RNA- seq Introduc1on. Promises and pi7alls

Transcription

1 RNA- seq Introduc1on Promises and pi7alls

2 RNA gives informa1on on which genes that are expressed How DNA get transcribed to RNA (and some1mes then translated to proteins) varies between e. g. - Tissues - Cell types - Cell states - Individuals - Cells

3 RNA gives informa1on on which genes that are expressed How DNA get transcribed to RNA (and some1mes then translated to proteins) varies between e. g. - Tissues - Cell types - Cell states - Individuals

4 RNA gives informa1on on which genes that are expressed How DNA get transcribed to RNA (and some1mes then translated to proteins) varies between e. g. - Tissues - Cell types - Cell states - Individuals

5 House keeping RNAs rrnas, trnas, snornas, snrnas, SRP RNAs, cataly1c RNAs (RNAse E) Protein coding RNAs RNA flavors (pre sequencing era) (1 coding gene ~ 1 mrna) Regulatory RNAs Few rare examples

6 ENCODE, the Encyclopedia of DNA Elements, is a project funded by the Na1onal Human Genome Research Ins1tute to iden1fy all regions of transcrip1on, transcrip1on factor associa1on, chroma1n structure and histone modifica1on in the human genome sequence.

7 ENCyclopedia Of Dna Elements

8 Different kind of RNAs have different expression values Landscape of transcrip/on in human cells, S Djebali et al. Nature 2012

9 What defines RNA depends on how you look at it Coverage Variants Abundance House keeping RNAs mrnas Regulatory RNAs Novel intergenic None Adapted from Landscape of transcrip/on in human cells, S Djebali et al. Nature 2012

10 Defining func1onal DNA elements in the human genome Statement A priori, we should not expect the transcriptome to consist exclusively of func1onal RNAs. Why is that Zero tolerance for errant transcripts would come at high cost in the proofreading machinery needed to perfectly gate RNA polymerase and splicing ac1vi1es, or to instantly eliminate spurious transcripts. In general, sequences encoding RNAs transcribed by noisy transcrip1onal machinery are expected to be less constrained, which is consistent with data shown here for very low abundance RNA Consequence Thus, one should have high confidence that the subset of the genome with large signals for RNA or chroma1n signatures coupled with strong conserva1on is func1onal and will be supported by appropriate gene1c tests. In contrast, the larger propor1on of genome with reproducible but low biochemical signal strength and less evolu1onary conserva1on is challenging to parse between specific func1ons and biological noise.

11 This is of course not without an debate Variants Most Dark Matter Transcripts Are Associated With Known Genes Perspective Harm van Bakel 1, Corey Nislow 1,2, Benjamin J. Blencowe 1,2, Timothy R. Hughes 1,2 * 1 Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada, 2 Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada The Reality of Pervasive Transcription Abundance Abstract Michael B. Clark 1, Paulo P. Amaral 1., Felix J. Schlesinger 2., Marcel E. Dinger 1, Ryan J. Taft 1, John L. Perspective A series Rinn of 3, reports Chrisover P. Ponting the last 4 few, Peter yearsf. have Stadler indicated 5, Kevin that av. much Morris larger 6, Antonin portion ofmorillon the mammalian 7, Joelgenome S. Rozowsky is 8, transcribed than can be accounted for by currently annotated genes, but the quantity and nature of these additional Mark B. Gerstein 8, Claes Wahlestedt 9, Yoshihide Hayashizaki 10, Piero Carninci 10, Thomas R. Gingeras 2 *, transcripts remains unclear. Here, we have used data from single- and paired-end RNA-Seq and tiling arrays to assess the quantity John ands. composition Mattick 1 * of transcripts in PolyA+ RNA from human and mouse tissues. Relative to tiling arrays, RNA-Seq Response to The Reality of Pervasive Transcription 1 Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia, 2 Watson School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, Harm Newvan York, United Bakel States, Corey of America, Nislow 3 Broad Institute,, Benjamin Cambridge, J. Massachusetts, BlencoweUnited 1,2, Timothy States of America, R. Hughes 4 MRC Functional * Genomics Unit, Department of 1Physiology, Banting and Anatomy Best Department and Genetics, of Medical University Research of Oxford, andoxford, TerrenceUnited Donnelly Kingdom, Centre5 for Department Cellular and of Computer Biomolecular Science, Research, University University of Leipzig, of Toronto, Leipzig, Toronto, Ontario, Germany, 6 Department Canada, 2of Department Molecular and of Molecular Experimental Genetics, Medicine, University Scripps of ResearchToronto, Institute, Ontario, La Jolla, Canada California, United States of America, 7 Institut Curie, UMR3244- Pavillon Trouillet Rossignol, Paris, France, 8 Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America, 9 University of Miami, Miami, Florida, United States of America, 10 Omics Science Center, RIKEN Yokohama Institute, Tsurumi-ku, Yokohama, Kanagawa, Japan Clark et al. criticize several aspects of tic transcripts greatly increases their emphasized the lack of abundant pervasive our study [1], and specifically challenge abundance [7,8]. transcription in our study. Clark et al. cite our assertion that the degree of pervasive We acknowledge that the phrase quoted papers that have previously documented transcription has previously been overstated. by Clark et al. in our Author Summary pervasive transcription, and point out that We disagree with much of their should have read stably transcribed, or several different approaches have been

12 Biochemical evidence not enough to iden1fy func1onal RNAs Defining functional DNA elements in the human genome Kellis M et al. PNAS 2014;111:

13 One gene many different mrnas

14 RNA seq course

15 The RNA seq course From RNA seq to reads Mapping reads programs Transcriptome reconstruc1on using reference Transcriptome reconstruc1on without reference QC analysis srna analysis Differen1al expression analysis mrnas mirnas Genome annota1on using RNA and other sources Differen1al expression using mul1 variate analysis RNA long read analysis

16 From RNA to short reads

17 Promises and pi7alls Long reads Low throughput (- ) Complete transcripts (+) Only highly expressed genes (- - ) Expensive (- ) Low background noise (+) Easy downstream analysis (+) Micro Arrays High throughput (+) Only known sequences (- ) Limited dynamic range (- ) Cheap (+) High background noise (- ) Not strand specific (- ) Well established downstream methods (+) RNAseq High throughput (+) Frac1ons of transcripts (- ) Full dynamic range (+- ) Unlimited dynamic range (+) Cheap (+) Low background noise (+) Strand specificity (+) Re- sequencing (+) Signal # trancripts/cell EST MicroArray RNAseq

18 How are RNA- seq data generated? Sampling process

19 RNA seq reads correspond directly to abundance of RNAs in the sample

20 RNA to reads RNA- > AAAAAAAA PolyA (mrna) RiboMinus (- rrna) Size <50 nt (mirna ).. enrichments - > library - > Size of fragment Strand specific 5 end specific 3 end specific.. reads - > Single end (1 read per fragment) Paired end (2 reads per fragment)

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22 Transcriptome assembly using reference

23 Transcriptome assembly without reference

24 Mapping long reads to reference

25 Quality control - samples might not be what you think they are Experiments go wrong 30 samples with 5 steps from samples to reads has 150 poten1al steps for errors Error rate 1/100 with 5 steps suggest that one of every 20 samples the reads does not represent the sample Mixing samples 30 samples with 5 steps from samples to reads has ~24M poten1al mix ups of samples Error rate 1/ 100 with 5 steps suggest that one of every 20 sample is mislabeled Combine the two steps and approximately one of every 10 samples are wrong

26 RNA QC Read quality Mapping sta1s1cs Transcript quality Compare between samples

27 Differen1al expression analysis using univariate analysis Typically univariate analysis (one gene at a 1me) even though we know that genes are not independent

28 Gene set analysis and data integra1on SCP hidden by gene downregulation?

29 microrna analysis (Jakub) (Berezikov et al. Genome Research, 2011.)

30 Single cell RNA- seq analysis (Sandberg, Nature Methods 2014)

31 Short reads Long reads

32 Long reads Short reads Long reads

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