RNA-Seq Experiments

An RNA-seq experiment generates a lot of data with a lot of dimensions. This indicates that for a small number of samples, we get a large number of observations. For example, 6 samples may have the expression of 20,000 genes analyzed (3 knock-out and 3 wild-type). A common method for analyzing RNA-seq data is to fit each gene to a linear model, where parameters must be determined using a limited number of observations for each of the 20,000 genes. RNA-seq investigations produce vast, complicated data sets that necessitate rapid, accurate, and adaptable software to convert raw read data into understandable conclusions. Shotgun sequencing is used to measure gene expression in RNA-seq. The procedure entails converting RNA to cDNA and then sequencing fragments on a high-throughput platform like Illumina to generate a huge number of short reads. The readings are then aligned to a genome for each sample, and the number of reads linked to each gene or feature is counted. The goal of a typical RNA-seq experiment is to uncover genes that are differently expressed between two conditions (e.g. up and down-regulated genes in knock-out mice compared to wild-type mice). New transcripts, splice variants, and fusion genes can all be discovered using RNA-seq.