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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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Related Experiment Video

Updated: Dec 22, 2025

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data

Published on: December 1, 2023

Negative binomial additive model for RNA-Seq data analysis.

Xu Ren¹, Pei-Fen Kuan²

¹Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, 11794, NY, USA.

BMC Bioinformatics

|May 3, 2020

Summary

This summary is machine-generated.

NBAMSeq improves genomic biomarker detection by modeling nonlinear covariate effects in gene expression data. This flexible statistical model enhances RNA-Seq analysis, outperforming existing methods for complex biological phenotypes.

Keywords:

Bayesian shrinkage Differential expression analysis Generalized additive model RNA-Seq Spline model

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Last Updated: Dec 22, 2025

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Area of Science:

Bioinformatics
Statistical Genomics
Computational Biology

Background:

Differential expression analysis is key for identifying genomic biomarkers using high-throughput sequencing.
Current models often assume linear relationships between gene counts and covariates, limiting their application for certain phenotypes.

Purpose of the Study:

To introduce NBAMSeq, a novel statistical model for differential expression analysis.
To address the limitations of existing models by incorporating nonlinear effects of covariates.

Main Methods:

NBAMSeq utilizes a generalized additive model framework.
It models the logarithm of mean gene counts using smooth functions, estimating parameters iteratively.
Variance estimation employs a Bayesian shrinkage approach for information sharing across genes.

Main Results:

NBAMSeq demonstrates superior performance in detecting nonlinear effects in gene expression data.
It maintains comparable performance to existing methods for linear effects.
Simulations and RNA-Seq case studies validate its effectiveness.

Conclusions:

NBAMSeq offers a more flexible and powerful approach for differential expression analysis.
It enhances the ability to identify genomic biomarkers, especially those influenced by nonlinear covariate effects.
The NBAMSeq package and source code are publicly available for use.