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Gene network inference

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https://www.readbyqxmd.com/read/28096376/myosin-driven-transport-network-in-plants
#1
Elizabeth G Kurth, Valera V Peremyslov, Hannah L Turner, Kira S Makarova, Jaime Iranzo, Sergei L Mekhedov, Eugene V Koonin, Valerian V Dolja
We investigate the myosin XI-driven transport network in Arabidopsis using protein-protein interaction, subcellular localization, gene knockout, and bioinformatics analyses. The two major groups of nodes in this network are myosins XI and their membrane-anchored receptors (MyoB) that, together, drive endomembrane trafficking and cytoplasmic streaming in the plant cells. The network shows high node connectivity and is dominated by generalists, with a smaller fraction of more specialized myosins and receptors...
January 17, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28092574/dynamics-in-epistasis-analysis
#2
Aseel Awdeh, Hilary Phenix, Mads Kaern, Theodore Perkins
Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28087598/bayesian-network-inference-modeling-identifies-trib1-as-a-novel-regulator-of-cell-cycle-progression-and-survival-in-cancer-cells
#3
Rina Gendelman, Heming Xing, Olga K Mirzoeva, Preeti Sarde, Christina Curtis, Heidi Feiler, Paul McDonagh, Joe W Gray, Iya Khalil, W Michael Korn
Molecular networks governing cellular responses to targeted therapies are complex dynamic systems with non-intuitive behaviors. Here we applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell cycle arrest caused by inhibition of MEK1/2. Through simulation of reverse-engineered Bayesian network modeling, we generated predictions of G1-S transition...
January 13, 2017: Cancer Research
https://www.readbyqxmd.com/read/28081159/osmoregulation-in-the-halophilic-bacterium-halomonas-elongata-a-case-study-for-integrative-systems-biology
#4
Viktoria Kindzierski, Silvia Raschke, Nicole Knabe, Frank Siedler, Beatrix Scheffer, Katharina Pflüger-Grau, Friedhelm Pfeiffer, Dieter Oesterhelt, Alberto Marin-Sanguino, Hans-Jörg Kunte
Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem...
2017: PloS One
https://www.readbyqxmd.com/read/28077953/systematic-epistatic-mapping-of-cellular-processes
#5
EDITORIAL
Maximilian Billmann, Michael Boutros
Genetic screens have identified many novel components of various biological processes, such as components required for cell cycle and cell division. While forward genetic screens typically generate unstructured 'hit' lists, genetic interaction mapping approaches can identify functional relations in a systematic fashion. Here, we discuss a recent study by our group demonstrating a two-step approach to first screen for regulators of the mitotic cell cycle, and subsequently guide hypothesis generation by using genetic interaction analysis...
2017: Cell Division
https://www.readbyqxmd.com/read/28077403/gene-co-expression-analysis-for-functional-classification-and-gene-disease-predictions
#6
Sipko van Dam, Urmo Võsa, Adriaan van der Graaf, Lude Franke, João Pedro de Magalhães
Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes...
January 10, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/28076954/identification-of-genes-associated-with-breast-cancer-metastasis-to-bone-on-a-protein-protein-interaction-network-with-a-shortest-path-algorithm
#7
Yu-Dong Cai, Qing Zhang, Yu-Hang Zhang, Lei Chen, Tao Huang
Tumor metastasis is defined as the spread of tumor cells from one organ or part to another that is not directly connected to it, which significantly contributes to the progression and aggravation of tumorigenesis. Because it always involves multiple organs, the metastatic process is difficult to study in its entirety. Complete identification of the genes related to this process is an alternative way to study metastasis. In this study, we developed a computational method to identify such genes. To test our method, we selected breast cancer bone metastasis...
January 11, 2017: Journal of Proteome Research
https://www.readbyqxmd.com/read/28070596/chip-seq-data-analysis-to-define-transcriptional-regulatory-networks
#8
Giulio Pavesi
The first step in the definition of transcriptional regulatory networks is to establish correct relationships between transcription factors (TFs) and their target genes, together with the effect of their regulatory activity (activator or repressor). Fundamental advances in this direction have been made possible by the introduction of experimental techniques such as Chromatin Immunoprecipitation, which, coupled with next-generation sequencing technologies (ChIP-Seq), permit the genome-wide identification of TF binding sites...
January 10, 2017: Advances in Biochemical Engineering/biotechnology
https://www.readbyqxmd.com/read/28068916/inferring-condition-specific-targets-of-human-tf-tf-complexes-using-chip-seq-data
#9
Chia-Chun Yang, Min-Hsuan Chen, Sheng-Yi Lin, Erik H Andrews, Chao Cheng, Chun-Chi Liu, Jeremy J W Chen
BACKGROUND: Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes...
January 10, 2017: BMC Genomics
https://www.readbyqxmd.com/read/28062755/evolutionary-conservation-and-divergence-of-gene-coexpression-networks-in-gossypium-cotton-seeds
#10
Guanjing Hu, Ran Hovav, Corrinne E Grover, Adi Faigenboim-Doron, Noa Kadmon, Justin T Page, Joshua A Udall, Jonathan F Wendel
The cotton genus (Gossypium) provides a superior system for the study of diversification, genome evolution, polyploidization, and human-mediated selection. To gain insight into phenotypic diversification in cotton seeds, we conducted coexpression network analysis of developing seeds from diploid and allopolyploid cotton species and explored network properties. Key network modules and functional associations were identified related to seed oil content and seed weight. We compared species-specific networks to reveal topological changes, including rewired edges and differentially coexpressed genes, associated with speciation, polyploidy, and cotton domestication...
January 6, 2017: Genome Biology and Evolution
https://www.readbyqxmd.com/read/28061857/coordinated-regulation-of-acid-resistance-in-escherichia-coli
#11
Patricia Aquino, Brent Honda, Suma Jaini, Anna Lyubetskaya, Krutika Hosur, Joanna G Chiu, Iriny Ekladious, Dongjian Hu, Lin Jin, Marianna K Sayeg, Arion I Stettner, Julia Wang, Brandon G Wong, Winnie S Wong, Stephen L Alexander, Cong Ba, Seth I Bensussen, David B Bernstein, Dana Braff, Susie Cha, Daniel I Cheng, Jang Hwan Cho, Kenny Chou, James Chuang, Daniel E Gastler, Daniel J Grasso, John S Greifenberger, Chen Guo, Anna K Hawes, Divya V Israni, Saloni R Jain, Jessica Kim, Junyu Lei, Hao Li, David Li, Qian Li, Christopher P Mancuso, Ning Mao, Salwa F Masud, Cari L Meisel, Jing Mi, Christine S Nykyforchyn, Minhee Park, Hannah M Peterson, Alfred K Ramirez, Daniel S Reynolds, Nae Gyune Rim, Jared C Saffie, Hang Su, Wendell R Su, Yaqing Su, Meng Sun, Meghan M Thommes, Tao Tu, Nitinun Varongchayakul, Tyler E Wagner, Benjamin H Weinberg, Rouhui Yang, Anastasia Yaroslavsky, Christine Yoon, Yanyu Zhao, Alicia J Zollinger, Anne M Stringer, John W Foster, Joseph Wade, Sahadaven Raman, Natasha Broude, Wilson W Wong, James E Galagan
BACKGROUND: Enteric Escherichia coli survives the highly acidic environment of the stomach through multiple acid resistance (AR) mechanisms. The most effective system, AR2, decarboxylates externally-derived glutamate to remove cytoplasmic protons and excrete GABA. The first described system, AR1, does not require an external amino acid. Its mechanism has not been determined. The regulation of the multiple AR systems and their coordination with broader cellular metabolism has not been fully explored...
January 6, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/28053006/co-option-and-de-novo-gene-evolution-underlie-molluscan-shell-diversity
#12
Felipe Aguilera, Carmel McDougall, Bernard M Degnan
Molluscs fabricate shells of incredible diversity and complexity by localized secretions from the dorsal epithelium of the mantle. Although distantly-related molluscs express remarkably different secreted gene products, it remains unclear if the evolution of shell structure and pattern is underpinned by the differential co-option of conserved genes or the integration of lineage-specific genes into the mantle regulatory program. To address this, we compare the mantle transcriptomes of 11 bivalves and gastropods of varying relatedness...
January 4, 2017: Molecular Biology and Evolution
https://www.readbyqxmd.com/read/28051121/global-prioritizing-disease-candidate-lncrnas-via-a-multi-level-composite-network
#13
Qianlan Yao, Leilei Wu, Jia Li, Li Guang Yang, Yidi Sun, Zhen Li, Sheng He, Fangyoumin Feng, Hong Li, Yixue Li
LncRNAs play pivotal roles in many important biological processes, but research on the functions of lncRNAs in human disease is still in its infancy. Therefore, it is urgent to prioritize lncRNAs that are potentially associated with diseases. In this work, we developed a novel algorithm, LncPriCNet, that uses a multi-level composite network to prioritize candidate lncRNAs associated with diseases. By integrating genes, lncRNAs, phenotypes and their associations, LncPriCNet achieves an overall performance superior to that of previous methods, with high AUC values of up to 0...
January 4, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28032149/dtni-a-novel-toxicogenomics-data-analysis-tool-for-identifying-the-molecular-mechanisms-underlying-the-adverse-effects-of-toxic-compounds
#14
Diana M Hendrickx, Terezinha Souza, Danyel G J Jennen, Jos C S Kleinjans
Unravelling gene regulatory networks (GRNs) influenced by chemicals is a major challenge in systems toxicology. Because toxicant-induced GRNs evolve over time and dose, the analysis of global gene expression data measured at multiple time points and doses will provide insight in the adverse effects of compounds. Therefore, there is a need for mathematical methods for GRN identification from time-over-dose-dependent data. One of the current approaches for GRN inference is Time Series Network Identification (TSNI)...
December 28, 2016: Archives of Toxicology
https://www.readbyqxmd.com/read/28031031/gene-regulatory-network-inference-using-pls-based-methods
#15
Shun Guo, Qingshan Jiang, Lifei Chen, Donghui Guo
BACKGROUND: Inferring the topology of gene regulatory networks (GRNs) from microarray gene expression data has many potential applications, such as identifying candidate drug targets and providing valuable insights into the biological processes. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We introduce an ensemble gene regulatory network inference method PLSNET, which decomposes the GRN inference problem with p genes into p subproblems and solves each of the subproblems by using Partial least squares (PLS) based feature selection algorithm...
December 28, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28028461/an-integrative-systematic-revision-and-biogeography-of-rhynchocalamus-snakes-reptilia-colubridae-with-a-description-of-a-new-species-from-israel
#16
Karin Tamar, Jiří Šmíd, Bayram Göçmen, Shai Meiri, Salvador Carranza
BACKGROUND: The colubrid snakes of the genus Rhynchocalamus are seldom studied and knowledge of their ecology and life history is scarce. Three species of Rhynchocalamus are currently recognized, R. satunini (from Turkey eastwards to Iran), R. arabicus (Yemen and Oman), and R. melanocephalus (from the Sinai Peninsula northwards to Turkey). All are slender, secretive, mainly nocturnal and rare fossorial snakes. This comprehensive study is the first to sample all known Rhynchocalamus species in order to review the intra-generic phylogenetic relationships and historical biogeography of the genus...
2016: PeerJ
https://www.readbyqxmd.com/read/28018170/mrna-transcriptomics-of-galectins-unveils-heterogeneous-organization-in-mouse-and-human-brain
#17
Sebastian John, Rashmi Mishra
Background: Galectins, a family of non-classically secreted, β-galactoside binding proteins is involved in several brain disorders; however, no systematic knowledge on the normal neuroanatomical distribution and functions of galectins exits. Hence, the major purpose of this study was to understand spatial distribution and predict functions of galectins in brain and also compare the degree of conservation vs. divergence between mouse and human species. The latter objective was required to determine the relevance and appropriateness of studying galectins in mouse brain which may ultimately enable us to extrapolate the findings to human brain physiology and pathologies...
2016: Frontiers in Molecular Neuroscience
https://www.readbyqxmd.com/read/28011782/inference-of-cellular-level-signaling-networks-using-single-cell-gene-expression-data-in-c-elegans-reveals-mechanisms-of-cell-fate-specification
#18
Xiao-Tai Huang, Yuan Zhu, Leanne Lai Hang Chan, Zhongying Zhao, Hong Yan
MOTIVATION: Cell fate specification plays a key role to generate distinct cell types during metazoan development. However, most of the underlying signaling networks at cellular level are not well understood. Availability of time lapse single-cell gene expression data collected throughout C. elegans embryogenesis provides an excellent opportunity for investigating signaling networks underlying cell fate specification at systems, cellular and molecular levels. RESULTS: We propose a framework to infer signaling networks at cellular level by exploring the single-cell gene expression data...
December 23, 2016: Bioinformatics
https://www.readbyqxmd.com/read/28007948/genome-metabolite-associations-revealed-low-heritability-high-genetic-complexity-and-causal-relations-for-leaf-metabolites-in-winter-wheat-triticum-aestivum
#19
Andrea Matros, Guozheng Liu, Anja Hartmann, Yong Jiang, Yusheng Zhao, Huange Wang, Erhard Ebmeyer, Viktor Korzun, Ralf Schachschneider, Ebrahim Kazman, Johannes Schacht, Friedrich Longin, Jochen Christoph Reif, Hans-Peter Mock
We investigated associations between the metabolic phenotype, consisting of quantitative data of 76 metabolites from 135 contrasting winter wheat (Triticum aestivum) lines, and 17 372 single nucleotide polymorphism (SNP) markers. Metabolite profiles were generated from flag leaves of plants from three different environments, with average repeatabilities of 0.5-0.6. The average heritability of 0.25 was unaffected by the heading date. Correlations among metabolites reflected their functional grouping, highlighting the strict coordination of various routes of the citric acid cycle...
December 22, 2016: Journal of Experimental Botany
https://www.readbyqxmd.com/read/28005952/microrna-and-transcription-factor-gene-regulatory-network-analysis-reveals-key-regulatory-elements-associated-with-prostate-cancer-progression
#20
Mehdi Sadeghi, Bijan Ranjbar, Mohamad Reza Ganjalikhany, Faiz M Khan, Ulf Schmitz, Olaf Wolkenhauer, Shailendra K Gupta
Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks...
2016: PloS One
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