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DESeq2 employs statistical methods to normalize and analyze RNA-seq data, making it a valuable tool for researchers studying gene expression patterns and regulation. It is available through the Bioconductor repository. It was first presented in 2014. [1] As of September 2023, its use has been cited over 30,000 times. [2]
cqn [35] is a normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. EDASeq [36] is a Bioconductor package to perform GC-Content Normalization for RNA-Seq Data. GeneScissors A comprehensive approach to detecting and correcting spurious transcriptome inference due to RNAseq reads misalignment.
RNA Seq Experiment. The single-cell RNA-seq technique converts a population of RNAs to a library of cDNA fragments. These fragments are sequenced by high-throughput next generation sequencing techniques and the reads are mapped back to the reference genome, providing a count of the number of reads associated with each gene. [13]
RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, bulk RNA sequencing, [6] 3' mRNA-sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing. [7]
Dryad: repository of data underlying scientific publications in the basic and applied biosciences; Edinburgh Mouse Atlas; EPD Eukaryotic Promoter Database; FINDbase (the Frequency of INherited Disorders database) GigaDB: repository of large scale datasets underlying scientific publications in the biological and biomedical research
The plot visualizes the differences between measurements taken in two samples, by transforming the data onto M (log ratio) and A (mean average) scales, then plotting these values. Though originally applied in the context of two channel DNA microarray gene expression data, MA plots are also used to visualise high-throughput sequencing analysis ...