Here, we document a collection of ∼7434 MiMIC (Minos Mediated Integration Cassette) insertions of which 2854 are inserted in coding introns. They allowed us to create a library of 400 GFP-tagged genes. We show that 72% of internally tagged proteins are functional, and that more than 90% can be imaged in unfixed tissues. Moreover, the tagged mRNAs can be knocked down by RNAi against GFP (iGFPi), and the tagged proteins can be efficiently knocked down by deGradFP technology. The phenotypes associated with RNA and protein knockdown typically correspond to severe loss of function or null mutant phenotypes. Finally, we demonstrate reversible, spatial, and temporal knockdown of tagged proteins in larvae and adult flies. This new strategy and collection of strains allows unprecedented in vivo manipulations in flies for many genes. These strategies will likely extend to vertebrates.

We report a statistical study to discover transcriptome similarity of developmental stages from D. melanogaster and C. elegans using modENCODE RNA-seq data. We focus on "stage-associated genes" that capture specific transcriptional activities in each stage and use them to map pairwise stages within and between the two species by a hypergeometric test. Within each species, temporally adjacent stages exhibit high transcriptome similarity, as expected. Additionally, fly female adults and worm adults are mapped with fly and worm embryos, respectively, due to maternal gene expression. Between fly and worm, an unexpected strong collinearity is observed in the time course from early embryos to late larvae. Moreover, a second parallel pattern is found between fly prepupae through adults and worm late embryos through adults, consistent with the second large wave of cell proliferation and differentiation in the fly life cycle. The results indicate a partially duplicated developmental program in fly. Our results constitute the first comprehensive comparison between D. melanogaster and C. elegans developmental time courses and provide new insights into similarities in their development . We use an analogous approach to compare tissues and cells from fly and worm. Findings include strong transcriptome similarity of fly cell lines, clustering of fly adult tissues by origin regardless of sex and age, and clustering of worm tissues and dissected cells by developmental stage. Gene ontology analysis supports our results and gives a detailed functional annotation of different stages, tissues and cells. Finally, we show that standard correlation analyses could not effectively detect the mappings found by our method.

Large scale surveys in mammalian tissue culture cells suggest that the protein expressed at the median abundance is present at 8,000-16,000 molecules per cell and that differences in mRNA expression between genes explain only 10-40% of the differences in protein levels. We find, however, that these surveys have significantly underestimated protein abundances and the relative importance of transcription. Using individual measurements for 61 housekeeping proteins to rescale whole proteome data from Schwanhausser et al. (2011), we find that the median protein detected is expressed at 170,000 molecules per cell and that our corrected protein abundance estimates show a higher correlation with mRNA abundances than do the uncorrected protein data. In addition, we estimated the impact of further errors in mRNA and protein abundances using direct experimental measurements of these errors. The resulting analysis suggests that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwanhausser et al. (2011), though because one major source of error could not be estimated the true percent contribution should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhausser et al. (2011), and that the measured and inferred translation rates correlate poorly (R(2) = 0.13). Based on this, our second strategy suggests that mRNA levels explain ∼81% of the variance in protein levels. We also determined the percent contributions of transcription, RNA degradation, translation and protein degradation to the variance in protein abundances using both of our strategies. While the magnitudes of the two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role. Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed.

Alternative splicing is regulated by RNA binding proteins (RBPs) that recognize pre-mRNA sequence elements and activate or repress adjacent exons. Here, we used RNA interference and RNA-seq to identify splicing events regulated by 56 Drosophila proteins, some previously unknown to regulate splicing. Nearly all proteins affected alternative first exons, suggesting that RBPs play important roles in first exon choice. Half of the splicing events were regulated by multiple proteins, demonstrating extensive combinatorial regulation. We observed that SR and hnRNP proteins tend to act coordinately with each other, not antagonistically. We also identified a cross-regulatory network where splicing regulators affected the splicing of pre-mRNAs encoding other splicing regulators. This large-scale study substantially enhances our understanding of recent models of splicing regulation and provides a resource of thousands of exons that are regulated by 56 diverse RBPs.

We evaluated how variations in sequencing depth and other parameters influence interpretation of chromatin immunoprecipitation-sequencing (ChIP-seq) experiments. Using Drosophila melanogaster S2 cells, we generated ChIP-seq data sets for a site-specific transcription factor (Suppressor of Hairy-wing) and a histone modification (H3K36me3). We detected a chromatin-state bias: open chromatin regions yielded higher coverage, which led to false positives if not corrected. This bias had a greater effect on detection specificity than any base-composition bias. Paired-end sequencing revealed that single-end data underestimated ChIP-library complexity at high coverage. Removal of reads originating at the same base reduced false-positives but had little effect on detection sensitivity. Even at mappable-genome coverage depth of ∼1 read per base pair, ∼1% of the narrow peaks detected on a tiling array were missed by ChIP-seq. Evaluation of widely used ChIP-seq analysis tools suggests that adjustments or algorithm improvements are required to handle data sets with deep coverage.

The gut microbiome produces vitamins, nutrients, and neurotransmitters, and helps to modulate the host immune system-and also plays a major role in the metabolism of many exogenous compounds, including drugs and chemical toxicants. However, the extent to which specific microbial species or communities modulate hazard upon exposure to chemicals remains largely opaque. Focusing on the effects of collateral dietary exposure to the widely used herbicide atrazine, we applied integrated omics and phenotypic screening to assess the role of the gut microbiome in modulating host resilience in Drosophila melanogaster. Transcriptional and metabolic responses to these compounds are sex-specific and depend strongly on the presence of the commensal microbiome. Sequencing the genomes of all abundant microbes in the fly gut revealed an enzymatic pathway responsible for atrazine detoxification unique to Acetobacter tropicalis. We find that Acetobacter tropicalis alone, in gnotobiotic animals, is sufficient to rescue increased atrazine toxicity to wild-type, conventionally reared levels. This work points toward the derivation of biotic strategies to improve host resilience to environmental chemical exposures, and illustrates the power of integrative omics to identify pathways responsible for adverse health outcomes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

modENCODE was a 5year NHGRI funded project (2007-2012) to map the function of every base in the genomes of worms and flies characterizing positions of modified histones and other chromatin marks, origins of DNA replication, RNA transcripts and the transcription factor binding sites that control gene expression. Here we describe the Drosophila modENCODE datasets and how best to access and use them for genome wide and individual gene studies.

Animal transcriptomes are dynamic, with each cell type, tissue and organ system expressing an ensemble of transcript isoforms that give rise to substantial diversity. Here we have identified new genes, transcripts and proteins using poly(A)+ RNA sequencing from Drosophila melanogaster in cultured cell lines, dissected organ systems and under environmental perturbations. We found that a small set of mostly neural-specific genes has the potential to encode thousands of transcripts each through extensive alternative promoter usage and RNA splicing. The magnitudes of splicing changes are larger between tissues than between developmental stages, and most sex-specific splicing is gonad-specific. Gonads express hundreds of previously unknown coding and long non-coding RNAs (lncRNAs), some of which are antisense to protein-coding genes and produce short regulatory RNAs. Furthermore, previously identified pervasive intergenic transcription occurs primarily within newly identified introns. The fly transcriptome is substantially more complex than previously recognized, with this complexity arising from combinatorial usage of promoters, splice sites and polyadenylation sites.

In eukaryotic cells, RNAs exist as ribonucleoprotein particles (RNPs). Despite the importance of these complexes in many biological processes, including splicing, polyadenylation, stability, transportation, localization, and translation, their compositions are largely unknown. We affinity-purified 20 distinct RNA-binding proteins (RBPs) from cultured Drosophila melanogaster cells under native conditions and identified both the RNA and protein compositions of these RNP complexes. We identified "high occupancy target" (HOT) RNAs that interact with the majority of the RBPs we surveyed. HOT RNAs encode components of the nonsense-mediated decay and splicing machinery, as well as RNA-binding and translation initiation proteins. The RNP complexes contain proteins and mRNAs involved in RNA binding and post-transcriptional regulation. Genes with the capacity to produce hundreds of mRNA isoforms, ultracomplex genes, interact extensively with heterogeneous nuclear ribonuclear proteins (hnRNPs). Our data are consistent with a model in which subsets of RNPs include mRNA and protein products from the same gene, indicating the widespread existence of auto-regulatory RNPs. From the simultaneous acquisition and integrative analysis of protein and RNA constituents of RNPs, we identify extensive cross-regulatory and hierarchical interactions in post-transcriptional control.

Drosophila melanogaster is one of the most well studied genetic model organisms; nonetheless, its genome still contains unannotated coding and non-coding genes, transcripts, exons and RNA editing sites. Full discovery and annotation are pre-requisites for understanding how the regulation of transcription, splicing and RNA editing directs the development of this complex organism. Here we used RNA-Seq, tiling microarrays and cDNA sequencing to explore the transcriptome in 30 distinct developmental stages. We identified 111,195 new elements, including thousands of genes, coding and non-coding transcripts, exons, splicing and editing events, and inferred protein isoforms that previously eluded discovery using established experimental, prediction and conservation-based approaches. These data substantially expand the number of known transcribed elements in the Drosophila genome and provide a high-resolution view of transcriptome dynamics throughout development.

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Drosophila melanogaster plays an important role in molecular, genetic, and genomic studies of heredity, development, metabolism, behavior, and human disease. The initial reference genome sequence reported more than a decade ago had a profound impact on progress in Drosophila research, and improving the accuracy and completeness of this sequence continues to be important to further progress. We previously described improvement of the 117-Mb sequence in the euchromatic portion of the genome and 21 Mb in the heterochromatic portion, using a whole-genome shotgun assembly, BAC physical mapping, and clone-based finishing. Here, we report an improved reference sequence of the single-copy and middle-repetitive regions of the genome, produced using cytogenetic mapping to mitotic and polytene chromosomes, clone-based finishing and BAC fingerprint verification, ordering of scaffolds by alignment to cDNA sequences, incorporation of other map and sequence data, and validation by whole-genome optical restriction mapping. These data substantially improve the accuracy and completeness of the reference sequence and the order and orientation of sequence scaffolds into chromosome arm assemblies. Representation of the Y chromosome and other heterochromatic regions is particularly improved. The new 143.9-Mb reference sequence, designated Release 6, effectively exhausts clone-based technologies for mapping and sequencing. Highly repeat-rich regions, including large satellite blocks and functional elements such as the ribosomal RNA genes and the centromeres, are largely inaccessible to current sequencing and assembly methods and remain poorly represented. Further significant improvements will require sequencing technologies that do not depend on molecular cloning and that produce very long reads.

The identification of full length transcripts entirely from short-read RNA sequencing data (RNA-seq) remains a challenge in the annotation of genomes. Here we describe an automated pipeline for genome annotation that integrates RNA-seq and gene-boundary data sets, which we call Generalized RNA Integration Tool, or GRIT. Applying GRIT to Drosophila melanogaster short-read RNA-seq, cap analysis of gene expression (CAGE) and poly(A)-site-seq data collected for the modENCODE project, we recovered the vast majority of previously annotated transcripts and doubled the total number of transcripts cataloged. We found that 20% of protein coding genes encode multiple protein-localization signals and that, in 20-d-old adult fly heads, genes with multiple polyadenylation sites are more common than genes with alternative splicing or alternative promoters. GRIT demonstrates 30% higher precision and recall than the most widely used transcript assembly tools. GRIT will facilitate the automated generation of high-quality genome annotations without the need for extensive manual annotation.

Identifying functional enhancer elements in metazoan systems is a major challenge. Large-scale validation of enhancers predicted by ENCODE reveal false-positive rates of at least 70%. We used the pregrastrula-patterning network of Drosophila melanogaster to demonstrate that loss in accuracy in held-out data results from heterogeneity of functional signatures in enhancer elements. We show that at least two classes of enhancers are active during early Drosophila embryogenesis and that by focusing on a single, relatively homogeneous class of elements, greater than 98% prediction accuracy can be achieved in a balanced, completely held-out test set. The class of well-predicted elements is composed predominantly of enhancers driving multistage segmentation patterns, which we designate segmentation driving enhancers (SDE). Prediction is driven by the DNA occupancy of early developmental transcription factors, with almost no additional power derived from histone modifications. We further show that improved accuracy is not a property of a particular prediction method: after conditioning on the SDE set, naïve Bayes and logistic regression perform as well as more sophisticated tools. Applying this method to a genome-wide scan, we predict 1,640 SDEs that cover 1.6% of the genome. An analysis of 32 SDEs using whole-mount embryonic imaging of stably integrated reporter constructs chosen throughout our prediction rank-list showed >90% drove expression patterns. We achieved 86.7% precision on a genome-wide scan, with an estimated recall of at least 98%, indicating high accuracy and completeness in annotating this class of functional elements.

Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community.

On the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.

Transcription factors (TFs) play a key role in development and in cellular responses to the environment by activating or repressing the transcription of target genes in precise spatial and temporal patterns. In order to develop a catalog of target genes of Drosophila melanogaster TFs, the modERN consortium systematically knocked down the expression of TFs using RNAi in whole embryos followed by RNA-seq. We generated data for 45 TFs which have 18 different DNA-binding domains and are expressed in 15 of the 16 organ systems. The range of inactivation of the targeted TFs by RNAi ranged from log2fold change -3.52 to +0.49. The TFs also showed remarkable heterogeneity in the numbers of candidate target genes identified, with some generating thousands of candidates and others only tens. We present detailed analysis from five experiments, including those for three TFs that have been the focus of previous functional studies (ERR, sens, and zfh2) and two previously uncharacterized TFs (sens-2 and CG32006), as well as short vignettes for selected additional experiments to illustrate the utility of this resource. The RNA-seq datasets are available through the ENCODE DCC (http://encodeproject.org) and the Sequence Read Archive (SRA). TF and target gene expression patterns can be found here: https://insitu.fruitfly.org. These studies provide data that facilitate scientific inquiries into the functions of individual TFs in key developmental, metabolic, defensive, and homeostatic regulatory pathways, as well as provide a broader perspective on how individual TFs work together in local networks during embryogenesis.