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 Isolate RNAs Generate cDNA, fragment, size select, add linkers Samples of interest Condition 1 (normal colon) Condition 2 (colon tumor) Sequence ends Map to genome, transcriptome, and predicted exon junctions 100s of millions of paired reads 10s of billions bases of sequence 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 prep 2 days for 8 samples Size selection step Adaptor ligation and standard library preparation 5ug of total RNA ~$100 per sample Not strand-specific

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