RNA-seq library prep introduction

NESCent Academy

Outline

• Methodologies and history• RNA-seq challenges• Library preparation methods• Common queries• Validation

• Spike-in and future-proofing your work

Gene expression

RNA sequencing

Condition 1(normal colon)

Condition 2(colon tumor)

Isolate RNAs

Sequence ends

100s of millions of paired reads10s of billions bases of sequence

Generate cDNA, fragment, size select, add linkersSamples of interest

Map to genome, transcriptome, and

predicted exon junctions

Downstream analysis

Metholologies for RNA-Seq studies

Mapping transcription start sites Strand-specific RNA-Seq Characterization of alternative splicing patterns Gene fusion detection Targeted approaches using RNA-Seq Small RNA profiling Direct RNA sequencing Profiling low-quantity RNA samples

Pre NGS Transcriptomics

Hybridization-based approaches Genomic tiling microarrays Fluorescently labelled cDNA with microarrays

Sequence-based approaches Sanger sequencing of cDNA or EST libraries Serial analysis of gene expression (SAGE) Cap analysis of gene expression (CAGE) Massively parallel signature sequencing (MPSS)

RNA-seq

Challenges• RNAs consist of small exons that may be separated by large

introns– Mapping reads to genome is challenging

• The relative abundance of RNAs vary wildly– 105 – 107 orders of magnitude– Since RNA sequencing works by random sampling, a small fraction of

highly expressed genes may consume the majority of reads– Ribosomal and mitochondrial genes

• RNAs come in a wide range of sizes– Small RNAs must be captured separately– PolyA selection of large RNAs may result in 3’ end bias

• RNA is fragile compared to DNA (easily degraded)• Bacterial samples may need to be depleted of rRNA

Rubbish in = Rubbish out

RNA-seq library prep methodologies

• Two main routes for mRNA-seq preparation– Illumina TruSeq prep– Script-seq

• Generally Script-seq is our favourite

RNA Illumina Tru-Seq library prep2 days for 8 sam

ples

5ug of total RNA ~$100 per sampleNot strand-specific

Size selection step

Adaptor ligation and standard library preparation

Script-seq method2 hours for 12 sam

ples

< 1ug of RNA~$150 per sampleStrand-specific

DNA library preparation: RNA fragmentation and DNA fragmentation compared

a | Fragmentation of oligo-dT primed cDNA (blue line) is more biased towards the 3' end of the transcript. RNA fragmentation (red line) provides more even coverage along the gene body, but is relatively depleted for both the 5' and 3' ends. Note that the ratio between the maximum and minimum expression level (or the dynamic range) for microarrays is 44, for RNA-Seq it is 9,560. The tag count is the average sequencing coverage for 5,000 yeast ORFs. b | A specific yeast gene, SES1 (seryl-tRNA synthetase), is shown.

Common questions: How much library depth is needed for RNA-seq?

• My advice. Don’t ask this question if you want a simple answer…

• Depends on a number of factors:– Question being asked of the data. Gene expression? Alternative

expression? Mutation calling?– Tissue type, RNA preparation, quality of input RNA, library

construction method, etc. – Sequencing type: read length, paired vs. unpaired, etc.– Computational approach and resources

• Identify publications with similar goals• Pilot experiment• Good news: 1/8th -1 lane of recent Illumina HiSeq data should

be enough for most purposes

Coverage versus depth

Common questions: What mapping strategy should I use for RNA-seq?

• Depends on read length• < 50 bp reads– Use aligner like BWA and a genome + junction database– Junction database needs to be tailored to read length

• Or you can use a standard junction database for all read lengths and an aligner that allows substring alignments for the junctions only (e.g. BLAST … slow).

– Assembly strategy may also work (e.g. Trans-ABySS)• > 50 bp reads– Spliced aligner such as TopHat or Trinity

Common questions: how reliable are expression predictions from RNA-seq?

• Are novel exon-exon junctions real?– What proportion validate by RT-PCR and Sanger sequencing?

• Are differential/alternative expression changes observed between tissues accurate?– How well do differential expression values correlate with

qPCR?• 384 validations

– qPCR, RT-PCR, Sanger sequencing• See ALEXA-Seq publication for details:

– Also includes comparison to microarrays– Griffith et al. Alternative expression analysis by RNA

sequencing. Nature Methods. 2010 Oct;7(10):843-847.

Common questions: How many replicates?

• As many as you can afford • Tophat/Cufflinks statistics work best with

three or more biological replicates

Validation (qualitative)

33 of 192 assays shown. Overall validation rate = 85%

RNA-seq vs Microarray

Spike-in controls

• How can you identify limits of detection and ensure your data can be compared to future platforms or new library prep methods? (e.g. How does Oxford Nanopore compare to Illumina sequencing?)

• Spike-in RNA to your total RNA which has a known concentration

• http://tools.invitrogen.com/content/sfs/manuals/4455352C.pdf

• Cost - $20 per sample

RNA-seq spike-in protocol

Assessing lower limit of detection

Assessing fold change response

Take home

• Good quality total RNA of 1-10ug• Have 3 or more biological replicates• Unless you have good reason, use a Script-seq

type protocol• Use a standard spike-in as an internal control

and to ensure samples can be compared across platforms

• Don’t forget to validate key findings with qPCR!