Dataset Information

DINGO: differential network analysis in genomics.

ABSTRACT:

Motivation

Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups.

Method

We propose a pathway-based differential network analysis in genomics (DINGO) model for estimating group-specific networks and making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development.

Results

Simulation studies demonstrate that DINGO provides more accurate group-specific conditional dependencies than achieved by using separate estimation approaches. We apply DINGO to key signaling pathways in glioblastoma to build differential networks for long-term survivors and short-term survivors in The Cancer Genome Atlas. The hub genes found by mRNA expression, DNA copy number, methylation and microRNA expression reveal several important roles in glioblastoma progression.

Availability and implementation

R Package at: odin.mdacc.tmc.edu/?vbaladan.

Contact

veera@mdanderson.org

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Ha MJ

PROVIDER: S-EPMC4751246 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DINGO: differential network analysis in genomics.

Ha Min Jin MJ Baladandayuthapani Veerabhadran V Do Kim-Anh KA

Bioinformatics (Oxford, England) 20150706 21

<h4>Motivation</h4>Cancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consi ...[more]

PMID: 26148744

Similar Datasets

Project description:BACKGROUND:Functional genomics employs several experimental approaches to investigate gene functions. High-throughput techniques, such as loss-of-function screening and transcriptome profiling, allow to identify lists of genes potentially involved in biological processes of interest (so called hit list). Several computational methods exist to analyze and interpret such lists, the most widespread of which aim either at investigating of significantly enriched biological processes, or at extracting significantly represented subnetworks. RESULTS:Here we propose a novel network analysis method and corresponding computational software that employs the shortest path approach and centrality measure to discover members of molecular pathways leading to the studied phenotype, based on functional genomics screening data. The method works on integrated interactomes that consist of both directed and undirected networks - HIPPIE, SIGNOR, SignaLink, TFactS, KEGG, TransmiR, miRTarBase. The method finds nodes and short simple paths with significant high centrality in subnetworks induced by the hit genes and by so-called final implementers - the genes that are involved in molecular events responsible for final phenotypic realization of the biological processes of interest. We present the application of the method to the data from miRNA loss-of-function screen and transcriptome profiling of terminal human muscle differentiation process and to the gene loss-of-function screen exploring the genes that regulates human oxidative DNA damage recognition. The analysis highlighted the possible role of several known myogenesis regulatory miRNAs (miR-1, miR-125b, miR-216a) and their targets (AR, NR3C1, ARRB1, ITSN1, VAV3, TDGF1), as well as linked two major regulatory molecules of skeletal myogenesis, MYOD and SMAD3, to their previously known muscle-related targets (TGFB1, CDC42, CTCF) and also to a number of proteins such as C-KIT that have not been previously studied in the context of muscle differentiation. The analysis also showed the role of the interaction between H3 and SETDB1 proteins for oxidative DNA damage recognition. CONCLUSION:The current work provides a systematic methodology to discover members of molecular pathways in integrated networks using functional genomics screening data. It also offers a valuable instrument to explain the appearance of a set of genes, previously not associated with the process of interest, in the hit list of each particular functional genomics screening.

Project description:Biological and medical sciences are increasingly acknowledging the significance of gene co-expression-networks for investigating complex-systems, phenotypes or diseases. Typically, complex phenotypes are investigated under varying conditions. While approaches for comparing nodes and links in two networks exist, almost no methods for the comparison of multiple networks are available and-to best of our knowledge-no comparative method allows for whole transcriptomic network analysis. However, it is the aim of many studies to compare networks of different conditions, for example, tissues, diseases, treatments, time points, or species. Here we present a method for the systematic comparison of an unlimited number of networks, with unlimited number of transcripts: Co-expression Differential Network Analysis (CoDiNA). In particular, CoDiNA detects links and nodes that are common, specific or different among the networks. We developed a statistical framework to normalize between these different categories of common or changed network links and nodes, resulting in a comprehensive network analysis method, more sophisticated than simply comparing the presence or absence of network nodes. Applying CoDiNA to a neurogenesis study we identified candidate genes involved in neuronal differentiation. We experimentally validated one candidate, demonstrating that its overexpression resulted in a significant disturbance in the underlying gene regulatory network of neurogenesis. Using clinical studies, we compared whole transcriptome co-expression networks from individuals with or without HIV and active tuberculosis (TB) and detected signature genes specific to HIV. Furthermore, analyzing multiple cancer transcription factor (TF) networks, we identified common and distinct features for particular cancer types. These CoDiNA applications demonstrate the successful detection of genes associated with specific phenotypes. Moreover, CoDiNA can also be used for comparing other types of undirected networks, for example, metabolic, protein-protein interaction, ecological and psychometric networks. CoDiNA is publicly available as an R package in CRAN (https://CRAN.R-project.org/package=CoDiNA).

Project description:BackgroundLarge-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene's expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.ResultsWe demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient "over-shrinkage" method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.ConclusionsBy adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.

Project description:BackgroundThe Australian dingo continues to cause debate amongst Aboriginal people, pastoralists, scientists and the government in Australia. A lingering controversy is whether the dingo has been tamed and has now reverted to its ancestral wild state or whether its ancestors were domesticated and it now resides on the continent as a feral dog. The goal of this article is to place the discussion onto a theoretical framework, highlight what is currently known about dingo origins and taxonomy and then make a series of experimentally testable organismal, cellular and biochemical predictions that we propose can focus future research.DiscussionWe consider a canid that has been unconsciously selected as a tamed animal and the endpoint of methodical or what we now call artificial selection as a domesticated animal. We consider wild animals that were formerly tamed as untamed and those wild animals that were formerly domesticated as feralized. Untamed canids are predicted to be marked by a signature of unconscious selection whereas feral animals are hypothesized to be marked by signatures of both unconscious and artificial selection. First, we review the movement of dingo ancestors into Australia. We then discuss how differences between taming and domestication may influence the organismal traits of skull morphometrics, brain and size, seasonal breeding, and sociability. Finally, we consider cellular and molecular level traits including hypotheses concerning the phylogenetic position of dingoes, metabolic genes that appear to be under positive selection and the potential for micronutrient compensation by the gut microbiome.ConclusionsWestern Australian Government policy is currently being revised to allow the widespread killing of the Australian dingo. These policies are based on an incomplete understanding of the evolutionary history of the canid and assume the dingo is feralized. However, accumulated evidence does not definitively show that the dingo was ever domesticated and additional focused research is required. We suggest that incorporating ancient DNA data into the debate concerning dingo origins will be pivotal to understanding the evolutionary history of the canid. Further, we advocate that future morphological, behavioural and genetic studies should focus on including genetically pure Alpine and Desert dingoes and not dingo-dog hybrids. Finally, we propose that future studies critically examine genes under selection in the dingo and employ the genome from a wild canid for comparison.

Dataset Information

DINGO: differential network analysis in genomics.

Motivation

Method

Results

Availability and implementation

Contact

Supplementary information

Publications

DINGO: differential network analysis in genomics.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets