R Tutorial : RNA-Seq Workflow

Want to learn more? Take the full course at https://learn.datacamp.com/courses/rna-seq-with-bioconductor-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now that you know a bit about the types of questions that RNA-Seq experiments can address, and how we use this technique to understand more about the genes important to a particular disease or condition, let's explore the steps required for the analysis workflow. Prior to starting the RNA-Seq workflow, planning is essential. This step in the analysis is crucial for good results, as there is often no saving a poorly designed experiment. There are a couple of important considerations during planning, including replicates, batch effects, and confounding: For RNA-Seq experiments there is generally low technical variation, so invest in biological replicates instead. The more biological replicates you have, the better the estimates are for mean expression and variation, leading to more robust analyses; be sure to have at least 3. Also, an experiment performed as different batches can confound your analysis. As much as possible try to perform experimental steps across all conditions at the same time, and if you cannot avoid batches, distribute the samples from each sample group into each batch. Finally, avoid confounding your experiment with major sources of variation. For example, if your animals are of different sexes, don't have all-male mice as control and all-female mice as treatment, as you won't be able to differentiate the treatment effect from the effect of sex. After you have a well-planned out the experiment, you can begin with sample preparation. When preparing RNA-Seq libraries, the samples are harvested, the RNA is isolated and DNA contamination is removed. The rRNA is removed or mature mRNAs are selected by their polyA tails. Then, the RNA is turned into cDNA, fragmented, size selected and adapters are added to generate the RNA-