scholarly journals An Approach to Inferring Transcriptional Regulation Among Genes From Large-Scale Expression Data

2003 ◽  
Vol 4 (1) ◽  
pp. 148-154 ◽  
Author(s):  
Javier Herrero ◽  
Ramón Díaz-Uriarte ◽  
Joaquín Dopazo
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Dna Microarrays ◽  
Genetic Network ◽  
Genetic Networks ◽  
Gene Interactions ◽  
Expression Data ◽  
Dynamic Correlations ◽  
Common Time ◽  
Data Points

The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.

scholarly journals Cancer Genetic Network Inference Using Gaussian Graphical Models

2019 ◽  
Vol 13 ◽  
pp. 117793221983940 ◽  
Author(s):  
Haitao Zhao ◽  
Zhong-Hui Duan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Human Cancer ◽  
Genetic Network ◽  
Genetic Networks ◽  
Gene Interactions ◽  
Expression Data ◽  
Rna Seq ◽  
Cancer Genetic ◽  
Graphical Lasso

The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.

scholarly journals Identification of Genetic Networks

Genetics ◽  
2004 ◽  
Vol 166 (2) ◽  
pp. 1037-1052
Author(s):  
Momiao Xiong ◽  
Jun Li ◽  
Xiangzhong Fang
Keyword(s):  
Differential Expression ◽  
Genetic Network ◽  
Structural Equations ◽  
Genetic Networks ◽  
Data Sets ◽  
Biological Processes ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Cell Phenotypes ◽  
Best Fit

Abstract In this report, we propose the use of structural equations as a tool for identifying and modeling genetic networks and genetic algorithms for searching the most likely genetic networks that best fit the data. After genetic networks are identified, it is fundamental to identify those networks influencing cell phenotypes. To accomplish this task we extend the concept of differential expression of the genes, widely used in gene expression data analysis, to genetic networks. We propose a definition for the differential expression of a genetic network and use the generalized T  2 statistic to measure the ability of genetic networks to distinguish different phenotypes. However, describing the differential expression of genetic networks is not enough for understanding biological systems because differences in the expression of genetic networks do not directly reflect regulatory strength between gene activities. Therefore, in this report we also introduce the concept of differentially regulated genetic networks, which has the potential to assess changes of gene regulation in response to perturbation in the environment and may provide new insights into the mechanism of diseases and biological processes. We propose five novel statistics to measure the differences in regulation of genetic networks. To illustrate the concepts and methods for reconstruction of genetic networks and identification of association of genetic networks with function, we applied the proposed models and algorithms to three data sets.

scholarly journals Computationally predicted gene regulatory networks in molluscan biomineralization identify extracellular matrix production and ion transportation pathways

2019 ◽  
Author(s):  
Victoria A Sleight ◽  
Philipp Antczak ◽  
Francesco Falciani ◽  
Melody S Clark
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Shell Growth ◽  
Extracellular Proteins ◽  
Supplementary Information ◽  
Gene Interactions ◽  
Expression Data ◽  
Gene Regulatory

Abstract Motivation The molecular processes regulating molluscan shell production remain relatively uncharacterized, despite the clear evolutionary and societal importance of biomineralization. Results Here we built the first computationally predicted gene regulatory network (GRN) for molluscan biomineralization using Antarctic clam (Laternula elliptica) mantle gene expression data produced over an age-categorized shell damage-repair time-course. We used previously published in vivo in situ hybridization expression data to ground truth gene interactions predicted by the GRN and show that candidate biomineralization genes from different shell layers, and hence microstructures, were connected in unique modules. We characterized two biomineralization modules of the GRN and hypothesize that one module is responsible for translating the extracellular proteins required for growing, repairing or remodelling the nacreous shell layer, whereas the second module orchestrates the transport of both ions and proteins to the shell secretion site, which are required during normal shell growth, and repair. Our findings demonstrate that unbiased computational methods are particularly valuable for studying fundamental biological processes and gene interactions in non-model species where rich sources of gene expression data exist, but annotation rates are poor and the ability to carry out true functional tests are still lacking. Availability and implementation The raw RNA-Seq data is freely available for download from NCBI SRA (Accession: PRJNA398984), the assembled and annotated transcriptome can be viewed and downloaded from molluscDB (ensembl.molluscdb.org) and in addition, the assembled transcripts, reconstructed GRN, modules and detailed annotations are all available as Supplementary Files. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Miika Ahdesmäki ◽  
Harri Lähdesmäki ◽  
Andrew Gracey ◽  
llya Shmulevich ◽  
Olli Yli-Harja
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Time Course ◽  
Robust Regression ◽  
Expression Data ◽  
Periodicity Detection

scholarly journals Transcription Profiling ofCandida albicansCells Undergoing the Yeast-to-Hyphal Transition

2002 ◽  
Vol 13 (10) ◽  
pp. 3452-3465 ◽  
Author(s):  
André Nantel ◽  
Daniel Dignard ◽  
Catherine Bachewich ◽  
Doreen Harcus ◽  
Anne Marcil ◽  
...  
Keyword(s):  
Time Course ◽  
Dna Microarrays ◽  
Culture Medium ◽  
Developmental Process ◽  
Pathogenic Fungus ◽  
Hyphal Growth ◽  
Transcription Profiles ◽  
Hyphal Morphology ◽  
External Signals ◽  
Temperature Time

The ability of the pathogenic fungus Candida albicans to switch from a yeast to a hyphal morphology in response to external signals is implicated in its pathogenicity. We used glass DNA microarrays to investigate the transcription profiles of 6333 predicted ORFs in cells undergoing this transition and their responses to changes in temperature and culture medium. We have identified several genes whose transcriptional profiles are similar to those of known virulence factors that are modulated by the switch to hyphal growth caused by addition of serum and a 37°C growth temperature. Time course analysis of this transition identified transcripts that are induced before germ tube initiation and shut off later in the developmental process. A strain deleted for the Efg1p and Cph1p transcription factors is defective in hyphae formation, and its response to serum and increased temperature is almost identical to the response of a wild-type strain grown at 37°C in the absence of serum. Thus Efg1p and Cph1p are needed for the activation of the transcriptional program that is induced by the presence of serum.

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