1. RNA-Seq Workflow
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.
2. RNA-Seq Workflow: RNA-Seq Experimental Design
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.
3. RNA-Seq Workflow: Sample prep
The first step in a successful RNA-Seq DE analysis is a well-planned experiment.
A well-planned experiment should avoid batch effects, should divide known major sources of variation, such as different sexes or ages, equally between sample groups, and should have a good number of biological replicates, preferably more than 3.
The more biological replicates we have, the better our ability to detect DE genes with better estimates of mean expression and variation. Also, if we need to remove an outlier sample, we will still have biological replicates for the analysis.
4. RNA-Seq Workflow: Sample prep
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-Seq libraries to be sequenced.
The sequencing generates millions of nucleotide sequences called reads. The reads correspond to ends of the fragments sequenced. The sequence of each read is output into FASTQ files.
5. RNA-Seq Workflow: Quality control
With the sequenced reads in the FASTQ files, a series of analytical steps is performed on the command line, beginning with the assessment of the raw data quality.
6. RNA-Seq Workflow: Quality control
At this step, we ensure something didn't go wrong at the sequencing facility and explore the data for contamination, such as vector, adapter, or ribosomal.
7. RNA-Seq Workflow: Alignment
The next step is alignment or mapping of the reads to the genome to determine the location on the genome where the reads originated.
8. RNA-Seq Workflow: Alignment
Since mRNA contains only the exons needed to create the proteins, when the mRNA is aligned to the genome containing introns, some of the reads will be split across introns. Therefore, tools for aligning reads to the genome need to align across introns for RNA-seq.
The output of alignment gives the genome coordinates for where the read most likely originated from in the genome and information about the quality of the mapping.
9. RNA-Seq Workflow: Quantitation
Following alignment, the reads aligning to the exons of each gene are quantified to yield a matrix of gene counts.
10. RNA-Seq Workflow: Count matrix
We can read into R the count matrix using the `read.csv()` function and specifying the file.
The gene count matrix is arranged with the samples as columns and gene IDs as rows. The count values represent the number of reads or fragments aligning to the exons of each gene.
11. RNA-Seq Workflow: Differential expression
Once we have count data, differential expression analysis is performed. With differential expression analysis, our goal is to determine whether the gene counts between the sample groups are significantly different given the variation in the counts within the sample group.
12. RNA-Seq Workflow: DE results
The output of the statistical analysis includes the log2 fold changes of expression between conditions and the adjusted p-values for each gene. Genes that reach a threshold for significance can be subset to define a list of significant differentially expressed genes.
13. RNA-Seq Workflow
Now that we have a general understanding of the workflow and have the counts file loaded, we can get started.
14. Back to you!
Now let's explore the workflow.