back-to-basics: rna sequencing 101 - agilent sequencing... · life sciences & diagnostics group...

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Back-to-Basics:

RNA Sequencing 101

Presented By:

Jean Jasinski, Ph.D. Field Application Scientist

Agilent Technologies

Life Sciences & Diagnostics Group


Back-to-Basics: Agilent’s Five Part 101

eSeminar Series


Topics for Today’s Presentation

Library Prep (Strand-Specific) 2

1

3 How RNA- and DNA-Seq Differ

4

What is RNA-Seq?

Targeted vs. Whole Transcriptome

5

Not approved for use in diagnostic

procedures

Summary & Upcoming 101 eSeminars


What is RNA-Seq?

Application of massively parallel next generation sequencing to

detect and quantify the RNA from a sample

seqwright.com

Isolate RNA

from samples

Prepare library

Obtain sequences;

Analyze data


Many Technologies for Transcriptome Studies


Research goals in transcriptome studies

• Novel transcript discovery

• Novel gene fusions

• Novel exons/alt. start sites

• Alternative splicing

• Differential expression

analysis

• Development of a gene

signature

• SNP detection

Profiling Discovery

Gene Expr.

Alt. Splicing

SNP


Agilent Microarrays vs. RNA-Seq

Microarrays complement RNA-Seq

Agilent’s (free) customization

capabilities allow you to

incorporate RNA-Seq

discoveries into microarray

experiment(s)




1


4

What is RNA-Seq?


5


procedures



Basic RNA-Seq Library Prep Steps

Isolate Total RNA PolyA selection or

ribosomal and/or global

depletion

1st strand cDNA synthesis

2nd strand cDNA synthesis

Sequence

Chemical or enzymatic

fragmentation

Repair ends & adaptor

ligation

PCR Amplification and

optional barcoding


5’ 3’

3’ 5’

Why Strand Specific RNA Sequencing?

• To discover antisense transcripts with potential regulatory roles

• To identify overlapping transcripts

• To accurately measure the expression level of a given transcript

Sultan et al, 2012 Biochem Biophys Res Commun 15;422(4):643-6

5’ 3’

3’ 5’


ILM

InPE1.0

primer

SureSelect

Pre-Capture

Primer Prepared Library

PCR

ATGC TACG

ATGC TACG

ATGC

TACG

GCG

CGC

GCG CGC

GCG CGC

Prepared Library

PCR

ATGC TACG

ATGC

UACG

GCG

CGC

GCG CGC

Non-Strand-Specific

Library Prep

Directional

(strand-specific)

Library prep

How is Strand Specificity Preserved?

SureSelect

primer

RNA Seq ILM

Reverse PCR primer

Brown = P5 Adapter (drives forward read)

Green = P7 Adapter (drives reverse read)

UDG treatment removes the

dU-containing strand so it isn’t amplified


Strand-specificity RNA-Seq Identified

Cis-NATs (Natural Anti-sense Transcripts) in Rice




1


4

What is RNA-Seq?


5


procedures



How RNA-Seq and DNA-Seq Differ

DNA-Seq

• Mechanical shearing (Covaris)

• Total DNA used in library prep

• Quality assessed by fragment size and concentration

• Replicates not required; some analysis use paired samples

RNA-Seq

• Enzymatic/chemical fragmentation

• More RNA species: requires initial

selection from total RNA (poly-A,

ribosomal depletion, globin

depletion)

• RIN or RINe (RNA-Integrity

Number) to assess integrity of

initial RNA isolation; also size

distribution and concentration

• Analysis more complex (biological

replicates for gene expression ,

normalization)


Quality Metrics of RNA-Seq Libraries

Levin et al. (2010), Nature Methods 7(9):709-15

• Library Complexity

• Strand-Specificity

• Coverage (reads/locus)

(10X, 20X, 100x)

• Sequencing Depth (number

of reads/sample)

• Mapping Efficiency (how

many reads can be aligned

to genome/transcriptome)




1


4

What is RNA-Seq?


5


procedures



More Sequencing Means Greater Diversity of RNA

Types

Tarazona et al., Genome Res. 2011 Dec;21(12):2213-23

Need more reads to

detect transcripts with

lower abundance like

regulatory RNA than

for protein coding

genes

Protein Coding RNA


How Much Sequencing is Needed?

Depends on experimental goal

Sequencing Depth

• Expressed as numbers of reads

per sample

• No set recommendation

30M-80M reads/sample

(Gene Expression)

100-200M reads/sample

(alt splice, novel transcripts)

Coverage

• Number of reads/locus

30X for Common SNPs

>50X Rare SNPS

Lander/Waterman Equation

Illumina HiSeq 2000: 300M-400M

paired end reads/lane; ~11 days

MiSeq: 30M-34M reads/run; 6-39 hours


How much sequencing for whole transcriptome RNA-

Seq equivalent to an Agilent Gene Expression array?

Similar dynamic range to Agilent’s Microarrays

only achieved after 80M reads!

# reads

1M

10M

40M

80M

Myth: NGS has

much better

dynamic range

than microarrays


Target Enrichment: It’s just like fishing…

Why perform target enrichment?

1. Sequence only your desired regions of interest (exons, gene panels, etc.)

2. Sequence more samples per lane/run (i.e., multiplex)

3. Save time and money

4. Faster time to data = Smaller datasets

5. Identify variants in samples with increased reliability and accuracy = Higher

Coverage

What is the basic concept?

• Isolate the transcripts of interest

• Regions that are captured/amplified from initial library (pre-capture library) undergo additional amplification and processing creating a post-capture library


Benefit of Target Enrichment RNA Target Enrichment

An enriched library provides more reads/transcript and better sequence coverage for

specific targets for the same sequencing depth


RNA-Seq Target Enrichment in Cancer

Aim: RNA Target Enrichment of 467 Cancer Genes (~all Tyr Kinases

+ Genes from Cancer Gene Census); Overall >800 target transcripts

Method: Enrich cDNA from K-562 CML cell line cDNA libraries and

compare results from before and after target enrichment

No bias


Benefits of RNA-Seq Target Enrichment in Cancer

• Increased coverage improves ability to find rare transcripts and novel fusions

• Whole transcriptome analysis requires >40x more sequencing to achieve

same depth for targeted regions


How SureSelect RNA Target Enrichment Works


Indexing (barcoding): More Samples/Lane

Used with Target-Enriched Libraries • Index is a nucleotide barcode, usually 6 bp in length

• Links reads to samples

• Included with sequencing adapter

• Allows multiple samples to be mixed on single lane to take full advantage of sequencing capacity

• Also known as multiplexing




1


4

What is RNA-Seq?


5


procedures



Which technology is best suited for your project?

RNA-Seq

• Discovery of transcripts, fusions, splice variants

• Higher cost (2-3x more than a microarray)

• Complicated data analysis

Microarrays

• Differential expression studies

• Lower cost with higher sample throughput

• Well-established data analysis

Profiling? Discovery?

What scientific question am I trying to address?

How much sample material do I have?

How fast do I need/want the results?

How much money do I have?

How many replicates will I need?


Key Considerations for RNA-Seq

• Choose the approach most suited for your research

question (whole transcriptome or targeted enrichment)

• Think outside “the box”: sequencing costs + library prep

costs + reagents + time + analysis

• Consult your friendly statistician/bioinformatician to

determine how many total reads/sample, how many

reads per locus, and how many (biological) replicates are

needed to address the biological question

• Develop a plan for data analysis; practice with NGS data

deposited in GEO, SRA, ArrayExpress


Key Considerations for RNA-Seq


Why we know NGS 101 & more…

Agilent Technologies Solutions for the NGS workflow

The Gold Standard for Sample QC

2100 Bioanalyzer Instrument & Kits

2200 TapeStation Instrument & Kits

NGS Analysis Software

GeneSpring NGS

SureCall

Validation Technologies

qPCR- Mx system & Brilliant reagents

Microarrays- CGH, CGH+SNP,

Gene Expression & miRNA

The Leader in NGS Target Enrichment

SureDesign

SureSelect

HaloPlex

Bravo Automation


The NGS 101 eSeminar Series continues…

Event Date & Time Speaker Topics

Methyl-Seq 101 Wed, Oct 9

4 pm ET

Alex Siebold, PhD

Field Application

Scientist

• Methylation Mechanisms and

Significance

• Review of Comparative

Technologies

• Introduction to Methyl-Seq

NGS Data Analysis 101 Thu, Oct 10

1 pm ET

Jean Jasinski, PhD

Field Application

Scientist

• Analysis Workflows, File Formats,

and Data Filtering

• DNA-Seq vs. RNA-Seq

Considerations

• Integrating Disparate Data Sets to

Create a More Complete Story

NGS Panels 101 Fri, Oct 11

1 pm ET

Adam Hauge,

University of

Minnesota

• Panel Design Process

• Quality at the Bench: Tips, Tricks,

and Lessons Learned

• Considerations for Future Panels


Contact Us

800.227.9770

[email protected]

www.agilent.com/genomics

http://www.agilent.com/genomics

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