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Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select truepositive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST. Fisher's method | mixture distribution models

Author(s): Mouse Genome Sequencing Consortium ; Genome Sequencing Center: ; Asif T. Chinwalla [1, 47]; Lisa L. Cook [1]; Kimberly D. Delehaunty [1]; Ginger A. Fewell [1]; Lucinda A. Fulton [...]

We present the current form of a provisional DNA sequence-based regulatory gene network that explains in outline how endomesodermal specification in the sea urchin embryo is controlled. The model of the network is in a continuous process of revision and growth as new genes are added and new experimental results become available; see http://www.its.cahech.edu/~mirsky/endomeso.htm (End-mes Gene Network Update) for the latest version. The network contains over 40 genes at present, many newly uncovered in the course of this work, and most encoding DNA-binding transcriptional regulatory factors. The architecture of the network was approached initially by construction of a logic model that integrated the extensive experimental evidence now available on endomesoderm specification. The internal linkages between genes in the network have been determined functionally, by measurement of the effects of regulatory perturbations on the expression of all relevant genes in the network. Five kinds of perturbation have been applied: (1) use of morpholino antisense oligonucleotides targeted to many of the key regulatory genes in the network; (2) transformation of other regulatory factors into dominant repressors by construction of Engrailed repressor domain fusions; (3) ectopic expression of given regulatory factors, from genetic expression constructs and from injected mRNAs; (4) blockade of the [beta]-catenin/Tcf pathway by introduction of mRNA encoding the intracellular domain of cadherin; and (5) blockade of the Notch signaling pathway by introduction of mRNA encoding the extracellular domain of the Notch receptor. The network model predicts the cis-regulatory inputs that link each gene into the network. Therefore, its architecture is testable by cis-regulatory analysis. Strongylocentroms purpuratus and Lytechinus variegatus genomic BAC recombinants that include a large number of the genes in the network have been sequenced and annotated. Tests of the cis-regulatory predictions of the model are greatly facilitated by interspecific computational sequence comparison, which affords a rapid identification of likely cis-regulatory elements in advance of experimental analysis. The network specifies genomically encoded regulatory processes between early cleavage and gastrula stages. These control the specification of the micromere lineage and of the initial [veg.sub.2] endomesodermal domain; the blastula-stage separation of the central [veg.sub.2] mesodermal domain (i.e., the secondary mesenchyme progenitor field) from the peripheral [veg.sub.2] endodermal domain; the stabilization of specification state within these domains; and activation of some downstream differentiation genes. Each of the temporal--spatial phases of specification is represented in a subelement of the network model, that treats regulatory events within the relevant embryonic nuclei at particular stages. Key Words: gene network; sea urchin embryo; gene regulation.

The investigation and modeling of gene regulatory networks requires computational tools specific to the task. We present several locally developed software tools that have been used in support of our ongoing research into the embryogenesis of the sea urchin. These tools are especially well suited to iterative refinement of models through experimental and computational investigation. They include: BioArray, a macroarray spot processing program; SUGAR, a system to display and correlate large-BAC sequence analyses; SeqComp and FamilyRelations, programs for comparative sequence analysis; and NetBuilder, an environment for creating and analyzing models of gene networks. We also present an overview of the process used to build our model of the Strongylocentrotus purpuratus endomesoderm gene network. Several of the tools discussed in this paper are still in active development and some are available as open source. Key Words: sequence annotation; comparative analysis; macroarrays; gene network modeling; computational tools.

Abstract Background CADM1 encodes an immunoglobulin superfamily (IGSF) cell adhesion molecule. Inactivation of CADM1, either by promoter hypermethylation or loss of heterozygosity, has been reported in a wide variety of tumor types, thus it has been postulated as a tumor suppressor gene. Findings We show for the first time that Cadm1 homozygous null mice die significantly faster than wildtype controls due to the spontaneous development of tumors at an earlier age and an increased tumor incidence of predominantly lymphomas, but also some solid tumors. Tumorigenesis was accelerated after irradiation of Cadm1 mice, with the reduced latency in tumor formation suggesting there are genes that collaborate with loss of Cadm1 in tumorigenesis. To identify these co-operating genetic events, we performed a Sleeping Beauty transposon-mediated insertional mutagenesis screen in Cadm1 mice, and identified several common insertion sites (CIS) found specifically on a Cadm1-null background (and not wildtype background). Conclusion We confirm that Cadm1 is indeed a bona fide tumor suppressor gene and provide new insights into genetic partners that co-operate in tumorigenesis when Cadm1-expression is lost.

Abstract Background As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site http://www.innateImmunity-systemsbiology.org. Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens. Description We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser. Conclusion We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at http://db.systemsbiology.net/IIDB.

The article discusses the role of lentiviral vector‐based insertional mutagenesis screen in identifying mechanisms of resistance to MAPK inhibitors in melanoma. It mentions that insertional mutagenesis is a forward genetic screening approach that uses specialized vectors which integrate into the genome. It reveals use of a BRAFV600E short‐term melanoma culture that had been characterized for somatic mutations.

Prostate cancer (CaP) is the most common adult male cancer in the developed world. The paucity of biomarkers to predict prostate tumor biology makes it important to identify key pathways that confer poor prognosis and guide potential targeted therapy. Using a murine forward mutagenesis screen in a Pten-null background, we identified peroxisome proliferator-activated receptor gamma (Pparg), encoding a ligand-activated transcription factor, as a promoter of metastatic CaP through activation of lipid signaling pathways, including up-regulation of lipid synthesis enzymes [fatty acid synthase (FASN), acetyl-CoA carboxylase (ACC), ATP citrate lyase (ACLY)]. Importantly, inhibition of PPARG suppressed tumor growth in vivo, with down-regulation of the lipid synthesis program. We show that elevated levels of PPARG strongly correlate with elevation of FASN in human CaP and that high levels of PPARG/FASN and PI3K/ pAKT pathway activation confer a poor prognosis. These data suggest that CaP patients could be stratified in terms of PPARG/FASN and PTEN levels to identify patients with aggressive CaP who may respond favorably to PPARG/FASN inhibition. [ABSTRACT FROM AUTHOR]

Mutations in SMAD4 predispose to the development of gastrointestinal cancer, which is the third leading cause of cancer-related deaths. To identify genes driving gastric cancer (GC) development, we performed a Sleeping Beauty (SB) transposon mutagenesis screen in the stomach of Smad4+/- mutant mice. This screen identified 59 candidate GC trunk drivers and a much larger number of candidate GC progression genes. Strikingly, 22 SB-identified trunk drivers are known or candidate cancer genes, whereas four SB-identified trunk drivers, including PTEN, SMAD4, RNF43, and NF1, are known human GC trunk drivers. Similar to human GC, pathway analyses identified WNT, TGF-β, and PI3K-PTEN signaling, ubiquitin- mediated proteolysis, adherens junctions, and RNA degradation in addition to genes involved in chromatin modification and organization as highly deregulated pathways in GC. Comparative oncogenomic filtering of the complete list of SB-identified genes showed that they are highly enriched for genes mutated in human GC and identified many candidate human GC genes. Finally, by comparing our complete list of SB-identified genes against the list of mutated genes identified in five large-scale human GC sequencing studies, we identified LDL receptor-related protein 1B (LRP1B) as a previously unidentified human candidate GC tumor suppressor gene. In LRP1B, 129 mutations were found in 462 human GC samples sequenced, and LRP1B is one of the top 10 most deleted genes identified in a panel of 3,312 human cancers. SB mutagenesis has, thus, helped to catalog the cooperative molecular mechanisms driving SMAD4-induced GC growth and discover genes with potential clinical importance in human GC. [ABSTRACT FROM AUTHOR]