Editing Sequence analysis (section)

=== Gene expression analysis === 

Mapped RNA sequences are analyzed to estimate gene expression levels using quantification tools such as HTSeq,<ref>{{cite journal |last1=Anders |first1=Simon |last2=Pyl |first2=Paul Theodore |last3=Huber |first3=Wolfgang |title=HTSeq—a Python framework to work with high-throughput sequencing data |journal=Bioinformatics |date=January 2015 |volume=31 |issue=2 |pages=166–169 |doi=10.1093/bioinformatics/btu638 |pmid=25260700 |url=https://doi.org/10.1093/bioinformatics/btu638|pmc=4287950 }}</ref> and identify differentially expressed genes (DEGs) between experimental conditions using statistical methods like [[DESeq2]].<ref>{{cite journal |last1=Love |first1=M.I. |last2=Huber |first2=W. |last3=Anders |first3=S. |title=Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 |journal=Genome Biology |date=December 2014 |volume=15 |issue=12 |page=550 |doi=10.1186/s13059-014-0550-8 |doi-access=free |pmid=25516281 |pmc=4302049 }}</ref> This is carried out to compare the expression levels of genes or isoforms between or across different samples, and infer biological relevance.<ref name=sequence_analysis/>
The output of gene expression analysis is typically a table with values representing the expression levels of gene IDs or names in rows and samples in the columns as well as standard errors and p-values. The results in the table can be further visualized using volcano plots and heatmaps, where colors represent the estimated expression level. Packages like ggplot2 in R and Matplotlib in Python are often used to create the visuals. The table can also be annotated using a reference annotation file, usually in [[gene transfer format|GTF or GFF]] format to provide more context about the genes, such as the chromosome name, strand, and start and positions, and aid result interpretation.<ref name=sequence_analysis/><ref name=galaxy1/><ref name=galaxy2/><ref name=galaxy3>{{cite web |last1=Batut |first1=Bérénice |last2=Freeberg |first2=Mallory |last3=Heydarian |first3=Mo |display-authors=2 |title=Reference-based RNA-Seq data analysis (Galaxy Training Materials) |url=https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/ref-based/tutorial.html |website=Galaxy Training! |date=17 March 2024 |access-date=26 April 2024}}</ref>