RNA Seq: Principle and Workflow of RNA Sequencing

Summary

This video delves into RNA-Seq, a next-generation sequencing technique that uncovers RNA presence and quantity in biological samples. It traces RNA-Seq's evolution from Sanger sequencing to Illumina's dominance and emerging third-generation technologies. RNA-Seq offers high-throughput, single-base resolution profiling, overcoming microarray and Sanger sequencing limitations. The video outlines the workflow, from RNA isolation to data generation, and touches on challenges like PCR bias. It also discusses the importance of quality assessment and the technology's ability to detect sequence variations and gene regulation.

Takeaways

• 🧬 **RNA-Seq Definition**: RNA sequencing (RNA-Seq) uses next-generation sequencing (NGS) to analyze the presence and quantity of RNA in a sample at a specific developmental stage or physiological condition.
• 📈 **Development of RNA-Seq**: RNA-Seq evolved from Sanger sequencing and microarrays to become dominant with the advent of NGS technologies like Illumina and Roche 454.
• 🔎 **Advantages of RNA-Seq**: RNA-Seq offers high-throughput RNA profiling at single base resolution with low background noise, surpassing microarrays and Sanger sequencing in sensitivity and specificity.
• 🧪 **Third-Generation Sequencing**: Third-generation sequencing technologies like Pacific Biosciences provide long-read sequencing, useful for identifying new transcripts and isoforms without fragmentation.
• 📉 **Challenges in RNA-Seq**: Challenges include short read biases and PCR amplification biases that can affect gene expression quantitation.
• 🧪 **Workflow of RNA-Seq**: The workflow involves converting RNA into cDNA fragments, attaching sequencing adapters, generating sequence data, and aligning reads to a reference genome or transcriptome.
• 🌟 **Types of Reads**: RNA-Seq classifies reads into exonic, junction, and poly-A reads to generate a base resolution expression profile.
• 🧪 **Sample Collection**: Total RNA is isolated and processed, often involving ribosomal RNA depletion to enrich non-ribosomal RNA species.
• 🔬 **Fragmentation**: RNA molecules are fragmented into smaller pieces for sequencing, with cDNA fragmentation providing information about the 3' ends and RNA fragmentation accessing the transcript body.
• 📊 **Bioinformatics Analysis**: The first step in RNA-Seq analysis is quality assessment, followed by read mapping or assembly, and gene expression level inference.
• 🔍 **Applications**: RNA-Seq is used to detect differential expression, SNPs, fusion genes, and post-transcriptional gene regulation.

Q & A

What is RNA-seq and what does it reveal about biological samples?

RNA-seq, short for RNA sequencing, uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a specific developmental stage or physiological condition. It is a powerful tool for analyzing the dynamic cellular transcriptome, which is essential for interpreting the functional elements of the genome, revealing molecular constituents of cells and tissues, and understanding development and disease.

What technological advancements have influenced the development of RNA-seq?

The development of RNA-seq has been influenced by several technological advancements, including Sanger sequencing technology, microarrays, and the advent of next-generation sequencing technologies. The first RNA-seq paper was published in 2006 using Roche 454 technology, and the technology matured with the advent of Illumina technology in 2008. Third-generation sequencing technologies, such as Pacific Biosciences' long-read sequencing, are also emerging in RNA-seq.

How does third-generation sequencing technology impact RNA-seq?

Third-generation sequencing technology, such as Pacific Biosciences' long-read sequencing, allows for full-length transcript sequencing without the need for fragmentation. This is useful for identifying new transcripts and novel isoforms, alternative splicing sites, fusion gene expression, and allelic expression more accurately.

What are the advantages of RNA-seq over traditional microarray and Sanger sequencing?

RNA-seq has several advantages over traditional microarray and Sanger sequencing, including high throughput RNA profiling at single base resolution, low background noise, the ability to map transcribed regions and gene expression simultaneously, and the capability to distinguish different isoforms and allelic expression. It also requires a lower amount of RNA and is less costly.

What are the challenges faced by RNA-seq?

RNA-seq faces challenges such as short read biases and PCR biases. Short read lengths were a concern, but Illumina sequencing technology has increased read length and throughput. PCR amplification can impact the accuracy of gene expression quantitation, but amplification-free technologies and PCR-free methods for Illumina sequencing have been developed to address this.

What is the workflow of RNA-seq using high-throughput sequencing technology?

The workflow of RNA-seq involves converting long RNAs into a library of cDNA fragments through RNA or DNA fragmentation, attaching sequencing adapters to each cDNA fragment, and generating sequence data from both ends in a high-throughput manner. The resulting sequence reads are aligned with the reference genome or transcriptome and classified into exonic, junction, and poly-A reads to generate a base resolution expression profile.

How is total RNA isolated for RNA-seq?

Total RNA is usually isolated via organic extraction or silica membranes of spin columns. The total RNA sample is then processed either by direct selection of polyA RNA or by selective removal of ribosomal RNA, as ribosomal RNA is often not the research focus and can greatly reduce the coverage of useful transcripts.

Why is ribosomal RNA depletion preferred over polyA RNA selection?

Ribosomal RNA depletion is preferred over polyA RNA selection because it enriches all non-ribosomal RNA species, including tRNA, ncRNA, non-polyA mRNA, and pre-processed RNA. This approach captures a broader range of RNA species compared to polyA RNA selection, which may miss some RNA transcripts that lack polyA tails.

How are larger RNA molecules prepared for deep sequencing technologies?

Larger RNA molecules need to be fragmented into smaller pieces (200 to 500 nt) before deep sequencing technologies. cDNA fragmentation is biased towards the identification of sequences from the 3' ends of transcripts, while RNA fragmentation provides access to the precise identity of the transcript body.

What are the different platforms that dominate RNA-seq?

RNA-seq is currently dominated by three different platforms: Illumina, Ion Torrent (Thermo Fisher Scientific), and Pacific Biosciences' SMRT. These platforms offer varying read lengths and technologies for sequencing.

How does RNA-seq enable the detection of differential expression and other genomic features?

RNA-seq allows for the detection of differential expression across treatments or conditions by normalizing the differences between samples. It also enables the identification of SNPs, fusion genes, and post-transcriptional gene regulation, such as RNA editing, degradation, and translation.

Outlines

00:00

🔬 Introduction to RNA Sequencing (RNA-seq) and its Evolution

This paragraph introduces RNA sequencing (RNA-seq), explaining its role in using next-generation sequencing (NGS) to detect the presence and quantity of RNA in biological samples. RNA-seq helps analyze the dynamic transcriptome of cells, crucial for understanding gene functions, cell composition, and disease mechanisms. The paragraph also discusses the historical development of RNA-seq, starting from early transcriptomics methods like SAGE and microarrays, and how NGS surpassed microarrays. The first RNA-seq paper was published in 2006, with the technology becoming dominant by 2008, driven by advancements in Illumina technology and the promise of third-generation sequencing technologies like Pacific Biosciences for more comprehensive transcript identification.

05:01

🧬 RNA-seq’s Advantages and Challenges

Here, the technological advantages of RNA-seq over older methods like microarray and Sanger sequencing are discussed. RNA-seq offers high throughput, single-base resolution, low background noise, and cost efficiency. It enables accurate mapping of gene expression and identification of different isoforms and allelic expressions. However, challenges like short read lengths and PCR biases are mentioned, though improvements in sequencing technology, such as longer reads and amplification-free techniques, are mitigating these issues. Third-generation sequencing is highlighted for full-length transcript sequencing, further improving RNA-seq's accuracy.

📊 RNA-seq Workflow Overview

This section details the typical RNA-seq workflow, starting from the conversion of long RNAs into cDNA fragments. The cDNA fragments are then sequenced, and the sequence reads are aligned with reference genomes or transcriptomes. The paragraph outlines the different types of reads generated—exonic, junction, and poly(A)—which help create a base-resolution gene expression profile. The workflow includes RNA extraction methods like organic extraction or silica membranes and highlights the importance of isolating non-ribosomal RNA to improve coverage of relevant transcripts. Challenges with fragmentation and bias are also mentioned, alongside the benefits of using RNA fragmentation for more even coverage across transcripts.

🧪 Ribosomal RNA Depletion and Fragmentation Methods

This paragraph focuses on ribosomal RNA depletion, a key step in RNA-seq for enriching non-ribosomal RNA species like tRNA, non-coding RNA, and mRNA. It explains two popular depletion methods: hybridization with biotin-labeled anti-rRNA probes, followed by removal with magnetic beads, and selective degradation of rRNA by exonucleases. The paragraph also discusses the importance of fragmentation, which breaks down larger RNA molecules into smaller pieces for sequencing. Different methods of fragmentation provide varying biases, with RNA fragmentation covering more of the transcript body, while cDNA fragmentation focuses on transcript ends.

🧬 Preserving Strand-Specific Information and RNA-seq Platforms

This section explains how strand-specific information, crucial for understanding transcriptional direction, is often lost in classic NGS protocols. To counter this, methods such as pre-treating RNA samples with sodium bisulfite or directly ligating RNA adapters have been developed. The paragraph also reviews the major RNA-seq platforms, including Illumina, Ion Torrent, Roche 454, and Pacific Biosciences, comparing their read lengths. Longer reads are valuable for studying complex transcriptomes, revealing exon connectivity and sequence variations, which helps in understanding gene expression and regulation more comprehensively.

🖥️ Bioinformatics and Data Analysis in RNA-seq

This paragraph emphasizes the bioinformatics analysis required for RNA-seq. The first step involves quality assessment, where low-quality and adapter sequences are removed to ensure accurate results. Once the sequence reads are filtered and mapped to the reference genome, gene expression levels are inferred. The data can be used to generate a transcriptome map on a genome-wide scale, allowing the detection of differential gene expression across different treatments or conditions. Additional RNA-seq capabilities include identifying SNPs, fusion genes, and post-transcriptional gene regulation mechanisms like RNA editing and degradation.

Keywords

💡RNA-Seq

RNA-Seq, short for RNA sequencing, is a powerful tool that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a specific developmental stage or physiological condition. It is essential for interpreting the functional elements of the genome, revealing the molecular constituents of cells and tissues, and understanding development and disease. In the script, RNA-Seq is the central theme, with the video providing a detailed introduction to its development and workflow.

💡Next Generation Sequencing (NGS)

NGS is a collective term for modern sequencing technologies that allow for the sequencing of an entire genome at a much faster pace and lower cost than traditional Sanger sequencing. The script mentions that RNA-Seq utilizes NGS to analyze the cellular transcriptome, which is crucial for understanding gene expression and regulation.

💡Transcriptome

The transcriptome refers to the complete set of RNA molecules, including mRNA, rRNA, tRNA, and other non-coding RNA produced in the cells of an organism. In the context of the video, RNA-Seq is used to analyze the continuously changing cellular transcriptome, which is essential for understanding gene expression and regulation.

💡Sanger Sequencing

Sanger sequencing is a method of DNA sequencing that was first used for transcriptomics. The script mentions that Sanger sequencing enabled methods like Serial Analysis of Gene Expression (SAGE), which was one of the first attempts to quantify gene expression on a global basis.

💡Microarrays

Microarrays are a technology that uses complementary probe hybridization to measure the expression levels of thousands of genes simultaneously. The script discusses how microarrays quickly emerged and dominated the field of transcriptomics profiling for the next decade before being surpassed by NGS technologies.

💡Illumina Technology

Illumina is a sequencing platform that uses NGS technology and is widely used in RNA-Seq. The script notes that the era of RNA-Seq dominance began in 2008 with the maturity of Illumina technology, which has steadily increased read length and throughput since its introduction.

💡Third Generation Sequencing

Third generation sequencing technologies, such as Pacific Biosciences' long-read sequencing, are characterized by their ability to sequence full-length transcripts without the need for fragmentation. The script highlights that this technology is useful for identifying new transcripts and accurately identifying isoforms, alternative splicing sites, fusion gene expression, and allelic expression.

💡Isoforms

Isoforms are different versions of a protein that are produced by alternative splicing of a single gene. The script mentions that RNA-Seq can be used to distinguish different isoforms, which is crucial for understanding the complexity of gene expression and regulation.

💡Allelic Expression

Allelic expression refers to the expression of one of the two alleles of a gene. The script notes that RNA-Seq can be used to distinguish allelic expression, which is important for understanding gene regulation and the role of genetic variation in phenotypic diversity.

💡PolyA RNA

PolyA RNA refers to mRNA molecules that have a polyadenine tail, which is a characteristic feature of most eukaryotic mRNAs. The script explains that the oligo-dT based mRNA purification procedure is widely used in eukaryotes, highlighting its importance in the workflow of RNA-Seq.

💡Ribosomal RNA Depletion

Ribosomal RNA depletion is a method used to selectively remove ribosomal RNA from a sample before sequencing. The script mentions that this approach is preferred because it enriches all non-ribosomal RNA species, including tRNA, ncRNA, nonpolyA mRNA, and pre-processed RNA, which are often the focus of research.

Highlights

RNA-Seq is a powerful tool to analyze the cellular transcriptome, essential for understanding development and disease.

Sanger sequencing and microarrays were early methods for transcriptomics, but have been surpassed by next-generation sequencing.

The first RNA-Seq paper using Roche 454 technology was published in 2006, marking the beginning of RNA-Seq dominance.

Illumina technology matured in 2008, further advancing RNA-Seq capabilities.

Third-generation sequencing technologies like Pacific Biosciences offer long-read sequencing, useful for identifying new transcripts and isoforms.

RNA-Seq provides high throughput RNA profiling at single base resolution with low background noise.

Compared to microarray and Sanger sequencing, RNA-Seq requires less RNA and is less costly.

Challenges for RNA-Seq include short read and PCR biases, which newer technologies are addressing.

Long paired and strand-specific reads are used for higher map ability and de novo assembly of transcriptomes.

The workflow of RNA-Seq involves converting long RNAs into cDNA fragments, attaching sequencing adapters, and generating sequence data.

RNA samples are processed by direct selection of polyA RNA or by selective removal of ribosomal RNA.

Ribosomal RNA depletion is preferred for enriching all non-ribosomal RNA species.

RNA fragmentation is crucial for sequencing larger RNA molecules into smaller pieces suitable for deep sequencing technologies.

Strand specificity can be maintained through various methods, including sodium bisulfite treatment and direct ligation of RNA adapters.

RNA-Seq platforms include Illumina, Ion Torrent, Roche 454, and Pacific Biosciences, each with different read lengths and capabilities.

RNA-Seq allows for the detection of differential expression across treatments or conditions.

Quality assessment is a critical first step in RNA-Seq bioinformatics analysis, ensuring accurate results.

RNA-Seq enables the identification of SNPs, fusion genes, and post-transcriptional gene regulation.

For more information on RNA-Seq services, visit the website www.cdgenomics.com.

Transcripts

welcome back to the cd genomics's next

generation sequencing video series the

topic here is the development and

workflow of rna sec

we will give you a detailed introduction

to rna sec including what rna sec is the

development of it and the workflow of it

rna sec the abbreviation of rna

sequencing utilizes next generation

sequencing ngs to reveal the presence

and quantity of rna in a biological

sample at a specific developmental stage

or physiological condition rna sec is a

powerful tool to analyze the

continuously changing cellular

transcriptome which is essential for

interpreting the functional elements of

the genome revealing the molecular

constituents of cells and tissues and

understanding development and disease

sanger sequencing technology was first

used for transcriptomics which enabled

methods such as sage serial analysis of

gene expression

sage was one of the first attempts to

quantify gene expression on a global

basis

almost simultaneously microarrays

utilizing complementary probe

hybridization quickly emerged and came

to dominate the field of transcriptomics

profiling for the next decade

the advent of next generation

technologies has enabled the sequencing

approach to surpass microarray approach

in 2006 the first rna sec paper was

published by utilizing roche 454

technology the era of rna sect dominance

began in 2008 with the maturity of

illuminate technology

despite the popularization of the ngs

technologies the application of third

generation sequencing in rna sec is on

its way

pacific biosciences long read sequencing

technology can easily cover complete

transcript without the need of

fragmentation which is useful to

identify new transcripts and new

entrants thereby accurately identifying

isoforms alternative splicing sites

fusion gene expression and allelic

expression

rna sec is often referred to as rna

profiling using the next or third

generation sequencing technologies

compared to traditional microarray and

sanger sequencing it has multiple

advantages in technology specifications

applications and practical issues rna

sec is a high throughput rna profiling

technology at single base resolution

with low background noise

it can be used to simultaneously map

transcribed regions and gene expression

as well as distinguish different

isoforms and allelic expression

compared to microarray and sanger

sequencing it requires a lower amount of

rna and cost less

however rna sec is faced with several

challenges such as short read and pcr

biases

short read used to be one concern

but illumina sequencing technology has

steadily increased read length and

throughput since its introduction in

2007.

long paired and strand specific reads

are commonly used for higher levels of

map ability and de novo assembly of

transcriptomes

furthermore the third generation

sequencing technology such as pacific

biosciences smart enables full-length

transcripts sequencing

another concern is the impact of pcr

amplification on the accuracy of gene

expression quantitation via rna sec

helicos and some of the third sequencer

use an amplification free technology

there are also pcr-free methods for

illumina sequencing

the workflow of rna sec by utilizing

high-throughput sequencing technology is

illustrated in the left figure

briefly long rnas are first converted

into a library of cdna fragments through

rna or dna fragmentation

sequencing adapters are then attached to

each cdna fragment and sequence data are

generated in a high throughput manner

from both ends the resulting sequence

reads are subsequently aligned with the

reference genome or transcriptome and

are classified into three types exonic

reads junction reads and polio and reads

a base resolution expression profile can

be generated by using these three types

of sequence reads

following sample collection total rna is

usually isolated via organic extraction

or silica membranes of spin columns

total rna sample is subsequently

processed either by direct selection of

polya rna or by selective removal of

ribosomal rna because the abundant

ribosomal rna is usually not the

research focus and greatly reduces the

coverage of the useful transcript

oligodt based mrna purification

procedure is widely used in eukaryotes

however some rna transcripts that lack

the polya tails are missed compared to

the polya rna selection ribosomal rna

depletion approach is preferred because

it enriches all non-ribosomal rna

species

including trna ncrna nonpolio mrna and

pre-processed rna

there are two popular ribosomal rna

depletion methods one is the

hybridization of ribosomal rna with

biotin labeled anti-ribosomal rna probes

followed by removal with streptavid and

counted magnetic beads

the other is the selective degradation

of ribosomal rna by a5 prime to three

prime exonuclease that specifically

recognizes ribosomal rna with a five

prime phosphate

fragmentation is subsequently conducted

to reach the desired length for

different ngs technologies some small

rnas such as micrornas pee-wee

interacting rnas and short interfering

rnas can be directly sequenced without

fragmentation

larger rna molecules need to be

fragmented into smaller pieces 200 to

500 nt

before deep sequencing technologies

cdna fragmentation is usually strongly

biased towards the identification of

sequences from the three prime ends of

transcripts while rna fragmentation has

little bias over the transcript but is

depleted for transcript ends

therefore cdna fragmentation provides

valuable information about the precise

identity of these ends and rna

fragmentation provides access to precise

identity of the transcript body

in the classic ngs protocols adapters

are ligated on to share double-stranded

dna fragments however a major drawback

of this approach is the loss of

information on transcriptional direction

the pre-treatment of the rna samples

with sodium bisulfate can convert the

citrine into uridine widespread c to t

transition thereby marks the coding

stand of each transcript

some other methods that maintain strand

specificity have been proposed such as

direct ligation of rna adapters to the

rna sample before reverse transcription

the rna sec is currently dominated by

three different platforms alumina ion

torrid roche 454 and pacific biosciences

smart

read lengths range from 200 to 600 bp

for illumina 400 bp for ion torrent 400

to 700 bp for four five four pyro

sequencing system and 15 to 20 kilobits

for pacific biosciences smart platforms

longer reads or paired and short reads

can reveal connectivity between multiple

exons rna sec is a powerful method to

study complex transcriptomes and reveal

sequence variations in the transcribed

regions

quality assessment is the first step for

the bioinformatics analysis of rna sec

which ensures a coherent final result by

removal of low-quality sequences

over-represented sequences and adapter

sequences once all reads have been

filtered and mapped or assembled gene

expression levels can thus be inferred

leading to a genome-scale transcriptome

map in terms of quality and quantity

rna sec also allows detecting

differential expression across

treatments of conditions

normalization has to be conducted to

adjust the differences between samples

such as library size and gene-specific

features

furthermore rna sec enables us to

identify snps fusion genes and

post-transcriptional gene regulation

such as rna editing degradation and

translation

in the end if you want more information

about accurate and reliable rna sex

service please visit our website www

cd

we are more than happy to be of

assistance