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1.  Integrating protein-protein interaction networks with phenotypes reveals signs of interactions 
Nature methods  2013;11(1):10.1038/nmeth.2733.
A major objective of systems biology is to organize molecular interactions as networks and to characterize information-flow within networks. We describe a computational framework to integrate protein-protein interaction (PPI) networks and genetic screens to predict the “signs” of interactions (i.e. activation/inhibition relationships). We constructed a Drosophila melanogaster signed PPI network, consisting of 6,125 signed PPIs connecting 3,352 proteins that can be used to identify positive and negative regulators of signaling pathways and protein complexes. We identified an unexpected role for the metabolic enzymes Enolase and Aldo-keto reductase as positive and negative regulators of proteolysis, respectively. Characterization of the activation/inhibition relationships between physically interacting proteins within signaling pathways will impact our understanding of many biological functions, including signal transduction and mechanisms of disease.
PMCID: PMC3877743  PMID: 24240319
2.  Conserved Regulators of Nucleolar Size Revealed by Global Phenotypic Analyses 
Science signaling  2013;6(289):ra70.
Regulation of cell growth is a fundamental process in development and disease that integrates a vast array of extra- and intracellular information. A central player in this process is RNA polymerase I (Pol I), which transcribes ribosomal RNA (rRNA) genes in the nucleolus. Rapidly growing cancer cells are characterized by increased Pol I–mediated transcription and, consequently, nucleolar hypertrophy. To map the genetic network underlying the regulation of nucleolar size and of Pol I–mediated transcription, we performed comparative, genome-wide loss-of-function analyses of nucleolar size in Saccharomyces cerevisiae and Drosophila melanogaster coupled with mass spectrometry–based analyses of the ribosomal DNA (rDNA) promoter. With this approach, we identified a set of conserved and nonconserved molecular complexes that control nucleolar size. Furthermore, we characterized a direct role of the histone information regulator (HIR) complex in repressing rRNA transcription in yeast. Our study provides a full-genome, cross-species analysis of a nuclear subcompartment and shows that this approach can identify conserved molecular modules.
PMCID: PMC3964804  PMID: 23962978
3.  Identification of Functional Elements and Regulatory Circuits by Drosophila modENCODE 
Roy, Sushmita | Ernst, Jason | Kharchenko, Peter V. | Kheradpour, Pouya | Negre, Nicolas | Eaton, Matthew L. | Landolin, Jane M. | Bristow, Christopher A. | Ma, Lijia | Lin, Michael F. | Washietl, Stefan | Arshinoff, Bradley I. | Ay, Ferhat | Meyer, Patrick E. | Robine, Nicolas | Washington, Nicole L. | Di Stefano, Luisa | Berezikov, Eugene | Brown, Christopher D. | Candeias, Rogerio | Carlson, Joseph W. | Carr, Adrian | Jungreis, Irwin | Marbach, Daniel | Sealfon, Rachel | Tolstorukov, Michael Y. | Will, Sebastian | Alekseyenko, Artyom A. | Artieri, Carlo | Booth, Benjamin W. | Brooks, Angela N. | Dai, Qi | Davis, Carrie A. | Duff, Michael O. | Feng, Xin | Gorchakov, Andrey A. | Gu, Tingting | Henikoff, Jorja G. | Kapranov, Philipp | Li, Renhua | MacAlpine, Heather K. | Malone, John | Minoda, Aki | Nordman, Jared | Okamura, Katsutomo | Perry, Marc | Powell, Sara K. | Riddle, Nicole C. | Sakai, Akiko | Samsonova, Anastasia | Sandler, Jeremy E. | Schwartz, Yuri B. | Sher, Noa | Spokony, Rebecca | Sturgill, David | van Baren, Marijke | Wan, Kenneth H. | Yang, Li | Yu, Charles | Feingold, Elise | Good, Peter | Guyer, Mark | Lowdon, Rebecca | Ahmad, Kami | Andrews, Justen | Berger, Bonnie | Brenner, Steven E. | Brent, Michael R. | Cherbas, Lucy | Elgin, Sarah C. R. | Gingeras, Thomas R. | Grossman, Robert | Hoskins, Roger A. | Kaufman, Thomas C. | Kent, William | Kuroda, Mitzi I. | Orr-Weaver, Terry | Perrimon, Norbert | Pirrotta, Vincenzo | Posakony, James W. | Ren, Bing | Russell, Steven | Cherbas, Peter | Graveley, Brenton R. | Lewis, Suzanna | Micklem, Gos | Oliver, Brian | Park, Peter J. | Celniker, Susan E. | Henikoff, Steven | Karpen, Gary H. | Lai, Eric C. | MacAlpine, David M. | Stein, Lincoln D. | White, Kevin P. | Kellis, Manolis
Science (New York, N.Y.)  2010;330(6012):1787-1797.
To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction. Our results provide a foundation for directed experimental and computational studies in Drosophila and related species and also a model for systematic data integration toward comprehensive genomic and functional annotation.
PMCID: PMC3192495  PMID: 21177974
4.  GCPReg package for registration of the segmentation gene expression data in Drosophila 
Fly  2009;3(2):151-156.
In modern functional genomics registration techniques are used to construct reference gene expression patterns and create a spatiotemporal atlas of the expression of all the genes in a network. In this paper we present a software package called GCPReg, which can be used to register the expression patterns of segmentation genes in the early Drosophila embryo. The key task, which this package performs, is the extraction of spatially localized characteristic features of expression patterns. To facilitate this task, we have developed an easy-to-use interactive graphical interface. We describe GCPReg usage and demonstrate how this package can be applied to register gene expression patterns in wild type and mutants. GCPReg has been designed to operate on a UNIX platform and is freely available via the Internet at
PMCID: PMC3171190  PMID: 19550114
image processing; confocal microscopy; quantitative gene expression data; spatial registration; segmentation genes
5.  False negative rates in Drosophila cell-based RNAi screens: a case study 
BMC Genomics  2011;12:50.
High-throughput screening using RNAi is a powerful gene discovery method but is often complicated by false positive and false negative results. Whereas false positive results associated with RNAi reagents has been a matter of extensive study, the issue of false negatives has received less attention.
We performed a meta-analysis of several genome-wide, cell-based Drosophila RNAi screens, together with a more focused RNAi screen, and conclude that the rate of false negative results is at least 8%. Further, we demonstrate how knowledge of the cell transcriptome can be used to resolve ambiguous results and how the number of false negative results can be reduced by using multiple, independently-tested RNAi reagents per gene.
RNAi reagents that target the same gene do not always yield consistent results due to false positives and weak or ineffective reagents. False positive results can be partially minimized by filtering with transcriptome data. RNAi libraries with multiple reagents per gene also reduce false positive and false negative outcomes when inconsistent results are disambiguated carefully.
PMCID: PMC3036618  PMID: 21251254
6.  Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images 
Fly  2008;2(2):58-66.
We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns.
PMCID: PMC2803333  PMID: 18820476
image processing; confocal microscopy; gene expression; image segmentation; spatial registration; background removal
7.  Characterization of the Drosophila segment determination morphome 
Developmental biology  2007;313(2):844-862.
Here we characterize the expression of the full system of genes which control the segmentation morphogenetic field of Drosophila at the protein level in one dimension. The data used for this characterization are quantitative with cellular resolution in space and about 6 min in time. We present the full quantitative profiles of all 14 segmentation genes which act before the onset of gastrulation. The expression patterns of these genes are first characterized in terms of their average or typical behavior. At this level, the expression of all of the genes has been integrated into a single atlas of gene expression in which the expression levels of all genes in each cell are specified. We show that expression domains do not arise synchronously, but rather each domain has its own specific dynamics of formation. Moreover, we show that the expression domains shift position in the direction of the cephalic furrow, such that domains in the anlage of the segmented germ band shift anteriorly while those in the presumptive head shift posteriorly. The expression atlas of integrated data is very close to the expression profiles of individual embryos during the latter part of the blastoderm stage. At earlier times gap gene domains show considerable variation in amplitude, and significant positional variability. Nevertheless, an average early gap domain is close to that of a median individual. In contrast, we show that there is a diversity of developmental trajectories among pair-rule genes at a variety of levels, including the order of domain formation and positional accuracy. We further show that this variation is dynamically reduced, or canalized, over time. As the first quantitatively characterized morphogenetic field, this system and its behavior constitute an extraordinarily rich set of materials for the study of canalization and embryonic regulation at the molecular level.
PMCID: PMC2254320  PMID: 18067886
Drosophila embryo; Segmentation genes; Blastoderm; Gene expression; Quantitative expression data; Positional information
8.  Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster 
PLoS Computational Biology  2007;3(7):e144.
Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.
Author Summary
The task of deciphering the complex transcriptional regulatory networks controlling development is one of the major current challenges for molecular biology. The problem is difficult, if not impossible, to solve without a detailed knowledge of the spatiotemporal dynamics of gene expression. Thus, to understand development, we need to identify and functionally characterize all players in regulatory networks. Data on gene expression dynamics obtained from whole transcriptome microarray experiments, combined with in situ hybridization mRNA localisation patterns for a subset of genes, may provide a route for predicting the localisation of gene expression for those genes for which in situ data has not been generated, as well as suggesting functional information for uncharacterised genes. Here, we report the development of one of the first methods for predicting the localisation of gene expression during Drosophila embryogenesis from microarray data. Pooling the subset of genes in the fly genome with in situ data to form functional units, localised in space and time for relevant developmental processes, facilitates the statement of a classification problem, which we address with machine-learning methods. Our approach promotes a richer annotation of biological function for genes in the absence of costly and time-consuming experimental analysis.
PMCID: PMC1924873  PMID: 17658945

Results 1-8 (8)