scholarly journals GIANT 2.0: genome-scale integrated analysis of gene networks in tissues

2018 ◽  
Vol 46 (W1) ◽  
pp. W65-W70 ◽  
Author(s):  
Aaron K Wong ◽  
Arjun Krishnan ◽  
Olga G Troyanskaya
Keyword(s):  
Gene Networks ◽  
Integrated Analysis ◽  
Genome Scale

scholarly journals Protein Interaction Verification and Functional Annotation by Integrated Analysis of Genome-Scale Data

2002 ◽  
Vol 9 (5) ◽  
pp. 1133-1143 ◽  
Author(s):  
Patrick Kemmeren ◽  
Nynke L. van Berkum ◽  
Jaak Vilo ◽  
Theo Bijma ◽  
Rogier Donders ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Functional Annotation ◽  
Integrated Analysis ◽  
Genome Scale ◽  
Scale Data

scholarly journals WheatNet: A genome-scale functional network for hexaploid bread wheat, Triticum aestivum

2017 ◽  
Author(s):  
Tak Lee ◽  
Sohyun Hwang ◽  
Chan Yeong Kim ◽  
Hongseok Shim ◽  
Hyojin Kim ◽  
...  
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
Unique Feature ◽  
Single Gene ◽  
Functional Network ◽  
System Level ◽  
Edge Information ◽  
Integrated Network ◽  
A Genome ◽  
Genome Scale

Gene networks provide a system-level overview of genetic organizations and enable the dissection of functional modules underlying complex traits. Here we report the generation of WheatNet, the first genome-scale functional network for T. aestivum and a companion web server (www.inetbio.org/wheatnet). WheatNet was constructed by integrating 20 distinct genomics datasets, including 156,000 wheat-specific co-expression links mined from 1,929 microarray data. A unique feature of WheatNet is that each network node represents either a single gene or a group of genes. We computationally partitioned gene groups mimicking homeologous genes by clustering 99,386 wheat genes, resulting in 20,248 gene groups comprising 63,401 genes and 35,985 individual genes. Thus, WheatNet was constructed using 56,233 nodes, and the final integrated network has 20,230 nodes and 567,000 edges. The edge information of the integrated WheatNet and all 20 component networks are available for download.

Structural and functional analyses of microbial metabolic networks reveal novel insights into genome-scale metabolic fluxes

2018 ◽  
Vol 20 (4) ◽  
pp. 1590-1603 ◽  
Author(s):  
Gaoyang Li ◽  
Huansheng Cao ◽  
Ying Xu
Keyword(s):  
Metabolic Networks ◽  
Reaction Network ◽  
Clustering Coefficient ◽  
Integrated Analysis ◽  
Node Degree ◽  
Power Law Distribution ◽  
Metabolic Fluxes ◽  
Reaction Cycles ◽  
Functional Analyses ◽  
Genome Scale

Abstract We present here an integrated analysis of structures and functions of genome-scale metabolic networks of 17 microorganisms. Our structural analyses of these networks revealed that the node degree of each network, represented as a (simplified) reaction network, follows a power-law distribution, and the clustering coefficient of each network has a positive correlation with the corresponding node degree. Together, these properties imply that each network has exactly one large and densely connected subnetwork or core. Further analyses revealed that each network consists of three functionally distinct subnetworks: (i) a core, consisting of a large number of directed reaction cycles of enzymes for interconversions among intermediate metabolites; (ii) a catabolic module, with a largely layered structure consisting of mostly catabolic enzymes; (iii) an anabolic module with a similar structure consisting of virtually all anabolic genes; and (iv) the three subnetworks cover on average ∼56, ∼31 and ∼13% of a network’s nodes across the 17 networks, respectively. Functional analyses suggest: (1) cellular metabolic fluxes generally go from the catabolic module to the core for substantial interconversions, then the flux directions to anabolic module appear to be determined by input nutrient levels as well as a set of precursors needed for macromolecule syntheses; and (2) enzymes in each subnetwork have characteristic ranges of kinetic parameters, suggesting optimized metabolic and regulatory relationships among the three subnetworks.

scholarly journals Developmental chromatin restriction of pro-growth gene networks acts as an epigenetic barrier to axon regeneration in cortical neurons

2018 ◽  
Author(s):  
Ishwariya Venkatesh ◽  
Vatsal Mehra ◽  
Zimei Wang ◽  
Ben Califf ◽  
Murray G. Blackmore
Keyword(s):  
Cortical Neurons ◽  
Gene Networks ◽  
Target Genes ◽  
Axon Regeneration ◽  
Axon Growth ◽  
Chromatin Accessibility ◽  
Integrated Analysis ◽  
The Central Nervous System ◽  
Pioneer Factors ◽  
Growth Ability

ABSTRACTAxon regeneration in the central nervous system is prevented in part by a developmental decline in the intrinsic regenerative ability of maturing neurons. This loss of axon growth ability likely reflects widespread changes in gene expression, but the mechanisms that drive this shift remain unclear. Chromatin accessibility has emerged as a key regulatory mechanism in other cellular contexts, raising the possibility that chromatin structure may contribute to the age-dependent loss of regenerative potential. Here we establish an integrated bioinformatic pipeline that combines analysis of developmentally dynamic gene networks with transcription factor regulation and genome-wide maps of chromatin accessibility. When applied to the developing cortex, this pipeline detected overall closure of chromatin in sub-networks of genes associated with axon growth. We next analyzed mature CNS neurons that were supplied with various pro-regenerative transcription factors. Unlike prior results with SOX11 and KLF7, here we found that neither JUN nor an activated form of STAT3 promoted substantial corticospinal tract regeneration. Correspondingly, chromatin accessibility in JUN or STAT3 target genes was substantially lower than in predicted targets of SOX11 and KLF7. Finally, we used the pipeline to predict pioneer factors that could potentially relieve chromatin constraints at growth-associated loci. Overall this integrated analysis substantiates the hypothesis that dynamic chromatin accessibility contributes to the developmental decline in axon growth ability and influences the efficacy of pro-regenerative interventions in the adult, while also pointing toward selected pioneer factors as high-priority candidates for future combinatorial experiments.

scholarly journals Reprogramming cell fate with a genome-scale library of artificial transcription factors

2016 ◽  
Vol 113 (51) ◽  
pp. E8257-E8266 ◽  
Author(s):  
Asuka Eguchi ◽  
Matthew J. Wleklinski ◽  
Mackenzie C. Spurgat ◽  
Evan A. Heiderscheit ◽  
Anna S. Kropornicka ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Cell Fate ◽  
High Throughput Screening ◽  
Gene Networks ◽  
Transcriptional Profiling ◽  
Interaction Domain ◽  
Cell Fate Decisions ◽  
Artificial Transcription Factors ◽  
A Genome ◽  
Genome Scale

Artificial transcription factors (ATFs) are precision-tailored molecules designed to bind DNA and regulate transcription in a preprogrammed manner. Libraries of ATFs enable the high-throughput screening of gene networks that trigger cell fate decisions or phenotypic changes. We developed a genome-scale library of ATFs that display an engineered interaction domain (ID) to enable cooperative assembly and synergistic gene expression at targeted sites. We used this ATF library to screen for key regulators of the pluripotency network and discovered three combinations of ATFs capable of inducing pluripotency without exogenous expression ofOct4(POU domain, class 5, TF 1). Cognate site identification, global transcriptional profiling, and identification of ATF binding sites reveal that the ATFs do not directly targetOct4; instead, they target distinct nodes that converge to stimulate the endogenous pluripotency network. This forward genetic approach enables cell type conversions without a priori knowledge of potential key regulators and reveals unanticipated gene network dynamics that drive cell fate choices.

Genome-Wide Programming of Myeloid Cell Fates

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. SCI-29-SCI-29
Author(s):  
Berthold Göttgens
Keyword(s):  
Transcription Factor ◽  
Stem Cell ◽  
Regulatory Networks ◽  
Control Cell ◽  
Cell Development ◽  
Model Systems ◽  
Myeloid Differentiation ◽  
Integrated Analysis ◽  
Specific Gene ◽  
Genome Scale

Abstract Abstract SCI-29 Hematopoiesis represents one of the most tractable models of adult stem cell development and differentiation. Transcription factor (TF) proteins have long been recognized as major regulators of blood stem cell development as well as the subsequent differentiation into the multiple mature hematopoietic lineages. Seminal studies in multiple vertebrate model systems have identified specific TFs that control cell fate choices during myeloid differentiation (1). It remains largely unknown, however, how individual TFs are integrated into wider transcriptional regulatory networks, and how combinatorial TF interactions within these networks drive lineage specific gene expression programs. We are addressing these issues using two complementary approaches. First, we use a combination of transgenic reporter assays and network modeling approaches to reconstruct core transcription factor networks operating in early myeloid differentiation. Second, we employ genome-scale analysis of transcription factor binding sites for key hematopoietic regulators in both stem/progenitor cells and mature lineages (2,3). Integrated analysis of genome-scale datasets reveals previously unrecognized combinatorial interactions within core hematopoietic regulatory networks, which can be validated using both biochemical and mouse knockout approaches. Moreover, our studies also pinpoint novel candidate hematopoietic regulators, several of which we have validated using high throughput loss-of-function assays in zebrafish. Disclosures: No relevant conflicts of interest to declare.

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