for interrogation of miRNA function have also been an area of recent development; fly stocks, reagents, and online tools related to miRNAs are presented in a later section.

Other physical resources and reagents Resources useful for functional genomics approaches include fly stocks, cultured cell lines, cell-based RNAi libraries, and cDNA or ORF clones, as well as online tools useful for the design of RNAi reagents, quantitative real-time PCR (qPCR) primers, or clustered regularly spaced short palindromic repeat (CRISPR) short guide (sg) RNAs (Table 2 and Table 3). The study of protein function in particular is reliant on the availability of appropriate resources, namely, high-quality plasmid clones that contain the ORFs in a format compatible with protein expression. The "Gold Collection" built by Berkeley Drosophila Genome Project (BDGP) using cDNA libraries from a variety of tissues and developmental stages (Stapleton et al. 2002) comprises 11,000 sequence-verified full-length cDNA clones representing 10,300 genes (74% of predicted D. melanogaster genes). To facilitate high-throughput subcloning, as well as ensure maximum flexibility, such as different tagging options and expression levels, it is important to use recombinational cloning vectors and include only the coding sequences, with all untranslated sequences removed. Successful large-scale use of the BDGP ORF collections was demonstrated in the DPiM project (Guruharsha et al. 2011). In this large-scale study, 4273 "bait" ORFs were cloned into a vector that fuses the ORF to a carboxyl-terminal FLAG-HA tag cassette under the control of a metallothionein promoter. Each clone was transiently transfected into S2R+ cells. The associated protein complexes were then affinity purified and detected by mass spectrometry analysis, which led to the generation of a map encompassing 556 protein complexes.

cDNA, ORF, and genomic clones for functional genomics Gold collection clones are available for search or order from the Drosophila Genomics Resource Center (Table 2). Currently most recombinational ORFs are in CloneTech Creator system vectors, with some smaller collections in Invitrogen Gateway cloning system vectors. The UAS-ORF collections generated by the Basler group are in a barcoded vector, pUASg.attB, for stop codon-containing clones, and pUASg- HA.attB, for ORFs with HA tags at their C-terminal ends. Using these clones together with the site-specific PhiC31 integrase, the group created a collection of 1149 UAS-ORF transgenic fly strains for in vivo overexpression screens (Bischof et al. 2013). Relevant to RNAi studies is the availability of genomic clones from non-melanogaster species that can be used for RNAi rescue approaches, as these distant species' genes are often different enough at the nucleotide level to evade RNAi but similar enough at the regulation and protein sequence level so as to confer function. Available collections include a D. persimilis library distributed by the DRSC (flyrnai. org/cgi-bin/RNAi_find_rescue_compl.pl) useful for building constructs for testing in cells or in vivo (Kondo et al. 2009) and as mentioned above, the FlyFos D. pseudoobscura collection as described in Ejsmont et al. (2009) and Langer et al. (2010).

Online tools for reagent search and information As shown in Table 1 and Table 2, most large-scale reagent collections are supported by one or more online site for search, view, and request of reagents. For most resources, it is easiest (and in some cases, only possible) to search for information related to one gene at a time. Moreover, the sites often support search of only one type or collection of reagents. For large-scale studies, however, it can often be easier to search in batch mode, as well as to search all available reagents of a type rather than only a specific collection. Another practical option for functional genomics studies, where long lists of genes are being managed, is to have the ability to download results in tab-delimited or similar file formats that are compatible with Excel and other spreadsheet or database software programs. Although FlyBase provides access to information about many different reagent collections, it is not easy for the novice user to search with more than one gene at a time or limit searches to a particular type of resource (for help with FlyBase searches see McQuilton et al. 2012; St Pierre et al. 2014). Conversely, although the metasite FlyMine (Lyne et al. 2007) makes it relatively easy to create and view information pertaining to long lists of genes, FlyMine appears to capture only limited information about available fly stocks and other reagents.

Identification of RNAi reagents To facilitate the identification of cell-based and in vivo RNAi reagents, we recently developed UP-TORR (updated targets of RNAi reagents). UP-TORR facilitates search with more than one gene identifier at a time, allows for simultaneous search of major public fly RNAi reagent collections [Drosophila RNAi Screening Center (DRSC), German Cancer Research Center (DKFZ), TRiP, VDRC, and NIG-Japan], and gives the option to download results as an Excel-compatible file. UP-TORR also addresses another common problem with reagent and other online search tools, that is, the need to keep up to date with new gene annotation releases, including changes to gene identifiers, gene names, and synonyms. Keeping up with new gene annotations is particularly important for RNAi reagents, for which the predicted targets and offtargets can change significantly over time, but the issue is generally relevant as well. Indeed, over time, many sites get out of synch with updates to FlyBase identifiers (FBgns), gene symbols, etc., such that searches can produce false negative and/or false positive results. Ideally, all sites would build automated detection and import of updates by FlyBase or other relevant information sources into their online tools, such that those tools automatically keep up to date. However, this is not a trivial task and still requires maintenance over time, as the information sources are likely to add to or change the format of data releases, such that the automated approach would have to be revised. One practical approach researchers can use to address the problem that different sites are built around different FlyBase releases is to process input and output lists through a user account at FlyMine. FlyMine detects "retired" gene symbols or identifiers in a list and allows the user to update a list based on suggested new identifiers, both at the time a list is created and when a registered user signs in and views a list already saved to his or her account. Another useful tool is FlyBase's Upload/Convert IDs tool (flybase.org/static_pages/downloads/IDConv. html).

Recent trends among online tools for reagent search and information Two recent trends in the organization and curation of Drosophila reagents are notable. First, groups such as the BDSC and TRiP are making efforts to highlight fly stocks that may be particularly relevant to the study of human disease (for URLs see Table 1). A recent review by Chen and Crowther (2012) highlights online tools useful for disease-related functional genomics. Second, an increasing number of online resources are soliciting direct contribution of information by the community. FlyBase has a long history of accepting personal communications and recently, FlyBase and other sites have begun to provide additional opportunities for direct contributions as a supplement to curated information. For example, FlyBase has links to FlyGene Wiki (see "User Contributed Data" under "Summaries" at a FlyBase gene page). Specific to reagent resources, we recently created online sites that allow users to input information about reagent quality, namely, the RNAi Stock Validation and Phenotypes (RSVP) searchable online database of quality information (qPCR and phenotypes) for RNAi fly stocks (flyrnai.org/rsvp), and FlyPrimer- Bank, which provides sequences and information regarding primer pairs for qPCR analysis (flyrnai.org/flyprimerbank) (Hu et al. 2013b). At the moment, much of the reagent information in RSVP and FlyPrimerBank is from our own internal studies or curated from the literature. It remains to be seen if direct contribution by individual labs or researchers will become a significant source of information at these sites. One reason for optimism regarding direct contribution of information about reagents in particular is the fact that providing negative information (e.g., an experimentally validated lack of RNAi knockdown), which researchers might be more willing to part with prepublication, can initiate renomination of a gene for design and production of alternative reagents, providing a significant incentive for submission of the data. It will be interesting to watch over time if it proves both practical and worthwhile to move away from curation by others and toward direct contribution, either for specific types of information or more generally.

The Rapid Emergence of Resources for Genome Engineering CRISPR system and resources Recently emerging technologies for genome engineering such as CRISPR/Cas9 and transcription activator-like effector nucleases (TALENs) were quickly adapted to knockout or tag genes in Drosophila. For example, several groups have demonstrated the efficacy of the CRISPR/Cas9 method for in vivo modification of Drosophila (Bassett et al. 2013a; Gratz et al. 2013; Kondo and Ueda 2013; Ren et al. 2013; Sebo et al. 2013; Yu et al. 2013). These initial reports suggest that off-target (OT) issues are not much of a concern in Drosophila provided that the sgRNA sequence is selected carefully.We implemented a genome-wide CRISPR resource annotating all possible CRISPR designs (flyrnai.org/crispr) (Ren et al. 2013). This resource visually displays all possible CRISPR designs using a genome browser for the gene or the genome location of interest specified by the user. For each sgRNA design, we analyzed the on-target efficiency based on nucleotide composition as well as all potential OT sites and the genomic feature of OT locations, such as whether an OT site is located at an intergenic region, intron, exon, or coding sequence (CDS) region of other genes. We plan to regularly update the tool as new information about CRISPR design becomes available. Similar tools include fly- CRISPR and e-CRISP (for URLs see Table 3), which also provide CRISPR sgRNA designs.

TALEN system and resources In addition to CRISPR, TALENs have also been shown to be effective in generating in vivo modifications in Drosophila (Liu et al. 2012; Beumer et al. 2013; Katsuyama et al. 2013; Kondo et al. 2013). In mammalian systems, the efficiency of doublestrand break generation appears to be better using CRISPR (Cong et al. 2013) but several groups have reported high rates of mutagenesis at OT sites (Cradick et al. 2013; Fu et al. 2013; Hsu et al. 2013; Mali et al. 2013; Pattanayak et al. 2013). In contrast, specificity does not appear to be a problem with the TALEN system due to the longer targeting sequence and use of heterodimeric nucleases (Mussolino et al. 2011; Cade et al. 2012; Dahlem et al. 2012; Ding et al. 2013; Hisano et al. 2013; Mali et al. 2013). In Drosophila, however, OT mutations associated with CRISPR appear to be less problematic with several groups reporting use of the system with no detectible OT effects (Bassett et al. 2013a,b; Gratz et al. 2013; Kondo and Ueda 2013; Ren et al. 2013), making the advantages of TALENs less clear. Furthermore, construction of TALENs is considerably more difficult, requiring specialized molecular biology techniques and production of separate fusion proteins to target each individual locus. Support for TALEN design and analysis is available at E-TALEN (Heigwer et al. 2013) (www.e-TALEN.org). This resource allows the user to simultaneously enter up to 50 gene identifiers or a specific sequence to be analyzed for possible TALEN target sites. These target sites are then assessed for possible OT effects and assigned a score indicating their likely functionality and specificity. Filters can also be applied to design TALENs for a specific purpose such as protein tagging or gene knockout. Alternatively, the user can enter the sequence of a specific TALEN pair and map this to the target genome. Further tools are also available at TALE-NT (Cermak et al. 2011; Doyle et al. 2012) (tale-nt.cac.cornell.edu). In this case, a suite of tools is available for the design of TALENs, TALEtranscription factors, or analysis of possible OTs in a range of model organism genomes or a sequence provided by the user. Finally, multiple other resources are available including databases of TALE-related information and forums for discussion (Table 3).

Next directions for genome engineering A further application of CRISPRs and TALEs is the regulation of gene expression. As recently demonstrated, both TALEs and CRISPR can be used to target a transcription factor fusion to a gene of interest and alter its transcriptional state (Crocker and Stern 2013; Gao et al. 2013; Gilbert et al. 2013; Konermann et al. 2013; Maeder et al. 2013a,b; Mali et al. 2013; Perez-Pinera et al. 2013a,b). Although to date CRISPR-based transcription factors have not been reported for Drosophila, success of related approaches in mammalian cells suggests that in this case, TALEs appear to be more effective than CRISPR. Thus, at least at this relatively early stage in the technologies, it seems likely that CRISPRs will be the system of choice for genome editing, whereas TALEs may be more useful for regulation of gene expression.

Resources for Search and View of Drosophila 'Omics Datasets Data search and view at metasites Sites like FlyBase and FlyMine provide access to a wealth of data relevant to Drosophila from the published literature and large-scale studies. In addition, a number of other sites provide access to specific data collections or types of 'omics data (Table 4). These include large-scale surveys by the Dow lab of gene expression in various Drosophila tissues, the FlyAtlas project (Chintapalli et al. 2007), and surveys by the modENCODE consortium across various Drosophila cell types, life cycle stages, treatment conditions, etc. (Cherbas et al. 2011; Graveley et al. 2011). For an individual gene, FlyAtlas and modENCODE transcript expression data can be viewed at the corresponding FlyBase gene page (expand to view "High- Throughput Expression Data" in the "Expression Data" section). FlyAtlas data can also be navigated at FlyAtlas (Chintapalli et al. 2007) or FlyAtlas2 (Robinson et al. 2013) (Table 4), which among other things facilitates search for genes that share a common pattern of expression as compared with the input gene ("Profile" option). For large-scale studies, researchers might find it necessary to download the underlying datasets and perform their own analyses offline of the web tools, because currently, using the available portals, it is difficult to search more than one gene at a time and/or results cannot easily be downloaded. A different view of RNA is available through FlyFish (Lecuyer et al. 2007) (Table 4), which facilitates search and view of the subcellular localization patterns of mRNAs, as determined using high-throughput fluorescence in situ detection. FlyFISH and other expression data can also be viewed and navigated at FlyExpress (Kumar et al. 2011) (Table 4).

Specific support of RNAi, proteomics, and other datasets Several resources facilitate search and view of cell-based and in vivo data relevant to RNAi screens, with online entry points to various datasets available through websites such as the DRSC (Flockhart et al. 2012) and GenomeRNAi (Schmidt et al. 2013) (for URLs see Table 4). The RSVP search tool for search and view of in vivo RNAi data is somewhat unique in that it allows users not just to view text but when available, also to view example images of mutant phenotypes. Large-scale proteomics data are available through the DPiM (Guruharsha et al. 2011) project site but in our experience, are more easily navigated through the Drosophila Interactions Database (DroID) (Yu et al. 2008; Murali et al. 2011), where researchers can select to view only DPiM results or view them alongside other data, e.g., from binary interaction studies. SignedPPI (Vinayagam et al. 2013b) is designed to predict activation or suppression roles for proteins that directly interact with one another, based on phenotype data from publicly available RNAi datasets and interaction data from a variety of sources. If two proteins interact with each other and always score in the same direction across multiple RNAi screens, the two proteins are predicted to be activators of one another in the SignedPPI network, whereas two proteins are predicted to be suppressors of one another if they always score in opposite directions in RNAi screens. The SignedPPI tool allows users to query the signed protein-protein network among proteins assigned signs, as well as view an extended network of interactors (Vinayagam et al. 2013b).

Resources for enrichment analysis Mining existing data can be useful at multiple stages of a functional genomics project. As mentioned previously, existing data can be used to limit or expand a list. For example, at the beginning of a study, a researcher might choose to narrow down a list of initial candidates to the subset of genes expressed in the stage or tissue of interest. Conversely, the choice might be to expand a list of initial candidates to include coexpressed genes, genes encoding proteins for which 'omics or other data suggest a physical interaction, genes with common gene ontology (GO) terms, etc. Perhaps more importantly, after a largescale study, researchers can use enrichment approaches to limit and expand an initial "hits" list in an effort to reduce the impact of false positive and/or false negative results. A quick scan for "favorite" or well-characterized genes predicted to be positive in the assay is usually impossible to resist. However, systematically looking for commonalities in a long list of positive results on a gene-by-gene basis can be impossible to do. In this case, researchers turn to computational approaches to find patterns or commonalities in gene lists based on existing information (Table 5), such as biological processes or pathways that are overrepresented among the scored genes. This is an effective strategy for reducing the impact of false positive and negative results inherent in high-throughput screen datasets. Moreover, the results of enrichment analysis can improve confidence in subsets of results by placing them in biological context and helping foster the development of new hypotheses for in-depth follow-up studies.

Enrichment tools for Drosophila FlyMine, modMine, and DAVID can be used for enrichment analysis for biological pathways, gene ontology terms, protein domains, publications, and more. FlyMine summarizes data sources at their website (see "Data Sources" tab on the home page flymine.org). The modMine home page similarly lists the types of data that can be viewed (chromatin structure, copy number variation, etc.; see intermine.modencode.org/). For National Institutes of Health, NIH DAVID, data, gene identifiers, and approaches are discussed in their frequently asked questions (FAQs) and summarized at david.abcc.ncifcrf.gov/content. jsp?file=update.html. It is worth noting that the backend knowledge bases that these tools are built on are assembled from publicly available resources and are slightly different from one another. For example, DAVID provides both original GO terms and "slim" GO terms, whereas FlyMine and modMine only provide original GO terms. In addition to GO term analysis, DAVID also provides pathway annotation from PANTHER and KEGG, while modMine provides pathway annotation from Reactome and flyReactome.

Pathway and other specialized enrichment tools More specific molecular pathway annotations can also be accessed directly through various online entry points (Table 5). FlyReactome (Croftet al. 2013) provides annotation of eight major signaling pathways, namely, the circadian clock, Hedgehog, Hippo, Imd, JAK/STAT, planar cell polarity, Toll, and Wingless pathways. KEGG (Ogata et al. 1999) also provides pathway annotation for fly genes, in this case with a focus on metabolism- related genes. Although GO and pathway annotations are useful, they can be either too specific or too broad. For example, annotations for pathways usually span from receptor complexes that receive a signal to transcription factor complexes that constitute the signal readout. To help scientists identify functional modules with higher resolution, we developed the protein COMPLex Enrichment Analysis tool (COMPLEAT) (Vinayagam et al. 2013a). This tool annotates protein complexes using both the literature and predictions from protein- protein networks. COMPLEAT facilitates analysis and visualization based on a full dataset, without input of a predefined cutoffvalue(s), as well as comparison of complexes at different conditions/time points.

Importance of background in enrichment analysis When performing enrichment analysis, it is important to select the proper background. For studies that began with a focused gene list, such as all kinases or another nonrandom subset of genes, this gene list should be used as the background, not the full genome. The default setting for most enrichment tools is to use the whole genome as the background. To specify the background, users typically need to upload the background list as a file. At FlyMine, for example, the background can be specified by uploading and saving the background gene list, uploading the list of genes scored in the experiments, and finally, after the initial enrichment is completed with default settings, clicking "change" at "background population" and selecting a background list from the dropdown menu. For information about using custom background lists with DAVID, we recommend starting with their FAQs answer on the topic (david.abcc.ncifcrf.gov/content.jsp?file=FAQs.html#22).

Similarity through shared phenotypes For Drosophila studies it is also useful to create or analyze a gene list based on a specific phenotype, for example to identify genes that might be involved in the same pathway or network. Expert users can generate queries in FlyBase, which relies on an extensive controlled vocabulary, to retrieve genes with related phenotypes (for help with FlyBase queries see McQuilton et al. 2012; St Pierre et al. 2014). PhenomicDB (Kahraman et al. 2005; Groth et al. 2007) allows users to generate gene lists by phenotype and provides mapping (via HomoloGene) to related genes in other species, so that phenotypes in other species can be explored. The Phenotype-based search is an area that might benefit from further development in the future.

Ortholog Identification and Use of Orthologs to Build Predictions Tools for finding human disease-related and other orthologs "Homologs" can be defined as genes that share a common origin, with the subcategory "paralogs" used to refer to genes of common origin within a species (such as due to gene duplication events) and "orthologs" used to refer to genes of common origin in different species (see figures 1-25 in Alberts 2002). Mapping gene orthologs among species serves an important role in functional genomics by allowing researchers to develop hypotheses about gene function in one organism based on what is known in another. Many algorithms for predicting orthologs have been developed but no one algorithm can hold itself up as providing perfect sensitivity and specificity. Moreover, some web interfaces for these algorithms are easier than others to navigate, and many do not support search of more than one gene at a time. To address some of these concerns, we developed the DRSC Integrative Ortholog Prediction Tool (DIOPT) (Hu et al. 2011) (Table 5), which combines results from 10 different ortholog prediction tools among flies, human, mouse, zebrafish, Xenopus, Caenorhabditis elegans, Saccharomyces cerevisiae, and Schizosaccharomyces pombe. To help support largescale studies, DIOPT facilitates search of multiple genes and results can be downloaded in an Excel-compatible format. Moreover, in an effort to integrate our tools, we recently added a feature that makes it possible to click a button ("find RNAi reagents") on the output page that will "carry" results from DIOPT to UP-TORR. Another tool based on the DIOPT approach, DIOPT Diseases and Traits (DIOPT-DIST) (Hu et al. 2011) (Table 5), integrates search for human gene orthologs with information from National Center for Biotechnology Information, NCBI Online Mendelian Inheritance in Man (OMIM) (Hamosh et al. 2000; Baxevanis 2012) or genomewide association studies (GWAS; see genome.gov/gwastudies) (Hindorffet al. 2009) that link human genes to specific diseases or traits. Previously developed disease-related ortholog databases include Homophila (Chien et al. 2002) and Ortho- Disease (O'Brien et al. 2004; Forslund et al. 2011), which support disease genes annotated in OMIM. With any of these tools, researchers should keep aware of when they were last updated (see below). It is worth mentioning that NeuroGem (Na et al. 2013) provides the first comprehensive knowledge base of integrated information on genetic modifiers of nine different neurodegenerative diseases, as identified in Drosophila, C. elegans, or yeast. This type of resource might prove useful for scientists who study the biological mechanisms underlying specific diseases.

Challenges to ortholog prediction that impact users For all ortholog tools, it is a significant challenge to keep up to date with current gene annotations, and for tools like DIOPT-DIST (Hu et al. 2011), OrthoDisease (O'Brien et al. 2004), and Homophila (Chien et al. 2002) (Table 5), there is the added challenge of keeping up with human diseasegene information, including new GWAS data, for which new deposits are frequent. We do not know of any tool that is set up to remain up to date with all relevant data. Updates to gene annotations and disease gene information could be automated to some extent, such as by using a strategy similar to the approach we used for auto-update of UP-TORR (Hu et al. 2013a). However, given the number of different data sources involved (i.e., various genome annotation and disease-gene association sources), full automation of updates does not currently seem like an achievable goal. Further standardization and centralization could help this problem in the future. In the meantime, users of ortholog and disease-gene ortholog search tools are cautioned to keep aware of versions and update schedules associated with ortholog search tools. In addition, for DIOPT, DIOPT-DIST, and individual ortholog search tools, search results might not be limited to true orthologs (e.g., the results for a given gene might include an ortholog as well as one or more related paralogs in the output species). Many additional tools are useful for researchers specifically interested in phylogeny (see evolution.genetics.washington.edu/phylip/ software.html).

Orthologs and metadata sites Some tools use underlying ortholog mapping to build predictions, as in the case of interlogs, which are putative interactors assigned based on interactions identified for orthologs in other species. Drosophila interlogs are included as an information source in DroID (Murali et al. 2011). Thus, if a user also maps interactions based on orthologs, such as using DIOPT or InParanoid, interologs from DroID should not be counted as a unique piece of evidence, as the same underlying information (InParanoid predictions) is already included. It is also worth mentioning that typically, built-in ortholog mapping in resources with another main focus are usually based on results from a single or a few ortholog prediction tools. As mentioned above, DroID uses InParanoid to map orthologs. In addition, PhenomicDB uses HomoloGene, and modMine uses InParanoid and TreeFam. For more comprehensive and accurate mapping of orthologs among species, the most cautious and comprehensive approach would be to download data and/or annotations from the original species only and then use a multialgorithm search tool like DIOPT to find predicted orthologs.

Integration and Visualization of Large-Scale Datasets and Networks Visualization of protein networks We increasingly view genes, proteins, small RNAs, etc. not as acting alone but as acting as parts of interconnected pathways and networks. Following a phenotypic study, an increasingly common and useful next step is to look at the relationship of genes with shared phenotypes or interactions to known or predicted protein interactions (Table 5). DroID (Yu et al. 2008; Murali et al. 2011) allows researchers to retrieve interactors identified in one or more of several large-scale protein complex and binary interaction studies, as well as to build visualizations of these networks. SignedPPI (Vinayagam et al. 2013b) allows users to build a "signed" network, i.e., a protein-protein interaction network with edges of different colors indicating the activator/ suppressor nature of any given protein pair. This can be done for a given gene list, and then the user can double-click on a given protein to extend the network and view additional interactors. For visualization of various types of biological data, many developers rely on the open source platform Cytoscape (Shannon et al. 2003; Kohl et al. 2011). For example, Cytoscape makes it possible to accommodate many configurations of nodes and edges, and thus it is used for visualizations at COMPLETE and SignedPPI. Downloads, tutorials, publications, etc. related to Cytoscape are available from their home page (http://www.cytoscape.org/) but we note that significant training and expertise is required to utilize the platform.

Visualization of mRNA expression data Visualizations are also useful in viewing mRNA expression data. FlyBase (McQuilton et al. 2012; St Pierre et al. 2014) and modMine (Contrino et al. 2012) provide visualizations of expression data from various sources, easily accessed in gene views (FlyBase) and also viewable in the context of a genome browser (FlyBase and modMine). As mentioned previously, FlyAtlas (Chintapalli et al. 2007) expression array data across various tissues can be searched or viewed at FlyAtlas (Chintapalli et al. 2007) or FlyAtlas2 (Robinson et al. 2013), as well as at FlyBase and FlyMine (Lyne et al. 2007) (Table 4). To help plan or analyze the results of cell-based studies, the DRSC provides tabular output with color coding regarding expression in commonly used cultured cell lines for single- or multigene searches (flyrnai.org/ cellexpress) (Flockhart et al. 2012) and the DGRC provides single-gene search access to data from 25 cell lines (dgrc.cgb. indiana.edu/cells/TilingSearch). The modMine site (Contrino et al. 2012) also provides visualizations in the form of heat maps based on their extensive RNAseq expression datasets, for single- or multigene searches (follow links from their home page modmine.org to view an example or initiate a search).

Resources for Interrogation of miRNA Function miRNA sequence and expression database Since the discovery of the first microRNAs (miRNAs), evidence has revealed the importance of miRNAs in numerous processes (Dai and Ahmed 2011; Huntzinger and Izaurralde 2011; Wang and Peng 2011). Many online tools assist in miRNA identification and target prediction (Table 6). We will discuss some of the online resources that are currently available, with an emphasis on tools that are available for Drosophila and among the most frequently utilized. The miRBase tool (www.mirbase.org) has become the primary online reference for all miRNA-related research, providing indexes of known miRNAs for a given organism, experimentally validated sequences, genomic locations, and miRNA transcript expression in various tissues and cell lines (Kozomara and Griffiths-Jones 2011). Release 20 of miRBase provides 24,521 precursor miRNA sequences and 30,424 mature miRNA sequences from 206 species. In general, a single miRNA precursor duplex produces one to two functional mature miRNA strands, while a rare third functional strand has been reported from the terminal loops of some miRNA precursors (Okamura et al. 2013). The number of precursor miRNAs and mature sequences for D. melanogaster has reached 238 and 426, respectively. Each miRBase miRNA entry page provides predicted hairpin structures and sequences, mature miRNA sequences, links to related miRNA families and clusters, reads from deep sequencing experiments containing each miRNA, links to some of the most widely used miRNA target prediction algorithms (discussed below), and supporting references for each miRNA. Many miRBase miRNA entry pages now also include a community annotation section, which provides information about specific miRNA families and functions taken directly from the free online encyclopedia,Wikipedia. This encourages miRBase users and miRNA experts to contribute their knowledge in the form of Wikipedia edits and new pages.

miRNA target prediction algorithms Now that genome-wide information regarding what miRNAs are encoded is available for Drosophila, identification of target genes for a given miRNA is arguably the most important information for moving miRNA studies forward. However, even for extensively studied organisms miRNA targets are largely unknown. Initial target prediction algorithms based on simple Watson-Crick base-pairing rules generated a high number of false positive results. Advancements in target prediction algorithms have been made in parallel with increasing knowledge about miRNAs. Nevertheless, no "perfect" set of rules has been identified, and the results provided by various updated prediction algorithms are still largely different, due to differences in the criteria they apply. These differences include stringency of base pairing, target site conservation, and target site accessibility. Below, we discuss three of the most frequently used online target prediction algorithms and the criteria each uses to generate target predictions, typically based on 39-untranslated regions (UTRs) of genes (for URLs see Table 6). An additional tool, minoTar, facilitates identification of potential miRNA binding sites in coding sequences (flyrnai.org/minotar) (Schnall-Levin et al. 2010).

TargetScanFly (targetscan.org/fly_12/) (Ruby et al. 2007b) is one of the pioneering target prediction algorithms presenting high sensitivity and precision. TargetScanFly Release 6.2 displays predicted regulatory targets of 148 D. melanogaster miRNAs (Ruby et al. 2007b), including mirtronic miRNAs (Ruby et al. 2007a). Targets are predicted using the TargetScanS algorithm (Lewis et al. 2005) and conservation cutoffs calculated using branch length scores (Kheradpour et al. 2007). TargetScanS requires complementarity of at least 6 nucleotides in the 59 end of the miRNA. TargetScanFly also offers users the option to search for target sites within ORFs (TargetScanFly ORFs) and to input a seed sequence (nucleotides 2-8) for a small RNA to search matching conserved sites (TargetScanFly Custom).

Another commonly used tool is DIANA-microT-CDS (microrna.gr/webServer) (Reczko et al. 2012; Paraskevopoulou et al. 2013). In its fifth version, the microT algorithm searches for target sites within both the 39-UTR and coding regions of a gene. DIANA-microT-CDS relies first on thermodynamic stability between miRNAs and their potential target sites. The algorithm also takes into account features such as binding category, conservation among species, accessibility of target sites as calculated using the Sfold algorithm (Chan et al. 2005), and AU content of regions flanking the target sites.

The miRanda tool (microrna.org) identifies target sites using features such as sequence complementarity and binding energy between the mature miRNA and the target site (Betel et al. 2010). Predicted duplexes are given an miRNA support vector regression (mirSVR) score, which computes a weighted sum of context and sequence features such as AU content flanking the target site, target site accessibility and position, 39-UTR length, base pairing, and a conservation score. Conservation score is calculated based on two features, evolutionary conservation of the target site sequence and site position in aligned 39-UTRs of homologous sequences.

Experimentally validated targets of miRNAs To date, miRNA target prediction algorithms have helped identify targets of miRNAs. These predictions were experimentally validated using various techniques, including reporter gene assays, evaluation of miRNA vs. mRNA expression using qPCR or Northern blotting, and assessment of the effects of miRNA expression on protein levels of a target gene using standard techniques such as immunoblotting. These results are now being curated into databases of experimentally validated miRNA targets. DIANA-TarBase (microrna.gr/tarbase) is currently the largest manually curated target database, with an index of .65,000 miRNA-target interactions from many species (Vergoulis et al. 2012). The second largest manually curated database of miRNA targets is miRTarBase (mirtarbase. mbc.nctu.edu.tw), with 51,460 miRNA-target interactions from 18 species (Hsu et al. 2011). Both databases offer extensive information on the techniques used for validation, target gene information, and miRNA binding sites, as well as links to supporting publications.

Reagents and fly stocks for miRNA functional studies: loss of function As for protein-coding genes, loss-of-function analysis is an appropriate route to study the endogenous function of miRNA-encoding genes. However, there are relatively few loss-of-function mutant strains available for individual miRNAs. Engineered transgenic constructs with multiple miRNA antisense binding sites, which should sequester miRNA and are known as "sponges," have been described as an alternative to knockouts (Kumar et al. 2008; Ebert and Sharp 2010; Papapetrou et al. 2010). In Drosophila, miRNA sponges have been shown to accurately copy the phenotype of loss-of-function mutants (Loya et al. 2009). Because miRNA sponges are under UAS control, tissue-specific Gal4 drivers can be used to manipulate spatiotemporal miRNA levels in intact flies. Furthermore, by introducing multiple different miRNA binding sites, sponge technology can also be used to study the role of different miRNAs simultaneously. Recently, Fulga and colleagues completed production of a transgenic library of conditional miRNA sponges (T. A. Fulga, E. M. McNeill, R. Binari, J. Yelick, A. Blanche, M. Booker, M. Schnall-Levin, Y. Zhao, T. DeLucca, F. Bejarano, Z. Han, E. C. Lai, D. Wall, N. Perrimon, and D. Van Vactor, unpublished data). The plasmids can also be used for high-throughput screening of miRNA function in cell culture.

Reagents and fly stocks for miRNA functional studies: gain of function Phenotype-driven gain-of-function miRNA screens are another approach to identifying the activities of miRNAs. Overexpression of miRNAs can produce phenotypes due to ectopic repression of one or more targets, lending insights into miRNA-target relationships. However, a significant caveat to this approach is that mis- and/or overexpression of some miRNAs might lead to binding of nonphysiological targets. Currently, there are four collections of conditional miRNA overexpression fly lines (Bejarano et al. 2012; Schertel et al. 2012; Szuplewski et al. 2012). Bejarano et al. (2012) constructed two collections of conditional miRNA overexpression lines. The first was built using vectors that coexpress DsRed as a marker and were randomly inserted into the genome. This collection includes 665 lines, comprising 165 different transgenes that cover 149 distinct miRNA hairpins. Because random insertion can lead to position effects, a second collection of 107 miRNA overexpression inserts was built using a site-directed attP vector. The second set of constructs also replaced DsRed with luciferase. Szuplewski et al. (2012) and Schertel et al. (2012) also generated collections using site-specific integration. Szuplewski et al. (2012) generated 109 miRNA overexpression lines and Schertel et al. (2012) generated 180 miRNA overexpression lines. The majority of these fly stocks can be directly obtained from their respective laboratories, and the 107 miRNA overexpression fly stocks from Bejarano et al. (2012) can be obtained from BDSC. As mentioned above, the plasmids used to construct miRNA overexpression fly stocks can be used to screen miRNA function in cell culture using high-throughput screening techniques (Silver et al. 2007).

Example Functional Genomics Workflows To illustrate how some of the tools that we highlight in this review can be integrated in the context of network studies, we briefly describe two recent studies from our group aimed to identify new components involved in autophagy and Hippo signaling. Many other examples and references of large-scale screens, especially starting from genome-wide RNAi screens, are available on the "Publications" page at flyrnai.org (see also Mohr et al. 2010).

In workflow one (Figure 1), (J. Zirin, J. Nieuwenhuis, A. Samsonova, R. Tao, and N. Perrimon, unpublished data) assembled a list of genes representing the Drosophila orthologs of a mammalian autophagy network (Behrends et al. 2010). Drosophila proteins related to the mammalian proteins were identified using DIOPT. Such a gene list can be further supplemented with additional genes using searches for genes with common expression patterns, functions, disease links, etc., using tools like FlyAtlas, modMINE, FlyMine, and/or DIOPTDIST. Next, a small library of dsRNAs for cell-based RNAi with multiple reagent-per-gene coverage was assembled based on the DRSC collection of RNAi reagents, supplemented with new designs generated using SnapDragon (Flockhart et al. 2012). The library was then screened using a robust autophagy primary muscle cell-based assay. Following an initial statistical analysis of the screen data, tools like COMPLEAT and DAVID were used to help identify high-confidence hits, as well as determine what functions are enriched for positive hits as compared with the starting set of genes in the library. Tools like UP-TORR and FlyPrimerBank can be used to help identify in vivo fly stocks for follow-up studies and design primers for qPCR validation of knockdown. Finally, after publication, the screen data are shared with the community through the DRSC (flyrnai.org), where they are subsequently captured by GenomeRNAi (Schmidt et al. 2013) and other metasites.

In workflow two (Figure 2), Kwon et al. (2013) performed a large-scale mass spec study followed by functional validation in both cell lines and in vivo. Specifically, the study started with a pathway of interest (Hippo signaling), such as might be explored at FlyReactome (Croftet al. 2013). Next, clones were identified and tagged fusion proteins developed for known pathway members. These were then expressed in fly cells, followed by pulldown, purification of complexes, and mass spec to identify complex members (Kwon et al. 2013). An initial analysis of mass spec results can be used to generate gene lists from each pulldown experiment. Tools like DroID (Yu et al. 2008; Murali et al. 2011), COMPLEAT (Vinayagam et al. 2013a), and DAVID (Dennis et al. 2003) can be used to assess quality and look for supporting evidence for potential new interactions. Cell-based reagents were identified from the DRSC collection using UP-TORR (Hu et al. 2013a) and newly identified genes were tested functionally using RNAi. For workflows like this one, tools like SignedPPI (Vinayagam et al. 2013b) and FlyBase phenotype annotations for individual genes can also provide a picture of potential functional roles and relationships among complex members.

Concluding Remarks In this review, we describe many of the resources and online tools available to Drosophila researchers for functional genomics approaches. Specifically, we list resources useful for three fundamental steps, i.e., to identify or design reagents; to narrow down lists of candidate or initial positive-scoring genes or proteins, such as following a large-scale study; and to expand lists of candidate genes. Numerous online tools and community resources have been developed. These serve the Drosophila community by providing genome-scale fly stock collections and other reagent for genetics, transcriptomics, and proteomics, as well as online tools for genome annotation, computational predictions, access to previous data, ortholog predictions, and data analysis, integration, and visualization.

Many of the online resources have become essential to the day-to-day workflow of Drosophila research. Nevertheless, there remain opportunities for refinement of existing tools and development of new ones. As mentioned previously, approaches for identifying genes with shared phenotypes is one area that might benefit from new development. Related to this is the development of new text-mining tools. Due to the long history of scientific publication, the majority of data exists in free-text format in the literature. Text-mining tools might help automate curation at FlyBase (McQuilton 2012) as well as help catalog previously identified gene-phenotype relationships, protein-protein interactions, etc. Another area for improvement is in annotation of regulatory sequences. Good prediction algorithms and experimental data are available for annotation of coding sequences. However, the locations and extents of UTRs, promoters, and enhancers are more difficult to annotate. For mammalian genes, the TRANSFAC database (Wingender et al. 1996) reports experimentally validated transcription factor binding sites, consensus binding sequences, etc. The availability of various relevant data from modENCODE (Contrino et al. 2012) should have a positive impact on annotation of regulatory regions in the future. Moreover, new databases relevant to fly transcriptomics are already emerging, e.g., OnTheFly (Shazman et al. 2013) and REDfly (Gallo et al. 2011). Additionally, although there is a wealth of information about signaling and biochemical pathways in Drosophila, the current high-quality pathway annotations grossly undersample the available information. Extensive pathway annotation for mammalian genes is available at Bi°Carta (biocarta.com) and Reactome (reactome.org), as well as commercial sources like Ingenuity. By contrast, for the fly, flyReactome (Croftet al. 2013) appears to be the only source, and includes only eight pathways.

We expect that data visualization tools will be increasingly important as more and more large-scale studies are performed and the data made available. These tools can help researchers view and compare data, leading to development of new hypotheses. It can also be helpful to view experimental images associated with small- or large-scale studies, e.g., light microscopy views of wing defects or fluorescence micrographs showing defects identified in a cell-based study. There are significant challenges in making image-based data available for view and download, including the basic problem of how to store, manage, and provide access to what tend to be large files. For both data visualization and images, it is tempting to look beyond what is being developed specifically for bioinformatics and begin to ask if we can learn from other areas, such as 3D and interactive video gaming and social networking sites like Flickr or Instagram that routinely handle large image datasets.

Altogether, we conclude that the Drosophila field is well served by existing functional genomics resources and online tools, and that with careful consideration of which tools to use and how to use them, the resources can have significant positive impact on study design, analysis, and integration. Moreover, given a continued commitment to update and maintenance of existing tools, as well as continued development of new resources, support for Drosophila functional genomics should continue to improve in the future.

Acknowledgments We extend our thanks to Kathy Matthews and Annette Parks at the Bloomington Drosophila Stock Center and to Bill Gelbart, Susan St. Pierre, and curators at FlyBase for helpful suggestions. We gratefully acknowledge support of the Drosophila RNAi Screening Center and Transgenic RNAi Project by National Institutes of Health-National Institute of General Medical Sciences (NIH-NIGMS) R01 GM067761, NIH-NIGMS R01 GM084947, and NIH-Office of Research Infrastructure Programs R24 RR032668. S.E.M. is also supported in part by the Dana Farber/Harvard Cancer Center and N.P. is an investigator of the Howard Hughes Medical Institute.

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Communicating editor: P. Sengupta Stephanie E. Mohr,*,[dagger],1 Yanhui Hu,*,[dagger] Kevin Kim,[dagger] Benjamin E. Housden,[dagger] and Norbert Perrimon*,[dagger],[double dagger] *Drosophila RNAi Screening Center and [dagger]Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, and [double dagger]Howard Hughes Medical Institute, Boston, Massachusetts 02115 Copyright © 2014 by the Genetics Society of America doi: 10.1534/genetics.113.154344 Manuscript received January 9, 2014; accepted for publication February 9, 2014 1Corresponding author: 77 Avenue Louis Pasteur, Department of Genetics NRB 336, Boston, MA 02115. E-mail: [email protected] (c) 2014 Genetics Society of America

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