Gene co-expression network analysis of transcriptome data has enabled the identification of key genes and important networks underlying complex production and disease traits. This study used weighted gene co-expression network analysis (WGCNA) approach to (1) detect modules or clusters of differentially expressed genes (DEG) with similar expression patterns in calf rumen transcriptome during pre- and post-weaning periods and (2) identify regulatory mechanisms linking gene modules to relevant phenotypes during the pre-weaning period (day 33 [d33]): weight gain (BWT_d33), average daily gain (ADG_d33), blood glucose (Glucose_d33) and β-hydroxybutyrate (BHB_d33) concentrations and post-weaning period (d96): weight gain (BWT_d96), average daily gain (ADG_d96), blood glucose (Glucose_d96) and β-hydroxybutyrate (BHB_d96) concentrations, dry matter intake (DMI_d96) and feed efficiency (FE_d96). Rumen tissues were collected from 16 calves on d33 and another 16 on d96 for whole transcriptome sequencing ...
Cervical cancer is the most common gynecologic malignant tumor, with a high incidence in 50-55-year-olds. This study aims to investigate the potential molecular mechanism of RRM2 for promoting the development of cervical cancer based on The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). RRM2 was found to be significant upregulated in cervical tissue (P,0.05) by extracting the expression of RRM2 from TCGA, GSE63514, GSE7410, GSE7803 and GSE9750. Survival analysis indicated that the overall survival was significantly worse in the patients with high-expression of RRM2 (P,0.05). The top 1000 positively/negatively correlated genes with RRM2 by Pearson Correlation test were extracted. The gene co-expression network by Weighted Gene Co-Expression Network Analysis (WGCNA) with these genes and the clinical characteristics (lymphocyte infiltration, monocyte infiltration, necrosis, neutrophil infiltration, the number of normal/stromal/tumor cells and the number of tumor nuclei) was ...
Recent work has revealed that a core group of transcription factors (TFs) regulates the key characteristics of embryonic stem (ES) cells: pluripotency and self-renewal. Current efforts focus on identifying genes that play important roles in maintaining pluripotency and self-renewal in ES cells and aim to understand the interactions among these genes. To that end, we investigated the use of unsigned and signed network analysis to identify pluripotency and differentiation related genes. We show that signed networks provide a better systems level understanding of the regulatory mechanisms of ES cells than unsigned networks, using two independent murine ES cell expression data sets. Specifically, using signed weighted gene co-expression network analysis (WGCNA), we found a pluripotency module and a differentiation module, which are not identified in unsigned networks. We confirmed the importance of these modules by incorporating genome-wide TF binding data for key ES cell regulators. Interestingly, we find
Table_6_Weighted Gene Co-expression Network Analysis Identifies Critical Genes for the Production of Cellulase and Xylanase in Penicillium oxalicum.XLSX
The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our
TY - JOUR. T1 - Co-expression network analysis of peripheral blood transcriptome identifies dysregulated protein processing in endoplasmic reticulum and immune response in recurrent MDD in older adults. AU - Ciobanu, Liliana G.. AU - Sachdev, Perminder S.. AU - Trollor, Julian N.. AU - Reppermund, Simone. AU - Thalamuthu, Anbupalam. AU - Mather, Karen A.. AU - Cohen-Woods, Sarah Louise. AU - Stacey, David. AU - Toben, Catherine. AU - Schubert, Klaus Oliver. AU - Baune, Bernhard T.. PY - 2018/12. Y1 - 2018/12. N2 - The molecular factors involved in the pathophysiology of major depressive disorder (MDD) remain poorly understood. One approach to examine the molecular basis of MDD is co-expression network analysis, which facilitates the examination of complex interactions between expression levels of individual genes and how they influence biological pathways affected in MDD. Here, we applied an unsupervised gene-network based approach to a prospective experimental design using microarray ...
Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. We constructed a genome-wide Arabidopsis gene co-expression network (AGCN) by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological
To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in ...
Numerous methods have been developed for inferring gene regulatory networks from expression data, however, both their absolute and comparative performance remain poorly understood. In this paper, we introduce a framework for critical performance assessment of methods for gene network inference. We present an in silico benchmark suite that we provided as a blinded, community-wide challenge within the context of the DREAM (Dialogue on Reverse Engineering Assessment and Methods) project. We assess the performance of 29 gene-network-inference methods, which have been applied independently by participating teams. Performance profiling reveals that current inference methods are affected, to various degrees, by different types of systematic prediction errors. In particular, all but the best-performing method failed to accurately infer multiple regulatory inputs (combinatorial regulation) of genes. The results of this community-wide experiment show that reliable network inference from gene expression ...
en] Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs.quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values ...
en] Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs.quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve (AUPR). By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values ...
The epithelial to mesenchymal transition (EMT) plays a key role in lung cancer progression and drug resistance. The dynamics and stability of gene expression patterns as cancer cells transition from E to M at a systems level and relevance to patient outcomes are unknown. Using comparative network and clustering analysis, we systematically analyzed time-series gene expression data from lung cancer cell lines H358 and A549 that were induced to undergo EMT. We also predicted the putative regulatory networks controlling EMT expression dynamics, especially for the EMT-dynamic genes and related these patterns to patient outcomes using data from TCGA. Example EMT hub regulatory genes were validated using RNAi. We identified several novel genes distinct from the static states of E or M that exhibited temporal expression patterns or periods during the EMT process that were shared in different lung cancer cell lines. For example, cell cycle and metabolic genes were found to be similarly down-regulated where
This video was recorded at 6th International Workshop on Machine Learning in Systems Biology (MLSB), Basel 2012. Motivation: Transcriptional regulatory network inference methods have been studied for years. Most of them relie on complex mathematical and algorithmic concepts, making them hard to adapt, re- implement or integrate with other methods. To address this problem, we introduce a novel method based on a minimal statistical model for observing transcriptional regulatory interactions in noisy expression data, which is conceptually simple, easy to implement and integrate in any statistical software environment, and equally well performing as existing methods. Results: We developed a method to infer regulatory interactions based on a model where transcription factors (TFs) and their targets are both differentially expressed in a gene-specific, critical sample contrast, as measured by repeated two-way t-tests. Benchmarking on standard E. coli and yeast reference datasets showed that this ...
Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues
Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC). Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes. Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic
Anxiety disorders are the most common psychiatric disorders, and the change in the activity of the prefrontal cortex (PFC) is considered as the underlying pathological mechanism. Parvalbumin-expressing (PV+) inhibition contributes to the overall activity of the PFC. However, the molecular mechanism underlying the excitation-inhibition imbalance of PV+ neurons in the PFC is unknown. Efnb2 is a membrane-bound molecule that plays an important role in the nervous system through binding the Eph receptor. To investigate whether the loss of Efnb2 in PV+ affects anxiety, we examined the behavior of wild type and Efnb2 in PV+ neurons knockout (KO) mice. We monitored the defensive responses to aversive stimuli of elevated plus maze (EPM) and found that KO mice exhibited obvious fearless and anxiolytic behaviors. To further investigate the underlying regulatory mechanism, we performed RNA sequencing, analyzed the differentially expressed genes (DEGs), and constructed the weighted gene co-expression network
Gene regulation is accomplished mainly by the interplay of multiple transcription factors. This gives rise to highly complex and cell-type specific, interwoven structures of regulatory interactions summarized in gene regulatory networks. In this thesis, I address two approaches of computational analysis of such networks, forward modeling and reverse engineering. The first part of this thesis is about the Web application GEne Network GEnerator (GeNGe) which I have developed as a framework for automatic generation of gene regulatory network models. I have developed a novel algorithm for the generation of network structures featuring important biological properties. In order to model the transcriptional kinetics, I have modified an existing non-linear kinetic. This new kinetic is particularly useful for the computational set-up of complex gene regulatory models. GeNGe supports also the generation of various in silico experiments for predicting effects of perturbations as theoretical counterparts of ...
Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using
TY - JOUR. T1 - A genome-wide cis-regulatory element discovery method based on promoter sequences and gene co-expression networks. AU - Gao, Zhen. AU - Zhao, Ruizhe. AU - Ruan, Jianhua. PY - 2013/1/21. Y1 - 2013/1/21. N2 - Background: Deciphering cis-regulatory networks has become an attractive yet challenging task. This paper presents a simple method for cis-regulatory network discovery which aims to avoid some of the common problems of previous approaches. Results: Using promoter sequences and gene expression profiles as input, rather than clustering the genes by the expression data, our method utilizes co-expression neighborhood information for each individual gene, thereby overcoming the disadvantages of current clustering based models which may miss specific information for individual genes. In addition, rather than using a motif database as an input, it implements a simple motif count table for each enumerated k-mer for each gene promoter sequence. Thus, it can be used for species where ...
BACKGROUND: Psoriatic arthritis (PsA) is inflammatory arthritis associated with psoriasis, which involves the axial joint and the distal interphalangeal joints. Its clinical features are varied, often resulting in delayed diagnosis and treatment. Improved knowledge about disease mechanisms will catalyze the rapid development of effective targeted therapies for this disease. The perturbations in the gene co-expression network may not be detected by the differential expression analysis of the microarray. This study aims to identify key modules and hub genes in psoriatic arthritis-applied WGCNA (weighted gene co-expression network analysis) on a microarray. METHODS: This study downloaded the array data of GSE61281 from the gene expression overview (GEO) database, which includes 20 psoriatic arthritis samples and 12 healthy controls. The analysis was performed with the WGCNA package. Gene ontology (GO) annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were ...
GRNsight is a web application and service for visualizing models of gene regulatory networks (GRNs). A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them which govern the level of expression of mRNA and protein from genes. The original motivation came from our efforts to perform parameter estimation and forward simulation of the dynamics of a differential equations model of a small GRN with 21 nodes and 31 edges. We wanted a quick and easy way to visualize the weight parameters from the model which represent the direction and magnitude of the influence of a transcription factor on its target gene, so we created GRNsight. GRNsight automatically lays out either an unweighted or weighted network graph based on an Excel spreadsheet containing an adjacency matrix where regulators are named in the columns and target genes in the rows, a Simple Interaction Format (SIF) text file, or a GraphML XML file. When a user uploads an input file specifying
Title: Validation of Inference Procedures for Gene Regulatory Networks. VOLUME: 8 ISSUE: 6. Author(s):Edward R. Dougherty. Affiliation:Department of Electrical and Computer Engineering, Texas A University, College Station, TX 77843-3128, USA.. Keywords:Epistemology, gene network, inference, validation. Abstract: The availability of high-throughput genomic data has motivated the development of numerous algorithms to infer gene regulatory networks. The validity of an inference procedure must be evaluated relative to its ability to infer a model network close to the ground-truth network from which the data have been generated. The input to an inference algorithm is a sample set of data and its output is a network. Since input, output, and algorithm are mathematical structures, the validity of an inference algorithm is a mathematical issue. This paper formulates validation in terms of a semi-metric distance between two networks, or the distance between two structures of the same kind deduced from ...
Gene expression network analysis and applications to immunology - We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set.
This function fits iRafNet, a flexible unified integrative algorithm that allows information from prior data, such as protein-protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference. This function takes as input only one set of sampling scores, computed considering one prior data such as protein-protein interactions or gene expression from knock-out experiments. Note that some of the functions utilized are a modified version of functions contained in the R package randomForest (A. Liaw and M. Wiener, 2002).
There are various factors that alter physiological characteristics in skin. Elucidating the underlying mechanism of transcriptional alterations by intrinsic and extrinsic factors may lead us to understand the aging process of skin. To identify the transcriptomic changes of the aging skin, we analyzed publicly available RNA sequencing data from Genotype-Tissue Expression (GTEx) project. GTEx provided RNA sequencing data of suprapubic (n=228) and lower leg (n=349) skins, which are photo-protected and photo-damaged. Using differentially expressed gene analysis and weighted gene co-expression network analysis, we characterized transcriptomic changes due to UV exposure and aging. Genes involved in skin development such as epidermal differentiation complex component (SPRR and LCE families), vasculature development (TGFBR1, TGFBR2, TGFBR3, KDR, FGF2, and VEGFC), and matrix metalloproteinase (MMP2, MMP3, MMP8, MMP10, and MMP13) were up
TY - JOUR. T1 - Inflammatory, regulatory, and autophagy co-expression modules and hub genes underlie the peripheral immune response to human intracerebral hemorrhage. AU - Durocher, Marc. AU - Ander, Bradley P.. AU - Jickling, Glen Clifford. AU - Hamade, Farah. AU - Hull, Heather. AU - Knepp, Bodie. AU - Liu, Da Zhi. AU - Zhan, Xinhua. AU - Tran, Anh. AU - Cheng, Xiyuan. AU - Ng, Kwan. AU - Yee, Alan. AU - Sharp, Frank R.. AU - Stamova, Boryana. PY - 2019/3/5. Y1 - 2019/3/5. N2 - Background: Intracerebral hemorrhage (ICH) has a high morbidity and mortality. The peripheral immune system and cross-talk between peripheral blood and brain have been implicated in the ICH immune response. Thus, we delineated the gene networks associated with human ICH in the peripheral blood transcriptome. We also compared the differentially expressed genes in blood following ICH to a prior human study of perihematomal brain tissue. Methods: We performed peripheral blood whole-transcriptome analysis of ICH and matched ...
Primary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF. Firstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database. We performed weighted correlation network and differential
This brief examines a deterministic, ODE-based model for gene regulatory networks (GRN) that incorporates nonlinearities and time-delayed feedback. An introductory chapter provides some insights into molecular biology and GRNs. The mathematical tools necessary for studying the GRN model are then
Enriched gene interaction network detected with opposite expression patterns in PZQ-treated paired or unpaired mature females.This gene interaction network is s
In a variety of solid cancers, missense mutations in the well-established TP53 tumour suppressor gene may lead to presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumour biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of co-expression of genes in tumours grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the
Animals consist of a wide variety of cells that serve different functions depending on their location in the body. Cells with similar functions, or cell types, in different animal species are related both by an evolutionary line of descentÐsimilar to the relatedness of species themselvesÐand by a developmental line of descent in the embryo. Networks of interacting genes, or gene regulatory networks, control gene expression in the cell, thereby specifying cell type identity. Understanding how new cell types arise by changing gene regulatory networks is critical both to comprehending fundamental aspects of human biology and to manipulating cell types in the laboratory. We approached this question by studying endometrial stromal fibroblast (ESF) cells from the uterus of humans and opossums, two distantly related mammals. We showed that the distantly related cell type in opossum expresses a similar set of regulatory genes as the human cell, but in response to pregnancy-related signals, the opossum ...
We data-analyzed and constructed the high-expression CAMK1 phosphoinositide signal-mediated protein sorting and transport network in human hepatocellular carcinoma (HCC) compared with low-expression (fold change ≥ 2) no-tumor hepatitis/cirrhotic tissues (HBV or HCV infection) in GEO data set, using integration of gene regulatory network inference method with gene ontology (GO). Our result showed that CAMK1 transport subnetwork upstream KCNQ3, LCN2, NKX2_5, NUP62, SORT1, STX1A activated CAMK1, and downstream CAMK1-activated AFP, ENAH, KPNA2, SLC4A3; CAMK1 signal subnetwork upstream BRCA1, DKK1, GPSM2, LEF1, NR5A1, NUP62, SORT1, SSTR5, TBL3 activated CAMK1, and downstream CAMK1-activated MAP2K6, SFRP4, SSTR5, TSHB, UBE2C in HCC. We proposed that CAMK1 activated network enhanced endosome to lysosome transport, endosome transport via multivesicular body sorting pathway, Golgi to endosome transport, intracellular protein transmembrane transport, intracellular protein transport, ion transport, mRNA ...
Gene regulatory networks (GRN) are being studied with increasingly precise quantitative tools and can provide a testing ground for ideas regarding the emergence and evolution of complex biological networks. We analyze the global statistical properties of the transcriptional regulatory network of the prokaryote Escherichia coli, identifying each operon with a node of the network. We propose a null model for this network using the content-based approach applied earlier to the eukaryote Saccharomyces cerevisiae (Balcan et al., 2007). Random sequences that represent promoter regions and binding sequences are associated with the nodes. The length distributions of these sequences are extracted from the relevant databases. The network is constructed by testing for the occurrence of binding sequences within the promoter regions. The ensemble of emergent networks yields an exponentially decaying in-degree distribution and a putative power law dependence for the out-degree distribution with a flat tail, in
A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included
A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data. Standard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or
Author Summary A regulatory protein can activate the expression of a target gene either directly, i.e., by binding to the genes promoter, or indirectly, i.e., by altering the expression of regulators, which, in turn, bind to the target genes promoter and induce or inhibit its transcription. Indirect regulatory circuits can contain multiple components and functional elements, such as feedforward and feedback loops. The complex structure of indirect regulation raises the question of its evolutionary origins. Here, we study the dynamic and evolutionary properties of regulatory architectures that involve members of the recently emerged class of bacterial proteins termed connectors. Such proteins post-translationally modulate the activity of two-component systems and phosphorelays, which constitute the prevalent form of bacterial signal transduction. We describe a novel connector-mediated regulatory circuit that combines the structural and functional properties of direct and indirect regulation. Our
Root nodule symbioses (nodulation) and whole genome duplication (WGD, polyploidy) are both important phenomena in the legume family (Leguminosae). Recently, it has been proposed that polyploidy may have played a critical role in the origin or refinement of nodulation. However, while nodulation and polyploidy have been studied independently, there have been no direct studies of mechanisms affecting the interactions between these phenomena in symbiotic, nodule-forming species. Here, we examined the transcriptome-level responses to inoculation in the young allopolyploid Glycine dolichocarpa (T2) and its diploid progenitor species to identify underlying processes leading to the enhanced nodulation responses previously identified in T2. We assessed the differential expression of genes and, using weighted gene co-expression network analysis (WGCNA), identified modules associated with nodulation and compared their expression between species. These transcriptomic analyses revealed patterns of non-additive
This chapter presents a survey of recent methods for reconstruction of time-varying biological networks such as gene interaction networks based on time series node observations (e.g. gene expressions) from a modeling perspective. Time series gene expression data has been extensively used for analysis of gene interaction networks, and studying the influence of regulatory relationships on different phenotypes. Traditional correlation and regression based methods have focussed on identifying a single interaction network based on time series data. However, interaction networks vary over time and in response to environmental and genetic stress during the course of the experiment. Identifying such time-varying networks promises new insight into transient interactions and their role in the biological process. A key challenge in inferring such networks is the problem of high-dimensional data i.e. the number of unknowns p is much larger than the number of observations n. We discuss the computational aspects of
It is crucial to grasp the characteristics of tumour immune microenvironment to improve effects of immunotherapy. In this study, the immune and stromal scores of 371 cases were calculated for quantitative analysis of immune and stromal cell infiltration in the tumour microenvironment of hepatocellular carcinoma (HCC). The weighted gene co-expression network analysis and protein–protein interaction network were analysed to identify immune microenvironment-related genes. The results showed that patients with high immune scores had a higher 4-year recurrence-free rate. TP53, CTNNB1, and AXIN1 mutations significantly varied with immune scores. In immune score-related modules analysis, Kyoto encyclopaedia of genes and genomes pathways and gene ontology terms were closely related to immune processes, tumorigenesis, and metastasis. Twelve new immune microenvironment-related genes were identified and had significantly positive correlations with seven immune checkpoint genes. In prognostic analysis,
Gene co-epxression network analyses are common in the study of large scale biological data sets. In this study, we have developed a methodology for the comparison of pairs of co-expression networks using the s-core network peeling approach. We apply the methodology to gene-expression data for human and mouse. ...
Endosperm is an absorptive structure that supports embryo development or seedling germination in angiosperms. The endosperm of cereals is a main source of food, feed, and industrial raw materials worldwide. However, the genetic networks that regulate endosperm cell differentiation remain largely unclear. As a first step toward characterizing these networks, we profiled the mRNAs in five major cell types of the differentiating endosperm and in the embryo and four maternal compartments of the maize (Zea mays) kernel. Comparisons of these mRNA populations revealed the diverged gene expression programs between filial and maternal compartments and an unexpected close correlation between embryo and the aleurone layer of endosperm. Gene coexpression network analysis identified coexpression modules associated with single or multiple kernel compartments including modules for the endosperm cell types, some of which showed enrichment of previously identified temporally activated and/or imprinted genes. ...
Mason MJ, Fan G, Plath K, Zhou Q, Horvath S (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells BMC Genomics 2009, 10:327. ...
Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GRN dynamics are usually investigated via stochastic models since molecular interactions are basically discrete and stochastic processes. However, this stochastic nature requires heavy simulation time to find the steady-state solution of the GRNs where thousands of genes are involved. This large number of genes also causes difficulties such as dimensionality problem in estimating their regulatory structure. ...
It is now well established that the study of biological complexity has shifted from gene level to interaction networks and this shift from components to associated interactions has gained increasing interest in network biology. Gene Regulatory Networks (GRNs) depict the functioning circuitry in organisms at the gene level and represent an abstract mapping of the more complicated biochemical network which includes other components such as proteins, metabolites, etc. Understanding GRNs can provide new ideas for treating complex diseases and offer novel candidate drug targets. A commonly accepted top-down approach is to reverse engineer GRNs from experimental data generated by microarray technology [1-5].. Early computational approaches for inferring GRNs from gene expression data employed classical methods. Boolean network modeling considers the gene expression to be in a binary state (either switched on or off), and display via a Boolean function the impact of other genes on a specific target ...
Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best
Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, … with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species ...
In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored. We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes. Although the individual networks represent different levels of cellular interactions with global structural and
Eric H. Davidson, PhD, has been awarded the International Prize for Biology from the Japan Society for the Promotion of Science for his research related to understanding gene regulatory networks, particularly as it pertains to embryonic development. Dr. Davidson is Norman Chandler Professor of Cell Biology in the Division of Biology at the California Institute of Technology in Pasadena.. Instead of focusing on individual genes, Dr. Davidson has focused his studies on a broad approach to understanding how groups of genes are regulated and interact to establish and maintain the developmental program that underlies developmental processes. This approach has proven critical to understanding how a single cell can ultimately give rise to multiple specialized cells and tissues with an array of diverse functions.. As their model system, Dr. Davidson and his colleagues have used the sea urchin to define and study these developmental gene regulatory networks. The production of hundreds of thousands of ...
TY - JOUR. T1 - Functional clustering of time series gene expression data by Granger causality. AU - Fujita, André. AU - Severino, Patricia. AU - Kojima, Kaname. AU - Sato, João R.. AU - Patriota, Alexandre G.. AU - Miyano, Satoru. PY - 2012/10/30. Y1 - 2012/10/30. N2 - Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its ...
TY - CHAP. T1 - Determining the properties of gene regulatory networks from expression data. AU - Liebovitch, Larry S.. AU - Shehadeh, Lina A.. AU - Jirsa, Viktor K.. AU - Hütt, Marc Thorsten. AU - Marr, Carsten. N1 - Copyright: Copyright 2014 Elsevier B.V., All rights reserved.. PY - 2009. Y1 - 2009. N2 - The expression of genes depends on the physical structure of DNA, how the function of DNA is regulated by the transcription factors expressed by other genes, RNA regulation, such as that through RNA interference, and protein signals mediated by protein-protein interaction networks. We illustrate different approaches to determining information about the network of gene regulation from experimental data. First, we show that we can use statistical information of the mRNA expression values to determine the global topological properties of the gene regulatory network. Second, we show that analyzing the changes in expression due to mutations or different environmental conditions can give us ...
A human disease network is a network of human disorders and diseases with reference to their genetic origins or other features. More specifically, it is the map of human disease associations referring mostly to disease genes. For example, in a human disease network, two diseases are linked if they share at least one associated gene. A typical human disease network usually derives from bipartite networks which consist of both diseases and genes information. Additionally, some human disease networks use other features such as symptoms and proteins to associate diseases. In 2007, Goh et al. constructed a disease-gene bipartite graph using information from OMIM database and termed human disease network. In 2009, Barrenas et al. derived complex disease-gene network using GWAs (Genome Wide Association studies). In the same year, Hidalgo et al. published a novel way of building human phenotypic disease networks in which diseases were connected according to their calculated distance. In 2011, Cusick et ...
Downloadable! In order to overcome the problem of free-riding in current P2P system, we suggest applying social network theory. Based on our exploration of the overlapping research fields of social networks and peer-to-peer networks, we propose a new P2P framework within this paper. It specifies social network information that can be used in a P2P system to avoid performance inefficiencies caused by free-riding or by policies to overcome free-riding. To identify this specific social network information, we conduct a survey among a small group of students, who use Skype, a popular P2P system. We use descriptive analysis and multiple regression analysis to analyze the survey data. The results of the analyses provide an indication that the idea of using social network information in P2P systems is valid and that it is supported by P2P users. Based on our findings, we make recommendations for a successful implementation of social-network-information-based P2P systems that can overcome free-riding issues and
Background: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. Methods: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. ...
Brachypodium distachyon is a close relative of many important cereal crops. Abiotic stress tolerance has a significant impact on productivity of agriculturally important food and feedstock crops. Analysis of the transcriptome of Brachypodium after chilling, high-salinity, drought, and heat stresses revealed diverse differential expression of many transcripts. Weighted Gene Co-Expression Network Analysis revealed 22 distinct gene modules with specific profiles of expression under each stress. Promoter analysis implicated short DNA sequences directly upstream of module members in the regulation of 21 of 22 modules. Functional analysis of module members revealed enrichment in functional terms for 10 of 22 network modules. Analysis of condition-specific correlations between differentially expressed gene pairs revealed extensive plasticity in the expression relationships of gene pairs. Photosynthesis, cell cycle, and cell wall expression modules were down-regulated by all abiotic stresses. Modules ...
The regulation of gene expression at the transcriptional level is a fundamental process in prokaryotes. Among the different kind of mechanisms modulating gene transcription, the one based on DNA binding transcription factors, is the most extensively studied and the results, for a great number of model organisms, have been compiled making it possible the in silico construction of their corresponding transcriptional regulatory networks and the analysis of the biological relationships of the components of these intricate networks, that allows to elucidate the significant aspects of their organization and evolution. We present a thorough review of each regulatory element that constitutes the transcriptional regulatory network of Bacillus subtilis. For facilitating the discussion, we organized the network in topological modules. Our study highlight the importance of σ factors, some of them acting as master regulators which characterize modules by inter- or intra-connecting them and play a key role in the
Title:Novel Study of Model-based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process. VOLUME: 12 Author(s):Farzane Ahmadi, Ali-Reza Abadi, Zahra Bazi and Abolfazl Movafagh*. Affiliation:Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Department of Community Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Department of Medical Genetics, School of Medicine, Cancer Research Center, Shohada Referral hospital, Shahid Beheshti University of Medical Sciences, Tehran. Keywords:Clustering; Mixtures of Matrix-Variate Normal Distributions; Aging; Time Series. Abstract:Background: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. ...
Autism spectrum disorder (ASD) is a common, highly heritable neuro-developmental condition characterized by marked genetic heterogeneity 1-3 . Thus, a fundamental question is whether autism represents an etiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain 4 . Here, we demonstrate consistent differences in transcriptome organization between autistic and normal brain by gene co-expression network analysis. Remarkably, regional patterns of gene expression that typically distinguish frontal and temporal cortex are significantly attenuated in the ASD brain, suggesting abnormalities in cortical patterning. We further identify discrete modules of co-expressed genes associated with autism: a neuronal module enriched for known autism susceptibility genes, including the neuronal specific splicing factor A2BP1/FOX1, and a module enriched for immune genes and glial markers. Using high-throughput RNA-sequencing we
In late-onset Alzheimers disease (AD), multiple brain regions are not affected simultaneously. Comparing the gene expression of the affected regions to identify the differences in the biological processes perturbed can lead to greater insight into AD pathogenesis and early characteristics. We identified differentially expressed (DE) genes from single cell microarray data of four AD affected brain regions: entorhinal cortex (EC), hippocampus (HIP), posterior cingulate cortex (PCC), and middle temporal gyrus (MTG). We organized the DE genes in the four brain regions into region-specific gene coexpression networks. Differential neighborhood analyses in the coexpression networks were performed to identify genes with low topological overlap (TO) of their direct neighbors. The low TO genes were used to characterize the biological differences between two regions. Our analyses show that increased oxidative stress, along with alterations in lipid metabolism in neurons, may be some of the very early ...
In the present study we examine the changes in the expression of genes of Lactococcus lactis subspecies cremoris MG1363 during growth in milk. To reveal which specific classes of genes (pathways, operons, regulons, COGs) are important, we performed a transcriptome time series experiment. Global analysis of gene expression over time showed that L. lactis adapted quickly to the environmental changes. Using upstream sequences of genes with correlated gene expression profiles, we uncovered a substantial number of putative DNA binding motifs that may be relevant for L. lactis fermentative growth in milk. All available novel and literature-derived data were integrated into network reconstruction building blocks, which were used to reconstruct and visualize the L. lactis gene regulatory network. This network enables easy mining in the chrono-transcriptomics data. A freely available website at http://milkts.molgenrug.nl gives full access to all transcriptome data, to the reconstructed network and to the ...
The combination of genomics, genetics, and systems-level computational methods provides a powerful approach toward insight into complex biological systems. Of particular significance is the discovery of genetic interactions that lead to desirable agricultural and economic traits in the Poaceae family (grasses). The Poaceae includes valuable crops such as rice (Oryza sativa), maize (Zea mays), wheat (Triticum spp.), and sugarcane (Saccharum officinarum), which are globally some of the most agriculturally and economically important crops (FAOSTAT, 2007). Understanding complex interactions underlying agronomic traits within these species, therefore, is of great significance, in particular to help with crop improvements to meet the challenges of plant and human health but also for basic understanding of complex biological systems.. In addition to their pivotal role in agriculture, grasses offer a powerful model system in that their genomes are closely conserved and functional genomic knowledge ...
1. Mirabello L, Troisi RJ, Savage SA. Osteosarcoma incidence and survival rates from 1973 to 2004: data from the Surveillance, Epidemiology, and End Results Program. Cancer-Am Cancer Soc. 2009;115:1531-1543 2. Bacci G, Longhi A, Versari M. et al. Prognostic factors for osteosarcoma of the extremity treated with neoadjuvant chemotherapy - 15-year experience in 789 patients treated at a single institution. Cancer-Am Cancer Soc. 2006;106:1154-1161 3. Ritter J, Bielack SS. Osteosarcoma. Ann Oncol. 2010;217:320-325 4. Lewis IJ, Nooij MA, Whelan J. et al. Improvement in histologic response but not survival in osteosarcoma patients treated with intensified chemotherapy: A randomized phase III trial of the European Osteosarcoma Intergroup. Journal of The National Cancer Institute. 2007;99:112-128 5. Ta HT, Dass CR, Choong PFM, Dunstan DE. Osteosarcoma treatment: state of the art. Cancer Metast Rev. 2009;28:247-263 6. Daw NC, Chou AJ, Jaffe N. et al. Recurrent osteosarcoma with a single pulmonary ...
Postdocatoral Research Fellow National University of Singapore NUS Graduate School for Integrative Sciences and Engineering and Department of Mathematics Singapore https://careers.iscb.org Description We are looking for three highly motivated postdoctoral researchers in computational analysis of gene regulatory networks (network robustness and topological properties) and its applications in cancer biology. Our research objective is to develop graph-based algorithms to study important biological questions in stem cell and cancer biology ...
Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect
TY - JOUR. T1 - Comparison and evaluation of network clustering algorithms applied to genetic interaction networks. AU - Hou, Lin. AU - Wang, Lin. AU - Berg, Arthur. AU - Qian, Minping. AU - Zhu, Yunping. AU - Li, Fangting. AU - Deng, Minghua. PY - 2012/1/1. Y1 - 2012/1/1. N2 - The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian ...
Pathway Tools is a comprehensive symbolic systems biology software system that supports several use cases in bioinformatics and systems biology: *Development of organism-specific databases called Pathway/Genome Databases (PGDBs) that integrate many bioinformatics datatypes, from genomes to pathways to regulatory networks. *Development of metabolic-flux models using flux-balance analysis *Scientific visualization, web publishing, and dissemination of those organism-specific databases, including: **Automatic display of metabolic pathways and full metabolic networks; generation of metabolic map diagram and of metabolic map poster ([http://bioinformatics.ai.sri.com/posters/ecoli-metab.pdf example]). **Genome browser; comparative genome browser; generation of genome poster ([http://bioinformatics.ai.sri.com/posters/ecoli-genome.pdf example]). **Display of operons, regulons, and full transcriptional regulatory networks *Analysis of omics datasets, including painting omics data on to diagrams of the ...
Maize leaves have distinct tissues that serve specific purposes. The blade tilts back to photosynthesize and the sheath wraps around the stem to provide structural support and protect young leaves. At the junction between blade and sheath are the ligule and auricles, both of which are absent in the recessive liguleless1 (lg1) mutant. Using an antibody against LG1, we reveal LG1 accumulation at the site of ligule formation and in the axil of developing tassel branches. The dominant mutant Wavy auricle in blade1 (Wab1-R) produces ectopic auricle tissue in the blade and increases the domain of LG1 accumulation. We determined that wab1 encodes a TCP transcription factor by positional cloning and revertant analysis. Tassel branches are few and upright in the wab1 revertant tassel and have an increased branch angle in the dominant mutant. wab1 mRNA is expressed at the base of branches in the inflorescence and is necessary for LG1 expression. wab1 is not expressed in leaves, except in the dominant ...
Grasses are characterized by a distinct leaf morphology that includes an enclosing sheath, a photosynthetic blade, two auricles and a ligule fringe. The first sign of a preligule band is a zone of division that occurs in a P6-P7 leaf primordium. These divisions are parallel and perpendicular to the long axis of the leaf. At about P7-P8, divisions occur that are periclinal to the surface, causing growth out of the plane of the leaf (Sharman, 1941; Sylvester et al., 1990). The periclinal divisions initiate the actual ligule and do not occur in the lg1-R leaf, leading to a failure of ligule and auricle formation (Sylvester et al., 1990). Previous work, in which RT-PCR (Foster et al., 2004; Moreno et al., 1997) and whole-mount in situ hybridization (Moon et al., 2013) was used, has shown that lg1 is expressed at the ligule. We used an antibody to follow the timing of LG1 accumulation at a cellular resolution. LG1 protein is visible in a band of ∼20 cells coincident with the first divisions. The ...
PubMed Central Canada (PMC Canada) provides free access to a stable and permanent online digital archive of full-text, peer-reviewed health and life sciences research publications. It builds on PubMed Central (PMC), the U.S. National Institutes of Health (NIH) free digital archive of biomedical and life sciences journal literature and is a member of the broader PMC International (PMCI) network of e-repositories.
In this article, we proposed the methodology of analyzing the distribution of gene functional properties in the context of statistical epistasis networks. The gene interaction network was constructed by first identifying the network of strong and significant pairwise SNP epistatic interactions and then building gene network on top of the SNP interaction network. After annotating genes as vertices based on their functional Gene Ontology, dyadicity and heterophilicity analysis was performed for each GO term to investigate to what degree the vertex characteristics correlate with the underlying interaction network topology. Using a population-based bladder cancer dataset and its previously identified SNP statistical epistasis network, we performed the dyadicity and heterophilicity analysis on enriched GO terms for the genes in the gene interaction network associated with bladder cancer. We were able to find 12 GO categories with significant dyadicity or heterophilicity, which indicated the ...
Bibliographic details on RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions.
Dr. Ganesh Sriram is Assistant Professor of Chemical and Biomolecular Engineering at University of Maryland. After earning his bachelors and masters degrees in chemical engineering from Indian Institute of Technology (IIT) Bombay, he graduated a Ph.D. in chemical engineering from Iowa State University in 2004, working in the lab of Dr. Jacqueline Shanks. From 2004 to 2007, he was a postdoctoral researcher in the labs of Dr. Katrina Dipple and Dr. James Liao at UCLA. Dr. Srirams research interests are metabolic engineering and systems biology, especially metabolic flux analysis and gene regulatory network analysis. See lab research page for more information. ...
Dr. Ganesh Sriram is Assistant Professor of Chemical and Biomolecular Engineering at University of Maryland. After earning his bachelors and masters degrees in chemical engineering from Indian Institute of Technology (IIT) Bombay, he graduated a Ph.D. in chemical engineering from Iowa State University in 2004, working in the lab of Dr. Jacqueline Shanks. From 2004 to 2007, he was a postdoctoral researcher in the labs of Dr. Katrina Dipple and Dr. James Liao at UCLA. Dr. Srirams research interests are metabolic engineering and systems biology, especially metabolic flux analysis and gene regulatory network analysis. See lab research page for more information. ...
No, not this Edison. Im talking about my software package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression)*.. What it does. Network inference. More precisely, network inference from time series data.. Imagine you have a dataset to analyse where a number of variables have been measured at regular intervals. You might ask, what are the relationships between these variables. In other words, if you were to link every two variables that depend on each other, what would the network you obtain look like. EDISON uses the observed data to infer the underlying network.. Now there are plenty of packages for network inference. What makes EDISON stand out is that it can also answer a second question: do the relationships between the variables change over time. The software package can identify changepoints in the time series of measurements where the structure of the underlying network changes.. Under the Hood. The underlying model for EDISON is a dynamic ...
Network inference of gene expression data is an important challenge in systems biology. Novel algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA)
Erich Grotewold, a professor of molecular genetics and horticulture and crop science at The Ohio State University, is leveraging the resources of the Ohio Supercomputer Center as part of his studies to address fundamentally important questions in plant research.. He and project co-principle investigators Andrea Doseff, an Ohio State Medical Center and Department of Molecular Genetics associate professor, and John Gray, an associate professor at the University of Toledo, are the recipients of a $4.23 million grant from the National Science Foundation Plant Genome Program to study Systems Approaches to Identify Gene Regulatory Networks in the Grasses.. Establishing gene regulatory networks and linking system components to agronomic traits is an important emerging theme in plant systems biology, Grotewold said. This is the first concerted effort to comprehensively dissect the gene regulatory networks that target the metabolism of phenolic compounds, found in maize, other cereal crops and ...
Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis. This study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed
TY - JOUR. T1 - Regulatory network modelling of iron acquisition by a fungal pathogen in contact with epithelial cells. AU - Linde, Jörg. AU - Wilson, Duncan. AU - Hube, Bernhard. AU - Guthke, Reinhard. PY - 2010/11/4. Y1 - 2010/11/4. N2 - Reverse engineering of gene regulatory networks can be used to predict regulatory interactions of an organism faced with environmental changes, but can prove problematic, especially when focusing on complicated multi-factorial processes. Candida albicans is a major human fungal pathogen. During the infection process, this fungus is able to adapt to conditions of very low iron availability. Such adaptation is an important virulence attribute of virtually all pathogenic microbes. Understanding the regulation of iron acquisition genes will extend our knowledge of the complex regulatory changes during the infection process and might identify new potential drug targets. Thus, there is a need for efficient modelling approaches predicting key regulatory events of ...
Reductionist philosophy has directed biological research for decades [1, 2]. A significant amount of information has been generated so far in the field of biological sciences as enrichment of human knowledgebase to understand life [1]. Despite enormous success of reductionism to decode the structural and functional attributes at cellular and molecular levels of life-organization, it is progressively becoming clearer that biological functions can rarely be credited to discrete perception of individual molecules. Alternatively, most biological phenomena emerge due to extremely interactive complexity derived from functional integrity of cells numerous constituents [2]. Various recent approaches have been initiated and accomplished to study biological systems in more integrative and comprehensive way. Network model can play an important role to understand the complex network system based on multiple sets of interactions and to make plain and clear analysis of the origin of observed network ...
Current network analysis packages tend to be standalone desktop tools which rely on local resources and whose operations are not easily integrated with other software and databases. A key contribution of this thesis is an extensible toolkit of biological network construction and analysis operations, developed as web services. Web services are a distributed technology that enable machine-to-machine interaction over a network, and promote interoperability by allowing tools deployed on heterogeneous systems to interface. A conceptual framework has been created, which is realised practically through the proposal of a common graph format to standardise network data, and the investigation of open-source deployment technologies. Workflows are a graph of web services, allowing analyses to be carried out as part of a bigger software pipeline. They may be constructed using web services within the toolkit together with those from other providers, and can be saved, shared and reused, allowing biologists to ...
One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be
TY - JOUR. T1 - Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments. AU - Cai, Hong. AU - Lilburn, Timothy G.. AU - Hong, Changjin. AU - Gu, Jianying. AU - Kuang, Rui. AU - Wang, Yufeng. PY - 2015/6/11. Y1 - 2015/6/11. N2 - Background: Malaria is a major health threat, affecting over 40% of the worlds population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis. Results: Our module-based subnetwork alignment approach identified 24 ...
If you have a question about this talk, please contact Xin Wang.. Differences in the gene regulatory network are hypothesized to contribute significantly to phenotypic divergence between and within species. Non-coding sequences with bursts of lineage-specific changes are promising candidates, because clusters of nearby substitutions are a hallmark of selection potentially modify evolutionarily conserved regulatory elements. Performing a comprehensive, genome-wide analysis, we find that genomic loci with high substitution rates in the human-chimp lineage are over-represented near genes that duplicated in the human-chimp ancestor. We also developed a method to screen for nucleotide substitutions predicted to affect transcription factor binding. Rates of binding site divergence are elevated in non-coding sequences near duplicated loci with accelerated substitution rates. Finally, GC-biased gene conversion (gBGC) is a non-adaptive, recombination-associated explanation for accelerated substitution ...
While accurate annotations of protein-coding regions in the human genome have been available for many years, annotation and interpretation of regulatory sequenc...
Brand: Blue sky components limitedPublisher: Blue sky components limitedDetails: Toggle Switches, Miniature - RS Series RS series of PCB mount miniature toggle switches with a low contact resistance and are available with either Gold (R) or Silver (Q) contact material. The RS series miniature toggle switches have a min
Microarray technology allows the simultaneous measurement of RNA levels in a cell culture or tissue sample. This has resulted in the generation of vast amounts of data, and the methods to analyze this onslaught are only just now catching up. In this thesis, we present three novel methods for the algorithmic and statistical analysis of microarray data.; First, in joint work with Michael R. Mehan, we systematically annotated over 300 microarray datasets with phenotype information, then used these annotations to find gene coexpression modules that are specific to particular phenotypes. Though previous methods existed to find recurrent coexpression modules, ours is the first to find phenotype-specific ones, avoiding the problem past methods faced of finding mostly general, cell-cycle related modules. Second, we studied the statistics of gene networks. Although the algorithms to study gene coexpression networks have become increasingly sophisticated, the statistics have failed to keep up. As a ...
Author(s): Bonham, Luke W; Steele, Natasha ZR; Karch, Celeste M; Manzoni, Claudia; Geier, Ethan G; Wen, Natalie; Ofori-Kuragu, Aaron; Momeni, Parastoo; Hardy, John; Miller, Zachary A; Hess, Christopher P; Lewis, Patrick; Miller, Bruce L; Seeley, William W; Baranzini, Sergio E; Desikan, Rahul S; Ferrari, Raffaele; Yokoyama, Jennifer S; International FTD-Genomics Consortium (IFGC) | Abstract: Objective:The neuroanatomical profile of behavioral variant frontotemporal dementia (bvFTD) suggests a common biological etiology of disease despite disparate pathologic causes; we investigated the genetic underpinnings of this selective regional vulnerability to identify new risk factors for bvFTD. Methods:We used recently developed analytical techniques designed to address the limitations of genome-wide association studies to generate a protein interaction network of 63 bvFTD risk genes. We characterized this network using gene expression data from healthy and diseased human brain tissue, evaluating regional
Pan-Cancer pattern of lncRNAs, miRNAs and their regulatory networks - posted in siRNA, microRNA and RNAi: Dysregulation of miRNAs and lncRNAs can contribute to tumor formation and progression. Little is known about the expression profiles of lncRNAs and miRNAs across diverse cancer types. Pan-Cancer miRNA and lncRNA resources: (1) panCancerMine: explore Pan-Cancer lncRNA, miRNA and their regulatory networks (ceRNA, coexpression) by mining expression profiles of miRNAs, lncRNAs...
Systems biology approaches can be used to study the regulatory interactions occurring between many components of the biological system at the whole-genome level and decipher the circuitries implicated in the regulation of cellular processes, including those imparting virulence to opportunistic fungi. Candida albicans (C. albicans) is a leading human fungal pathogen. It undergoes morphological switching between a budding yeast form and an elongated multicellular hyphal form. This transition is required for C. albicans ability to cause disease and is regulated through highly interconnected regulatory interactions between transcription factors (TFs) and target genes. The chromatin immunoprecipitation (ChIP)-High-throughput sequencing (Seq) technology (ChIP-Seq) is a powerful approach for decoding transcriptional regulatory networks. This protocol was optimized for the preparation of ChIP DNA from filamenting C. albicans cells followed by high-throughput sequencing to identify the targets of TFs that
March 2, 2010 - The underlying causes of the debilitating psychiatric disorder schizophrenia remain poorly understood. In a study published online in Genome Research (www.genome.org), scientists have performed a powerful gene network analysis that has revealed surprising new insights into how gene regulation and age play a role in schizophrenia.. Researchers are actively working to identify the direct cause of schizophrenia, likely rooted in interactions between genes and the environment resulting in abnormal gene expression in the central nervous system. Scientists have been studying expression changes in schizophrenia on an individual gene basis, yet this strategy has explained only a portion of the genetic risk.. In this work, a team of researchers led by Elizabeth Thomas of the Scripps Research Institute has taken a novel approach to this problem, performing a gene network-based analysis that revealed surprising insight into schizophrenia development.. The group analyzed gene expression data ...
Ultraviolet light damages the DNA of cells, which prevents duplication and thereby cell division. Bacteria respond to such damage by producing a number of proteins that help to detect, bypass, and repair the damage. This SOS response system displays intricate dynamical behavior-in particular the tightly regulated turn-on and turn-off of error-prone DNA polymerases that result in mutagenesis-and the puzzling resurgence of SOS gene activity 30-40 min after irradiation. The SOS response in the bacterium E. coli encompasses many proteins involved in detecting and repairing DNA damaged by a variety of agents, such as UV radiation, or chemicals such as mitomycin and bleomycin. A complex regulatory network, comprising both transcriptional and post-translational regulators, controls the concentrations and levels of activity of these proteins. The collective actions of this regulatory network are orchestrated so that the SOS response is commensurate with the magnitude of DNA damage. The prokaryotic SOS ...
The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.