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Gene network inference

Héloïse Chassé, Sandrine Boulben, Vlad Costache, Patrick Cormier, Julia Morales
During the past decade, there has been growing interest in the role of translational regulation of gene expression in many organisms. Polysome profiling has been developed to infer the translational status of a specific mRNA species or to analyze the translatome, i.e. the subset of mRNAs actively translated in a cell. Polysome profiling is especially suitable for emergent model organisms for which genomic data are limited. In this paper, we describe an optimized protocol for the purification of sea urchin polysomes and highlight the critical steps involved in polysome purification...
October 7, 2016: Nucleic Acids Research
Meng-Hui Zhang, Qin-Hai Shen, Zhao-Min Qin, Qiao-Ling Wang, Xi Chen
The objective of the present study is to identify significant genes and pathways associated with hepatocellular carcinoma (HCC) by systematically tracking the dysregulated modules of re-weighted protein-protein interaction (PPI) networks. Firstly, normal and HCC PPI networks were inferred and re-weighted based on Pearson correlation coefficient. Next, modules in the PPI networks were explored by a clique-merging algorithm, and disrupted modules were identified utilizing a maximum weight bipartite matching in non-increasing order...
November 2016: Oncology Letters
Pablo Cordero, Joshua M Stuart
The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves...
2016: Pacific Symposium on Biocomputing
Gökmen Altay, Onur Mendi
The inference of gene regulatory networks is an important process that contributes to a better understanding of biological and biomedical problems. These networks aim to capture the causal molecular interactions of biological processes and provide valuable information about normal cell physiology. In this book chapter, we introduce GNI methods, namely C3NET, RN, ARACNE, CLR, and MRNET and describe their components and working mechanisms. We present a comparison of the performance of these algorithms using the results of our previously published studies...
2017: Methods in Molecular Biology
Junha Shin, Insuk Lee
Functional constraints between genes display similar patterns of gain or loss during speciation. Similar phylogenetic profiles, therefore, can be an indication of a functional association between genes. The phylogenetic profiling method has been applied successfully to the reconstruction of gene pathways and the inference of unknown gene functions. This method requires only sequence data to generate phylogenetic profiles. This method therefore has the potential to take advantage of the recent explosion in available sequence data to reveal a significant number of functional associations between genes...
2017: Methods in Molecular Biology
Oleg Simakov, Takeshi Kawashima
Metazoan evolution encompasses a vast evolutionary time scale spanning over 600 million years. Our ability to infer ancestral metazoan characters, both morphological and functional, is limited by our understanding of the nature and evolutionary dynamics of the underlying regulatory networks. Increasing coverage of metazoan genomes enables us to identify the evolutionary changes of the relevant genomic characters such as the loss or gain of coding sequences, gene duplications, micro- and macro-synteny, and non-coding element evolution in different lineages...
November 24, 2016: Developmental Biology
Hyunghoon Cho, Bonnie Berger, Jian Peng
The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node...
November 22, 2016: Cell Systems
Sahand Hormoz, Zakary S Singer, James M Linton, Yaron E Antebi, Boris I Shraiman, Michael B Elowitz
As they proliferate, living cells undergo transitions between specific molecularly and developmentally distinct states. Despite the functional centrality of these transitions in multicellular organisms, it has remained challenging to determine which transitions occur and at what rates without perturbations and cell engineering. Here, we introduce kin correlation analysis (KCA) and show that quantitative cell-state transition dynamics can be inferred, without direct observation, from the clustering of cell states on pedigrees (lineage trees)...
November 23, 2016: Cell Systems
Youngjune Park, Sangsoo Lim, Jin-Wu Nam, Sun Kim
Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD)...
November 24, 2016: Scientific Reports
Ming Shi, Weiming Shen, Hong-Qiang Wang, Yanwen Chong
Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l1-norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual...
December 2016: IET Systems Biology
Ivan Kondofersky, Fabian J Theis, Christiane Fuchs
In systems biology, one is often interested in the communication patterns between several species, such as genes, enzymes or proteins. These patterns become more recognisable when temporal experiments are performed. This temporal communication can be structured by reaction networks such as gene regulatory networks or signalling pathways. Mathematical modelling of data arising from such networks can reveal important details, thus helping to understand the studied system. In many cases, however, corresponding models still deviate from the observed data...
December 2016: IET Systems Biology
Jessica J Wadley, Damien A Fordham, Vicki A Thomson, Euan G Ritchie, Jeremy J Austin
The distribution of antilopine wallaroo, Macropus antilopinus, is marked by a break in the species' range between Queensland and the Northern Territory, coinciding with the Carpentarian barrier. Previous work on M. antilopinus revealed limited genetic differentiation between the Northern Territory and Queensland M. antilopinus populations across this barrier. The study also identified a number of divergent lineages in the Northern Territory, but was unable to elucidate any geographic structure. Here, we re-examine these results to (1) determine phylogeographic patterns across the range of M...
November 2016: Ecology and Evolution
Luis F Iglesias-Martinez, Walter Kolch, Tapesh Santra
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model-based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently...
November 23, 2016: Scientific Reports
Petra Stamm, Alexander T Topham, Nur K Mukhtar, Matthew Jackson, Daniel Tome, Jim L Beynon, George W Bassel
The seed to seedling transition is driven exclusively by cell shape changes within the axis of the plant embryo. Using digital single cell analysis of 3D cellular growth, here we identify the presence of two spatially and temporally distinct cell expansion modules within subdomains of the growing embryo. The second growth module is within the hypocotyl and drives the final phase of the completion of germination under optimal conditions. Using network inference, we identified the transcription factor ATHB5 as a genetic factor specifically induced and localized to the hypocotyl prior to its expansion...
November 21, 2016: Plant Physiology
Kyrylo Bessonov, Kristel Van Steen
Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIFcond ) may lead to improved performance compared to Breiman's implementation of Random Forests (RF)...
December 2016: Genetic Epidemiology
Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Exploring how the structure of a gene regulatory network differs between two different disease states is fundamental for understanding the biological mechanisms behind disease development and progression. Recently, with rapid advances in microarray technologies, gene expression profiles of the same patients can be collected from multiple microarray platforms. However, previous differential network analysis methods were usually developed based on a single type of platform, which could not utilize the common information shared across different platforms...
November 21, 2016: Molecular BioSystems
Gina M Calabrese, Larry D Mesner, Joseph P Stains, Steven M Tommasini, Mark C Horowitz, Clifford J Rosen, Charles R Farber
Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. Genome-wide association studies (GWAS) for BMD have identified dozens of associations; yet, the genes responsible for most associations remain elusive. Here, we used a bone co-expression network to predict causal genes at BMD GWAS loci based on the premise that genes underlying a disease are often functionally related and functionally related genes are often co-expressed. By mapping genes implicated by BMD GWAS onto a bone co-expression network, we predicted and inferred the function of causal genes for 30 of 64 GWAS loci...
November 15, 2016: Cell Systems
Wei Zhou, Yan Zhang, Yue-Hua Li, Shuang Wang, Jing-Jing Zhang, Cui-Xia Zhang, Zhi-Sheng Zhang
OBJECTIVE: This work aimed to identify dysregulated pathways for Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network (PIN). METHODS: The inference of dysregulated pathways was comprised of four steps: preparing gene expression data, protein-protein interaction (PPI) data and pathway data; constructing a PIN dependent on the data and Pearson correlation coefficient (PCC); selecting seed pathway from PIN by computing activity score for each pathway according to principal component analysis (PCA) method; and investigating dysregulated pathways in a minimum set of pathways (MSP) utilizing seed pathway and the area under the receiver operating characteristics curve (AUC) index implemented in support vector machines (SVM) model...
November 13, 2016: Computational Biology and Chemistry
Karoline Faust, Jeroen Raes
Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples...
2016: F1000Research
Casey P Shannon, Virginia Chen, Mandeep Takhar, Zsuzsanna Hollander, Robert Balshaw, Bruce M McManus, Scott J Tebbutt, Don D Sin, Raymond T Ng
BACKGROUND: Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues...
November 14, 2016: BMC Bioinformatics
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