scholarly journals Genome-Scale CRISPR Screening Identifies Novel Human Pluripotent Gene Networks

2018 ◽  
Author(s):  
Robert J. Ihry ◽  
Max Salick ◽  
Daniel J. Ho ◽  
Marie Sondey ◽  
Sravya Kommineni ◽  
...  
Keyword(s):  
Gene Networks ◽  
Genome Scale

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.

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 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.

scholarly journals DeGNServer: Deciphering Genome-Scale Gene Networks through High Performance Reverse Engineering Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Li ◽  
Hairong Wei ◽  
Patrick Xuechun Zhao
Keyword(s):  
Reverse Engineering ◽  
Sample Size ◽  
Gene Networks ◽  
High Performance ◽  
Large Scale ◽  
Small Sample ◽  
Biological Processes ◽  
Expression Data ◽  
Data Set ◽  
Genome Scale

Analysis of genome-scale gene networks (GNs) using large-scale gene expression data provides unprecedented opportunities to uncover gene interactions and regulatory networks involved in various biological processes and developmental programs, leading to accelerated discovery of novel knowledge of various biological processes, pathways and systems. The widely used context likelihood of relatedness (CLR) method based on the mutual information (MI) for scoring the similarity of gene pairs is one of the accurate methods currently available for inferring GNs. However, the MI-based reverse engineering method can achieve satisfactory performance only when sample size exceeds one hundred. This in turn limits their applications for GN construction from expression data set with small sample size. We developed a high performance web server, DeGNServer, to reverse engineering and decipher genome-scale networks. It extended the CLR method by integration of different correlation methods that are suitable for analyzing data sets ranging from moderate to large scale such as expression profiles with tens to hundreds of microarray hybridizations, and implemented all analysis algorithms using parallel computing techniques to infer gene-gene association at extraordinary speed. In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery. DeGNServer is publicly and freely available online.

scholarly journals Genome-Scale CRISPR Screening Identifies Novel Human Pluripotent Gene Networks

2018 ◽  
Author(s):  
Robert J. Ihry ◽  
Max R. Salick ◽  
Daniel J. Ho ◽  
Marie Sondey ◽  
Sravya Kommineni ◽  
...  
Keyword(s):  
Gene Networks ◽  
Loss Of Function ◽  
Preclinical Research ◽  
Novel Genes ◽  
Cell Library ◽  
Fundamental Properties ◽  
Cell Clones ◽  
Genome Wide ◽  
Genome Scale ◽  
At Will

ABSTRACTHuman pluripotent stem cells (hPSCs) generate a wide variety of disease-relevant cells that can be used to improve the translation of preclinical research. Despite the potential of hPSCs, their use for genetic screening has been limited because of technical challenges. We developed a renewable Cas9/sgRNA-hPSC library where loss-of-function mutations can be induced at will. Our inducible-mutant hPSC library can be used for an unlimited number of genome-wide screens. We screened for novel genes involved in 3 of the fundamental properties of hPSCs: Their ability to self-renew/survive, their capacity to differentiate into somatic cells, and their inability to survive as single-cell clones. We identified a plethora of novel genes with unidentified roles in hPSCs. These results are available as a resource for the community to increase the understanding of both human development and genetics. In the future, our stem cell library approach will be a powerful tool to identify disease-modifying genes.VISUAL ABSTRACT

scholarly journals Reverse engineering and analysis of large genome-scale gene networks

2012 ◽  
Vol 41 (1) ◽  
pp. e24-e24 ◽  
Author(s):  
Maneesha Aluru ◽  
Jaroslaw Zola ◽  
Dan Nettleton ◽  
Srinivas Aluru
Keyword(s):  
Reverse Engineering ◽  
Gene Networks ◽  
Large Genome ◽  
Genome Scale
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