Anyone working with gene expression, transcriptional regulation or cDNA cloning will undoubtedly have used reverse-transcription PCR (RT-PCR) at some point in their workflow.

RT-PCR is usually carried out as a two-step process: first, total or mRNA-enriched RNA is converted to complementary DNA (cDNA) by a reverse transcriptase (RT) enzyme. The cDNA is then used as a template for PCR.

Modern genomics seems to be undergoing a shift from using short-read technologies to sequence large numbers of genomes with the aim of detecting SNPs, to sequencing fewer genomes using long-read technologies that can resolve more complex events, e.g., structural rearrangements, copy number variations, and repeat expansions.

Welcome back to our RNA-seq article series! Now that we’ve gone through the general workflow and the ins and outs of RNA quality, we will look at some of the popular methods and platforms that you are likely to encounter if you embark on RNA-seq experiments.

Welcome back to our RNA-seq series! In Part 2, we take a look at RNA quality considerations for RNA-seq.

As outlined in Part 1, library preparation is a pivotal part of the RNA-seq workflow. Although the exact protocol will vary according to RNA-seq platform technology (more about this in Part 3), the overall goal is the same: to create a library of complementary DNA fragments that represent the originating RNA sample as accurately as possible and provide information about the RNA features of interest, including information about antisense transcripts, alternative splice variants, miRNAs and other non-coding RNAs, low-abundance transcripts and more.

RNA sequencing or RNA-seq uses next-generation sequencing (NGS) technology to provide a snapshot of the numbers and identities of RNA molecules in any sample at any time under a condition(s) of interest. Since its inception in the early 00’s until now, data gleaned from RNA-seq experiments has revolutionised our understanding of RNA, its roles in animal and plant development, the importance of differential gene expression during health and disease, and how individuals respond to drug treatments. It has also revealed that eukaryotic transcriptomes are much more complex than previously thought!

Genomes, epigenomes, transcriptomes, proteomes, and metabolomes. The list goes on…

In this rapidly evolving ‘omics’ era, researchers are generally becoming less enthusiastic about analyzing a genome, a transcriptome, or a proteome, etc., in isolation. More and more, researchers want to study multiple ‘omes’ in parallel, and one of the most popular combinations nowadays is genome and transcriptome analysis from the same sample.