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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.

Tyler Grimes¹, Somnath Datta¹

¹Univeristy of Florida, Department of Biostatistics.

Journal of Statistical Software

|July 29, 2021

Summary

SeqNet is a new R package for simulating RNA-seq data to benchmark gene regulatory network inference methods. It generates diverse gene networks and realistic expression data for robust method assessment.

Keywords:

Gaussian graphical model Gene regulatory networks co-expression methods differential network analysis

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Published on: December 7, 2021

Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Gene expression data are crucial for understanding gene regulatory networks.
Comparing gene network inference methods is challenging due to limited gold-standard datasets.
Existing data simulation tools do not accurately reflect RNA-seq experimental data.

Purpose of the Study:

To introduce SeqNet, an R package for simulating RNA-seq data.
To provide tools for generating diverse gene network structures.
To enable robust benchmarking of gene network inference algorithms.

Main Methods:

SeqNet generates various gene network topologies.
The package simulates RNA-seq count data from these networks.
It allows for the creation of in silico datasets for method evaluation.

Main Results:

SeqNet facilitates the generation of realistic, simulated RNA-seq data.
The package supports the creation of complex gene regulatory network structures.
In silico data from SeqNet can be used for benchmarking inference tools.

Conclusions:

SeqNet addresses the need for realistic simulated data in gene network inference research.
The R package provides a valuable resource for assessing the performance of bioinformatics tools.
SeqNet enhances the ability to compare and validate gene regulatory network inference methods.