How do mRNASeq, BulkRNASeq, and TotalRNASeq differ in terms of workflow?

Question

[manish.nu]

I want to look for differential gene expression a dataset from a collaborating lab. I have successfully done end-to-end analysis for mRNASeq, CHIPSeq and ATACSeq but I'm very confused about analyzing BulkRNASeq and TotalRNASeq. How different is the pipeline for BulkRNASeq and TotalRNASeq from mRNASeq? For example, for my mRNASeq, I used STAR for alignment, hisat2 for counts followed by DESEQ2 for differential gene expression and RMATS for splicing. Is this the same workflow for BulkRNASeq? I would appreciate any feedback.

[GenoMax]

Bulk RNAseq is an umbrella term for protocols where some sort of single cell (either truly single cell captured per well or 10x type tech) level data is not involved. So mRNAseq and TotalRNAseq are essentially bulkRNAseq methods. With mRNAseq you are making an effort to capture/enrich mRNA. TotalRNAseq is going to contain all types of RNA's including rRNA's which can be up to 90+% of all cellular RNAs.

AI bots can be very useful for getting concise information about questions like this. Give it a try with ChatGPT, Bard or whichever AI engine you like.

[Ram]

tinybio is a bioinformatics specific GPT bot - might be better here. It does do some weird stuff like assuming context that you don't give it (weird stuff bolded in the content below). Here's a screenshot of the conversation:

Here's the content:

How do mRNASeq, BulkRNASeq, and TotalRNASeq differ in terms of workflow?

The workflows for mRNASeq, BulkRNASeq, and TotalRNASeq differ in terms of the type of RNA they target and the subsequent analysis steps.

mRNASeq: This workflow targets only messenger RNAs (mRNAs), which are the RNAs that get translated into proteins. The workflow typically involves the following steps: RNA extraction, mRNA enrichment (to remove other types of RNAs), library preparation, sequencing, and data analysis. The data analysis usually includes quality control, alignment to a reference genome, quantification of gene expression, and differential expression analysis.

BulkRNASeq: This workflow targets all types of RNAs in a sample, without any enrichment. It is also known as total RNA sequencing. The workflow is similar to mRNASeq, but without the mRNA enrichment step. The data analysis can be more complex because it needs to account for different types of RNAs (mRNAs, tRNAs, rRNAs, etc.). In addition to gene expression analysis, it can also provide information about other RNA features, such as alternative splicing, RNA editing, and non-coding RNAs.

TotalRNASeq: This is essentially the same as BulkRNASeq, targeting all types of RNAs without any enrichment. However, some people use the term TotalRNASeq to refer specifically to workflows that include a step to deplete ribosomal RNAs (rRNAs), which are the most abundant RNAs in a cell but are usually not of interest in RNASeq studies. The data analysis is similar to BulkRNASeq.

The specific tools and parameters used in the data analysis can vary depending on the specific goals of the study and the characteristics of the data. For example, the bcbio pipeline you mentioned uses tools like STAR or hisat2 for alignment, salmon or kallisto for quantification, and arriba or pizzly for fusion detection. Other pipelines might use different tools or additional analysis steps.

[Ram]

ChatGPT does a much better job, which is kind of expected.

mRNA sequencing (mRNA-Seq), Bulk RNA sequencing (Bulk RNA-Seq), and Total RNA sequencing (Total RNA-Seq) are three different techniques used in transcriptomics to study gene expression. They differ in terms of their workflow and the information they provide:

1. mRNA Sequencing (mRNA-Seq):

• Target: Focuses specifically on the sequencing of mRNA molecules.
• Workflow: a. Isolates and enriches for mRNA molecules using techniques like poly(A) tail selection or rRNA depletion.
  b. Converts mRNA into complementary DNA (cDNA) through reverse transcription.
  c. Amplifies and sequences the cDNA.
• Purpose: Provides information about the expression levels of genes at the mRNA level, allowing for the quantification of individual gene expression.

2. Bulk RNA Sequencing (Bulk RNA-Seq):

• Target: Analyzes the transcriptome of a population of cells or tissue in bulk.
• Workflow: a. Isolates total RNA, which includes mRNA, rRNA, tRNA, and other non-coding RNAs.
  b. Optionally removes ribosomal RNA (rRNA) to enrich for mRNA (if mRNA-Seq is desired).
  c. Converts RNA into cDNA through reverse transcription.
  d. Amplifies and sequences the cDNA.
• Purpose: Provides a snapshot of gene expression in a mixed population of cells, helping to identify changes in gene expression patterns between conditions or cell types.

3. Total RNA Sequencing (Total RNA-Seq):

• Target: Analyzes all RNA molecules present in a sample, including mRNA, non-coding RNA (e.g., microRNA, long non-coding RNA), and ribosomal RNA (rRNA).
• Workflow: a. Isolates and sequences all RNA molecules present in the sample.
• Purpose: Offers a comprehensive view of the transcriptome, including not only gene expression but also the presence and expression levels of non-coding RNAs and the overall RNA composition within a sample.

In summary, the key differences lie in the target of sequencing and the workflow. mRNA-Seq specifically targets and sequences mRNA molecules, providing detailed gene expression information. Bulk RNA-Seq starts with total RNA, which includes all RNA types, and can be used to assess gene expression at the population level. Total RNA-Seq, on the other hand, provides a comprehensive view of all RNA species in a sample, allowing for the study of both coding and non-coding RNAs. The choice of which method to use depends on the research question and the level of detail required in the analysis of gene expression.