Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution

AbstractThe biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER’s predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

Download Full-text

Towards inferring causal gene regulatory networks from single cell expression Measurements

10.1101/426981 ◽

2018 ◽

Cited By ~ 14

Author(s):

Xiaojie Qiu ◽

Arman Rahimzamani ◽

Li Wang ◽

Qi Mao ◽

Timothy Durham ◽

...

Keyword(s):

Single Cell ◽

Cell Fate ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Time Series Data ◽

Network Reconstruction ◽

Series Data ◽

Causal Gene ◽

Downstream Target ◽

Gene Regulatory

AbstractSingle-cell transcriptome sequencing now routinely samples thousands of cells, potentially providing enough data to reconstruct causal gene regulatory networks from observational data. Here, we present Scribe, a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs Restricted Directed Information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for "pseudotime” ordered single-cell data compared to true time series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as “RNA velocity” restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses therefore highlight an important shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and point the way towards overcoming it.

Download Full-text

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

10.1101/581678 ◽

2019 ◽

Cited By ~ 6

Author(s):

Christopher A Jackson ◽

Dayanne M Castro ◽

Giuseppe-Antonio Saldi ◽

Richard Bonneau ◽

David Gresham

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Gene Regulatory Network ◽

Gene Regulatory Networks ◽

Regulatory Network ◽

Regulatory Networks ◽

Network Reconstruction ◽

Single Cell Rna Sequencing ◽

Gene Regulatory

AbstractUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.

Download Full-text

A comparative analysis of synthetic genetic oscillators

Journal of The Royal Society Interface ◽

10.1098/rsif.2010.0183 ◽

2010 ◽

Vol 7 (52) ◽

pp. 1503-1524 ◽

Cited By ~ 135

Author(s):

Oliver Purcell ◽

Nigel J. Savery ◽

Claire S. Grierson ◽

Mario di Bernardo

Keyword(s):

Experimental Data ◽

Comparative Analysis ◽

Time Delay ◽

Synthetic Biology ◽

Gene Regulatory Networks ◽

Network Topology ◽

Regulatory Networks ◽

State Of The Art ◽

Gene Regulatory ◽

Future Work

Synthetic biology is a rapidly expanding discipline at the interface between engineering and biology. Much research in this area has focused on gene regulatory networks that function as biological switches and oscillators. Here we review the state of the art in the design and construction of oscillators, comparing the features of each of the main networks published to date, the models used for in silico design and validation and, where available, relevant experimental data. Trends are apparent in the ways that network topology constrains oscillator characteristics and dynamics. Also, noise and time delay within the network can both have constructive and destructive roles in generating oscillations, and stochastic coherence is commonplace. This review can be used to inform future work to design and implement new types of synthetic oscillators or to incorporate existing oscillators into new designs.

Download Full-text

Comparative analysis of salt responsive gene regulatory networks in rice and Arabidopsis

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2019.107188 ◽

2020 ◽

Vol 85 ◽

pp. 107188

Author(s):

Rui Wang ◽

Yanhao Cheng ◽

Xiaojuan Ke ◽

Xiaofan Zhang ◽

Hongsheng Zhang ◽

...

Keyword(s):

Comparative Analysis ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory ◽

Responsive Gene

Download Full-text

Effect of imputation on gene network reconstruction from single-cell RNA-seq data

10.1101/2021.04.13.439623 ◽

2021 ◽

Author(s):

Lam-Ha Ly ◽

Martin Vingron

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Network Reconstruction ◽

Data Sets ◽

Rna Seq ◽

Imputation Methods ◽

Before And After ◽

Gene Regulatory ◽

The Difference

AbstractDespite the advances in single-cell transcriptomics the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA-seq data make it hard to infer networks from transcriptome data. Data imputation can be applied in order to enhance gene-gene correlations and facilitate downstream data analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks.To study this question, we evaluate the effect of imputation methods on the performance and structure of the reconstructed networks in different experimental single-cell RNA-seq data sets. We use state-of-the-art algorithms for both imputation and network reconstruction and evaluate the difference in results before and after imputation. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. Yet, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms, while warning against the indiscriminate use of imputation before network reconstruction in general.

Download Full-text