Flynet: a genomic resource for Drosophila melanogaster transcriptional regulatory networks

Abstract

Motivation: The highly coordinated expression of thousands of genes in an organism is regulated by the concerted action of transcription factors, chromatin proteins and epigenetic mechanisms. High-throughput experimental data for genome wide in vivo protein–DNA interactions and epigenetic marks are becoming available from large projects, such as the model organism ENCyclopedia Of DNA Elements (modENCODE) and from individual labs. Dissemination and visualization of these datasets in an explorable form is an important challenge.Results: To support research on Drosophila melanogaster transcription regulation and make the genome wide in vivo protein–DNA interactions data available to the scientific community as a whole, we have developed a system called Flynet. Currently, Flynet contains 101 datasets for 38 transcription factors and chromatin regulator proteins in different experimental conditions. These factors exhibit different types of binding profiles ranging from sharp localized peaks to broad binding regions. The protein–DNA interaction data in Flynet was obtained from the analysis of chromatin immunoprecipitation experiments on one color and two color genomic tiling arrays as well as chromatin immunoprecipitation followed by massively parallel sequencing. A web-based interface, integrated with an AJAX based genome browser, has been built for queries and presenting analysis results. Flynet also makes available the cis-regulatory modules reported in literature, known and de novo identified sequence motifs across the genome, and other resources to study gene regulation.Contact: grossman@uic.eduAvailability: Flynet is available at https://www.cistrack.org/flynet/.Supplementary information: Supplementary data are available at Bioinformatics online.

Computational Prediction of Protein–Protein Interaction Networks: Algorithms and Resources

“…The Database of Interacting Proteins ( DIP TM )[ 14 ] developed at the University of California, Los Angeles has combined data from a variety of sources to create a single, consistent set of PPI. In addition to the primary sources, DIP drives its data from a number of other databases such as Yeast Protein Database (YPD) [ 15 ], EcoCyc [ 16 ], and FlyNet [ 17 ], Kyoto Encyclopedia of Genes and Genomes (KEGG)[ 18 ]. The complete DIP dataset are freely available for download as well as specialized DIP subsets and additional data (free registration required), the database contains more than 460 organisms.…”

Computational Challenges in Deciphering Genomic Structures of Bacteria

“…So we need to constantly remind ourselves about this when attempting to bridge the gap! In the rest of the article, I will discuss the information that is potentially derivable from genome sequence alone in the foreseeable future, which will need to be integrated, when attempting to understand the whole picture, with information derivable based on other experimental data such as (a) microarray gene expression data [76][77] for inference of the transcription subsystem and associated regulatory subsystem in a cell, (b) tiling arrays [78][79] for identification of cis regulatory elements of operons, (c) ChIP on chip data [80] for identification of interaction partners between transcription regulators and their cis regulatory elements, (d) proteomic data measuring the presence and the quantities of proteins under specific conditions typically collected using mass spectrometry techniques [81][82] , (e) metabolomic data measuring the metabolites as the results of metabolic reactions and their quantities using mass spectrometry or nuclear magnetic resonance (NMR) techniques [83][84] , (f) protein interaction data generated using techniques like yeast two-hybrid arrays [85] or pull-down approaches [86][87] , (g) protein and complex structures generated using X-ray crystallography [88][89] , NMR or electronic cryo-microscopy techniques [90][91] , which can provide detailed information such as how a protein executes a particular reaction, and (h) imaging data for tracing the movements of bio-molecules inside a cell, as well as information derived through systems level modeling and simulational studies as outlined earlier.…”