scholarly journals corto: a lightweight R package for Gene Network Inference and Master Regulator Analysis

2020 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M. Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference ◽  
Link Type

AbstractMotivationGene Network Inference and Master Regulator Analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses, but most require a computer cluster or large amounts of RAM to be executed.ResultsWe developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for Copy Number Variations (CNVs) and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets.AvailabilityCross-platform and multi-threaded R package on CRAN (stable version) https://cran.rproject.org/package=corto and Github (development release) https://github.com/federicogiorgi/[email protected]

scholarly journals corto: a lightweight R package for gene network inference and master regulator analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 3916-3917 ◽  
Author(s):  
Daniele Mercatelli ◽  
Gonzalo Lopez-Garcia ◽  
Federico M Giorgi
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Network Inference ◽  
Human Tumor ◽  
R Package ◽  
Supplementary Information ◽  
Specific Gene ◽  
Master Regulator ◽  
Gene Network Inference

Abstract Motivation Gene network inference and master regulator analysis (MRA) have been widely adopted to define specific transcriptional perturbations from gene expression signatures. Several tools exist to perform such analyses but most require a computer cluster or large amounts of RAM to be executed. Results We developed corto, a fast and lightweight R package to infer gene networks and perform MRA from gene expression data, with optional corrections for copy-number variations and able to run on signatures generated from RNA-Seq or ATAC-Seq data. We extensively benchmarked it to infer context-specific gene networks in 39 human tumor and 27 normal tissue datasets. Availability and implementation Cross-platform and multi-threaded R package on CRAN (stable version) https://cran.r-project.org/package=corto and Github (development release) https://github.com/federicogiorgi/corto. Supplementary information Supplementary data are available at Bioinformatics online.

Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications

2020 ◽  
Vol 15 (6) ◽  
pp. 629-655
Author(s):  
A.C. Iliopoulos ◽  
G. Beis ◽  
P. Apostolou ◽  
I. Papasotiriou
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Complex Networks ◽  
Gene Network ◽  
Network Inference ◽  
Mathematical Framework ◽  
Cancer Gene ◽  
Expression Data ◽  
Gene Network Inference ◽  
Basic Features

In this brief survey, various aspects of cancer complexity and how this complexity can be confronted using modern complex networks’ theory and gene expression datasets, are described. In particular, the causes and the basic features of cancer complexity, as well as the challenges it brought are underlined, while the importance of gene expression data in cancer research and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction to the corresponding theoretical and mathematical framework of graph theory and complex networks is provided. The basics of network reconstruction along with the limitations of gene network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades in complex networks, are described. Finally, an indicative and suggestive example of a cancer gene co-expression network inference and analysis is given.

IMPROVING GENE NETWORK INFERENCE BY COMPARING EXPRESSION TIME-SERIES ACROSS SPECIES, DEVELOPMENTAL STAGES OR TISSUES

2004 ◽  
Vol 02 (04) ◽  
pp. 765-783 ◽  
Author(s):  
GUILLAUME BOURQUE ◽  
DAVID SANKOFF
Keyword(s):  
Time Series ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Time Series Data ◽  
Series Data ◽  
Interaction Coefficients ◽  
Gene Network Inference ◽  
Linear Differential

We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.

scholarly journals DepEst: an R package of important dependency estimators for gene network inference algorithms

2017 ◽  
Author(s):  
Gökmen Altay ◽  
Zeyneb Kurt ◽  
Nejla Altay ◽  
Nizamettin Aydin
Keyword(s):  
Gene Network ◽  
Network Inference ◽  
R Package ◽  
Supplementary File ◽  
Main Process ◽  
Data Sets ◽  
Gene Network Inference ◽  
Inference Algorithms ◽  
Almost All ◽  
Access Link

AbstractGene network inference algorithms (GNI) are popular in bioinformatics area. In almost all GNI algorithms, the main process is to estimate the dependency (association) scores among the genes of the dataset.We present a bioinformatics tool, DepEst (Dependency Estimators), which is a powerful and flexible R package that includes 11 important dependency score estimators that can be used in almost all GNI Algorithms. DepEst is the first bioinformatics package that includes such a large number of estimators that runs both in parallel and serial.DepEst is currently available at https://github.com/altayg/Depest. Package access link, instructions, various workflows and example data sets are provided in the supplementary file.

scholarly journals SeaBase: A Multispecies Transcriptomic Resource and Platform for Gene Network Inference

2014 ◽  
Vol 54 (2) ◽  
pp. 250-263 ◽  
Author(s):  
A. H. L. Fischer ◽  
D. Mozzherin ◽  
A. M. Eren ◽  
K. D. Lans ◽  
N. Wilson ◽  
...  
Keyword(s):  
Gene Network ◽  
Network Inference ◽  
Gene Network Inference ◽  
Transcriptomic Resource

scholarly journals Limit cycle dynamics can guide the evolution of gene regulatory networks towards point attractors

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stuart P. Wilson ◽  
Sebastian S. James ◽  
Daniel J. Whiteley ◽  
Leah A. Krubitzer
Keyword(s):  
Gene Expression ◽  
Limit Cycles ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Specific Gene ◽  
Boolean Models ◽  
A Genome ◽  
Order Of Magnitude ◽  
Accelerate Evolution

AbstractDevelopmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.

scholarly journals Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures

2014 ◽  
Vol 10 ◽  
pp. EBO.S13481 ◽  
Author(s):  
Gökmen Altay ◽  
Zeyneb Kurt ◽  
Matthias Dehmer ◽  
Frank Emmert-Streib
Keyword(s):  
Gene Network ◽  
Network Inference ◽  
Gene Network Inference ◽  
Inference Algorithms

scholarly journals ALG-1 Influences Accurate mRNA Splicing Patterns in the Caenorhabditis elegans Intestine and Body Muscle Tissues by Modulating Splicing Factor Activities

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 931-951 ◽  
Author(s):  
Kasuen Kotagama ◽  
Anna L. Schorr ◽  
Hannah S. Steber ◽  
Marco Mangone
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Rna Splicing ◽  
Specific Gene ◽  
Splicing Factors ◽  
Tissue Specific ◽  
Link Type ◽  
Body Muscle ◽  
Mirna Targets ◽  
Mirna Pathway

MicroRNAs (miRNAs) are known to modulate gene expression, but their activity at the tissue-specific level remains largely uncharacterized. To study their contribution to tissue-specific gene expression, we developed novel tools to profile putative miRNA targets in the Caenorhabditis elegans intestine and body muscle. We validated many previously described interactions and identified ∼3500 novel targets. Many of the candidate miRNA targets curated are known to modulate the functions of their respective tissues. Within our data sets we observed a disparity in the use of miRNA-based gene regulation between the intestine and body muscle. The intestine contained significantly more putative miRNA targets than the body muscle highlighting its transcriptional complexity. We detected an unexpected enrichment of RNA-binding proteins targeted by miRNA in both tissues, with a notable abundance of RNA splicing factors. We developed in vivo genetic tools to validate and further study three RNA splicing factors identified as putative miRNA targets in our study (asd-2, hrp-2, and smu-2), and show that these factors indeed contain functional miRNA regulatory elements in their 3′UTRs that are able to repress their expression in the intestine. In addition, the alternative splicing pattern of their respective downstream targets (unc-60, unc-52, lin-10, and ret-1) is dysregulated when the miRNA pathway is disrupted. A reannotation of the transcriptome data in C. elegans strains that are deficient in the miRNA pathway from past studies supports and expands on our results. This study highlights an unexpected role for miRNAs in modulating tissue-specific gene isoforms, where post-transcriptional regulation of RNA splicing factors associates with tissue-specific alternative splicing.

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