Chapter 6 - RNA-seq Analysis

Authors: Nasheath Ahmed

Maintainers: Nasheath

Version: 0.1

License: CC-BY-NC-SA 4.0

Bulk RNA Sequence Analysis

What is RNA Sequencing?

Process of using high-throughput sequencing methods to quantify gene expession and provide insight into the transcriptome of cells and tissues. It allows us to gain information on which genes are active and their trancription levels. RNA sequencing can allow for the discovery of novel transcripts and their associated genes.

How are samples prepared and sequenced?

High quality RNA samples are essential to successful RNA-seq sample preparation. The simple pipeline includes: isolating the RNA from tissues of various samples, breaking the RNA into smaller fragments, converting the RNA fragments into complementary DNA, adding sequencing adaptors to the ends of the DNA, amplify the fragments using PCR. Extract mRNA from tissue of interest, then break it up by process called sonification. Breaks into fragments of about 200 basepairs.

Paired-End Sequencing vs Single-Read Sequencing

Paired-End sequencing is where both ends of the fragmented DNA can be sequenced. It produces twice the number of reads in the same amount of time. This method allows for more accurate read alignment and can be used to detect insertion-deletion(indel) variants (Nakazato et al., 2013). Single-read sequencing utilizes the fragmented DNA from only one end. It is good choice for methods such as small RNA-Seq.

The fragments of DNA are then sequenced using a high throughput sequencer such as Illumina. The output of the sequencing data is a data file called FASTQwhich stores the nucleotide sequences and the quality scores for the sequences.

Sequence Alignment and Aligners

Aligment is the process of correlating the fragments of DNA that were sequenced through the

Quality Control

Quality control is a critical step in ensuring the raw and generated reads from the samples collected are accurate in the transcript measurements and can be used in downstream analysis. The quality of RNA sequencing data can be divided into the RNA quality, the raw read data quality, alignment quality, and expression data quality.

Quality Control tools: FastQC, FastQScreen, FASTX

Data Normalization

Data Normalization is an essential component in the pipeline of the RNA sequencing analysis. In gene expression data, normalization is used to correct for differences in the amount of total RNA that was extracted from each sample This ensures that the gene expression levels are comparable across samples, allowing for more accurate comparison and interpretation of the results.

After aligning the raw fastq files and obtaining a count matrix of genes/transcripts per sample,

Dimensionality reduction and Data Visualizations

Differential Gene Expression Analysis Algorithms and Tools

Differential gene expression is where we look at the distruution of expression in one group versus another group of samples, and determine a gene to be differentially expressed if the distributions of the genes are statistically different.

Enrichment analysis

Small Molecule Predictions

Analyzing Large Patient Cohorts

Single Cell RNA Sequence Analysis

Technologies used to collect scRNA-seq data

File formats for Single Cell Data

Tools and workflows

Alignment

Quality control Metrics

Data normalization and Imputation

Dimensionality reduction and data visualization

Automatic identification of clusters

Differential gene expression analysis

Cell type identification

Trajectory analysis

Spatial single cell transcriptomics

Nakazato T, Ohta T, Bono H. Experimental design-based functional mining and characterization of high-throughput sequencing data in the sequence read archive. PLoS One. 2013;8(10):e77910.