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RNA-seq library prep introduction NESCent Academy.

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Presentation on theme: "RNA-seq library prep introduction NESCent Academy."— Presentation transcript:

1 RNA-seq library prep introduction NESCent Academy

2 Outline Methodologies and history RNA-seq challenges Library preparation methods Common queries Validation Spike-in and future-proofing your work

3 Gene expression

4 RNA sequencing Condition 1 (normal colon) Condition 2 (colon tumor) Isolate RNAs Sequence ends 100s of millions of paired reads 10s of billions bases of sequence Generate cDNA, fragment, size select, add linkers Samples of interest Map to genome, transcriptome, and predicted exon junctions Downstream analysis

5 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

6 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)

7 RNA-seq

8 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 – 10 5 – 10 7 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

9 Rubbish in = Rubbish out

10 RNA-seq library prep methodologies Two main routes for mRNA-seq preparation – Illumina TruSeq prep – Script-seq Generally Script-seq is our favourite

11 RNA Illumina Tru-Seq library prep 2 days for 8 samples 5ug of total RNA ~$100 per sample Not strand-specific Size selection step Adaptor ligation and standard library preparation

12 Script-seq method 2 hours for 12 samples < 1ug of RNA ~$150 per sample Strand-specific

13 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.

14 Common questions: How much library depth is needed for RNA-seq? My advice. Dont 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: 1/8 th -1 lane of recent Illumina HiSeq data should be enough for most purposes

15 Coverage versus depth

16 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

17 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 Oct;7(10):

18 Common questions: How many replicates? As many as you can afford Tophat/Cufflinks statistics work best with three or more biological replicates

19 Validation (qualitative) 33 of 192 assays shown. Overall validation rate = 85%

20 RNA-seq vs Microarray

21 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 Cost - $20 per sample

22 RNA-seq spike-in protocol

23 Assessing lower limit of detection

24 Assessing fold change response

25 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 Dont forget to validate key findings with qPCR!

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