https://www.youtube.com/watch?v=xh_wpWj0AzM
If you benefit from my tutorial and use the same strategy for data analysis, please CITE my RNA-Seq paper published in "Scientific Reports - Nature": https:/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6096346/
A major goal of RNA-seq analysis is to identify differentially expressed and coregulated genes and to infer biological meaning for further studies. Source material can be cells cultured in vitro, whole-tissue homogenates, or sorted cells. The ability to interpret findings depends on appropriate experimental design, implementation of controls

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954399/
Differential gene expression ( DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190152
The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq data also increased rapidly.

https://hbctraining.github.io/Training-modules/planning_successful_rnaseq/lessons/sample_level_QC.html
The next step in the RNA-seq workflow is the differential expression analysis. The goal of differential expression testing is to determine which genes are expressed at different levels between conditions. These genes can offer biological insight into the processes affected by the condition (s) of interest. The steps outlined in the gray box

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2968-1
Despite the availability of many ready-made testing software, reliable detection of differentially expressed genes in RNA-seq data is not a trivial task. Even though the data collection is considered high-throughput, data analysis has intricacies that require careful human attention. Researchers should use modern data analysis techniques that incorporate visual feedback to verify the

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827276/
To identify differentially expressed genes between two conditions, it is important to find statistical distributional property of the data to approximate the nature of differential genes. In the present study, the focus is mainly to investigate the differential gene expression analysis for sequence data based on compound distribution model.

https://mkempenaar.github.io/gene_expression_analysis/chapter-4.html
4.3 Using Bioconductor Packages. This section demonstrates the use of two packages to perform DEG-analysis on count data. There are many packages available on Bioconductor for RNA-Seq analysis, such as DSS, EBSeq, NOISeq and BaySeq, but here we will focus on edgeR and DESeq2 for processing our count-based data. Chances are that one of these two packages are mentioned if the article described

https://www.nature.com/articles/s41467-020-17900-3
A common analysis of single-cell sequencing data includes clustering of cells and identifying differentially expressed genes (DEGs). How cell clusters are defined has important consequences for

https://pubmed.ncbi.nlm.nih.gov/30099484/
Differential gene expression (DGE) analysis is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes across two or more conditions and is widely used in many applications of RNA-seq data analysis. Interpretation of the DGE results can be nonintuitive and

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2486-6
RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. iDEP (integrated

https://www.nature.com/articles/s41598-020-76881-x
As the analysis of RNA-seq data is complex, it has prompted a large amount of research on algorithms and methods. ... methods for detecting differentially expressed genes from RNA-seq data. Am. J

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion

https://avikarn.com/2020-07-02-RNAseq_DeSeq2/
In this tutorial, negative binomial was used to perform differential gene expression analyis in R using DESeq2, pheatmap and tidyverse packages. The workflow for the RNA-Seq data is: Generate the count matrix of the aligned reads i.e. the number of reads aligning to the exons of each gene.

https://www.mathworks.com/help/bioinfo/ug/identifying-differentially-expressed-genes-from-rna-seq-data.html
You can now identify the most up-regulated or down-regulated genes by considering an absolute fold change above a chosen cutoff. For example, a cutoff of 1 in log2 scale yields the list of genes that are up-regulated with a 2 fold change. Get. % find up-regulated genes. up = diffTableLocalSig.Log2FoldChange > 1;

https://www.10xgenomics.com/analysis-guides/differential-gene-expression-analysis-in-scrna-seq-data-between-conditions-with-biological-replicates
A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Brief Bioinformatics 2022: distinct: Tests for differences in the distribution of gene expression between conditions. First the empirical distribution per sample for a given gene in each cell type is computed.

https://pubmed.ncbi.nlm.nih.gov/22130886/
In this chapter, we provide a tutorial on RNA-Seq data analysis to enable researchers to quantify gene expression, identify splice junctions, and find novel transcripts using publicly available software. We focus on the analyses performed in organisms where a reference genome is available and discuss issues with current methodology that have to

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-91
Background Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As

https://cellandbioscience.biomedcentral.com/articles/10.1186/2045-3701-2-26
RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most common questions in a typical gene profiling experiment is how to identify a set of transcripts that are differentially expressed between different experimental conditions. Some of the statistical methods developed for microarray data analysis can

https://link.springer.com/chapter/10.1007/978-3-319-07212-8_2
Abstract. RNA-sequencing (RNA-seq) technology has become a major choice in detecting differentially expressed genes across different biological conditions. Although microarray technology is used for the same purpose, statistical methods available for identifying differential expression for microarray data are generally not readily applicable to

https://link.springer.com/protocol/10.1007/978-1-0716-3918-4_18
RNA sequencing, often abbreviated as RNA-Seq, represents a crucial pillar in modern genomics research, employing high-throughput sequencing technologies to illuminate the dynamic nature of the transcriptome [].It offers a precise, quantitative, and high-resolution perspective of the transcriptional landscape, enabling unprecedented exploration of the functional elements of the genome, the

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5739479/
Introduction. High-throughput sequencing has become the main choice to measure expression levels, i.e., RNA-Seq [].RNA-Seq can be performed without prior knowledge of the reference or sequence of interest and allows a wide variety of applications such as: 'de novo' reconstruction of the transcriptome (without a reference genome), evaluation of nucleotide variations, evaluation of

https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-9-r95
A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has

https://link.springer.com/protocol/10.1007/978-1-61779-400-1_17
Abstract. RNA-Seq is arising as a powerful method for transcriptome analyses that will eventually make microarrays obsolete for gene expression analyses. Improvements in high-throughput sequencing and efficient sample barcoding are now enabling tens of samples to be run in a cost-effective manner, competing with microarrays in price, excelling

https://pubmed.ncbi.nlm.nih.gov/29267363/
Abstract. The correct identification of differentially expressed genes (DEGs) between specific conditions is a key in the understanding phenotypic variation. High-throughput transcriptome sequencing (RNA-Seq) has become the main option for these studies. Thus, the number of methods and softwares for differential expression analysis from RNA-Seq

https://pubmed.ncbi.nlm.nih.gov/22849430/
This review attempts to give an in-depth review of these statistical methods, with the goal of providing a comprehensive guide when choosing appropriate metrics for RNA-Seq statistical analyses. RNA sequencing (RNA-Seq) is rapidly replacing microarrays for profiling gene expression with much improved accuracy and sensitivity. One of the most

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-024-10414-y
Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science

https://www.nature.com/articles/s41580-024-00748-6
The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225332/
To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation

https://www.biorxiv.org/content/10.1101/2024.06.28.601227v1
From the pathogen side, this transcriptomic analysis revealed the upregulation of virulence factors, metabolism and sporulation genes, as well as the identification of 61 ncRNAs differentially expressed during infection that correlated with the analysis of available raw RNA-seq datasets from two independent studies.