RNA SEQUENCING AND CHIP SEQUENCING

Presented by,

Jyoti Kumari

B.Tech(Bioinformatics)

What is RNA?

Within a multicellular organism and throughout its life, its genome stays mostly unchanged. Its cells, however, can have very distinct appearances, functions and respond differently to extracellular stimuli.

These differences are possible because cells make different use of stretches of the DNA, called genes, as templates to build functional cellular products in a process called gene expression. In the first step of gene expression, known as transcription, the information in the DNA is used to create ribonucleic acid molecules (RNA).

RNA is synthesised using one of the DNA strands as a template and has the same chemical structure except that thymine is replaced by uracil (U). Some RNA molecules can be the end product in themselves and some can in turn be used as a template for the creation of other molecules, proteins, in a process called translation.

RNAs that are used as a template for proteins arecalled messenger RNAs(mRNAs) and the ones that are not are non-coding RNAs (ncRNAs).

Transcriptome: RNA WORLD!! The transcriptome is the complete

set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition.

Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease.

Techonologies to deduce and quantify transcriptome

 Hybridization-based : Microarrays Advantages: 1. High Throughput 2. Relatively inexpensive Limitations: 1. Reliance upon existing knowledge about

genome sequence. 2. High background levels owing to cross-hybridization.

3. Limited dynamic range of detection 

Sequence Based: Sanger sequencing of cDNA or EST libraries

Limitations 1.  Low throughput

2. Expensive

Tag Based: Serial analysis of gene expression (SAGE)

Cap analysis of gene expression (CAGE)

Massively parallel signature sequencing (MPSS)

Advantages1. High throughput2. Provide precise, ‘digital’ gene expression levels.

Limitations1. Expensive

2.  Only a portion of the transcript is analysed. 3. Isoforms are generally indistinguishable from each other.

RNA-Sequencing Advantages 1. It is not limited to detecting

transcripts that correspond to existing genomic sequence.2. It is helpful for studying complex genomes3. High Throughput4. It can also reveal sequence variations (for example, SNPs) in the transcribed regions.

What is RNA Sequencing?

RNA sequencing or Rna-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies.

 It includes high-throughput shotgun sequencing of cDNA molecules obtained by reverse transcription from RNA, and next-generation sequencing technologies to sequence the RNA molecules within a biological sample in an effort to determine the primary sequence and relative abundance of each RNA molecule.

A typical RNA-Seq Experiment

Uses of RNA-Seq

1. Identify and quantify both rare and common transcripts, with over six orders of magnitude of dynamic range

2. Align sequencing reads across splice junctions, and detect isoforms, novel transcripts and gene fusions

3. Perform robust whole-transcriptome analysis on a wide range of samples, including low-quality samples

4. Identification of exons and introns and mapping of their boundaries.

5. Identification of the 5’ and 3’ ends of genes and identification of transcription start sites.

6. Quantification of exon expression and splicing variants.

RNA-Sequencing WORKFLOW:

A typical RNA-Seq experiments follows these steps:

RNA Preparation Since the goal of RNA-seq is to characterize the transcriptome

the first step naturally involves isolating and purifying cellular RNAs.

Isolation and purification of RNA typically involves disrupting cells in the presence of detergents and chaotropic agents.

After homogenization, RNA can be recovered and purified from the total cell lysate using either liquid-liquid partitioning or solid-phase extraction.

Typically the total RNA is then enriched for messenger RNA (mRNA). This can be done by either directly selecting mRNA or by selectively removing ribosomal RNA (rRNA).

To make the RNA suitable for RNA-seq it is typically fragmented and then the quality and fragmentation are assessed.

Library Preparation

In all cases an RNA-seq experiment involves making a collection of cDNA fragments which are flanked by specific constant sequences (known as adapters) that are necessary for sequencing. This collection (referred to as a library) is then sequenced using short-read sequencing which produces millions of short sequence reads that correspond to individual cDNA fragments.

After obtaining an RNA preparation that is suitable for RNA-seq the RNA must be converted to double-stranded complementary DNA (cDNA).

This comprises of two steps:1. First strand synthesis

In order to convert RNA to DNA the RNA must be used as a template for DNA polymerase. Most DNA polymerases cannot use RNA as a template. However, retroviruses encode a unique type of polymerase known as reverse transcriptases, which are able to synthesize DNA using an RNA template.2. Second strand synthesis

The second cDNA strand is synthesized by a DNA polymerase using the RT-synthesized

DNA- strand as a template.

To achieve the highest quality of data, quality is validated and the cDNA libraries are accurately quantified before sequencing.

Assessing the fragment size distribution of final RNA-seq libraries is also important. The sizes of the molecules should fall into the expected size range.

Fragment sizes can be evaluated via electrophoresis, preferably using a sensitive instrument such as an ABI Bioanalyzer.

Sequencing For sequencing, a sequencing

platform is required. The current leading platform for RNA-

seq is Illumina. This platform enables deep sequencing which is generally important for RNA-seq, and provides long enough, low-error reads that are suitable for mapping to reference genomes and transcriptome assembly.

Other platforms is PacBio platform.

Analysis Sequence reads are mapped with a combination

of SOAP and BLAT. SOAP is a very fast mapping program, and BLAT contains powerful options for mapping gapped reads.

Most tags will map back to a unique place in the genome.

There are two special types of tags:1. Gapped alignments: They are reads which

putatively span an intron.2. 3 end tags: They are sequence reads with a non- genomic run of.A. or .T. bases, indicating that they are the site of a polyadenylation event. These areuseful in determining the 3 ends of genes.

With the help of Bioinformatics we can calculate the expression level for each base pair of the genome.

it is possible to annotate the genome with information1. where introns are located (via gapped alignments)2. 5’ ends (via suddenexpression drops)3. 3’ ends (via sudden expression drops and 3 end tags).

ChIP Sequencing

ChIP sequencing, also known as ChIP-Seq, is a method used to analyze protein interactions with DNA.

ChIP stands for Chromatin Immuno-Precipitation and seq refers to the high throughput sequencing to detect bound genomic locations.

ChIP-Seq combines chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing to identify the binding sites of DNA-associated proteins.

It can be used to map global binding sites precisely for any protein of interest

USES ChIP-seq is used primarily to determine

how transcription factors and other chromatin-associated proteins influence phenotype affecting mechanisms.

 Determining how proteins interact with DNA to regulate gene expression is essential for fully understanding many biological processes and disease states.

Specific DNA sites in direct physical interaction with transcription factors and other proteins can be isolated by chromatin immunoprecipitation.

ChIP produces a library of target DNA sites bound to a protein of interest in vivo

Overview of ChIP sequencing

Why Chip-Sequencing is better approach

High Quality Data: Positional precision of mapped binding sites += 50bp

Wide Dynamic Range: Robust quantification for determining binding specificities of varying strengths.

High Signal-to-Noise-Ratio: Lower background than ChIP-chip, no cross hybridization.

Genome-Wide Analysis: Identifies any binding sites, not limited to array features.

Low Starting Material Requirement: Robust output from as little as 10ng of precious input.

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