(Practical) Bioinformatics for CRISPR/Cas9

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1 (Practical) Bioinformatics for CRISPR/Cas9 Jacob Corn IGI Workshop 2016 Bioinformatics is (mostly) things you could do yourself Just done very fast

2 What makes these guides different? GAGTCCGAGCAGAAGAAGAA NGG EMX1 highly active relatively specific GACCCCCTCCACCCCGCCTC NGG VEGFA site2 highly active many off-targets GCGACGAGTCTCATTCAAAC NGG deubiquitinase no activity at all

3 General guide design algorithm Find guides region depends on experiment Map them to a reference genome guide site potential off-targets genes, transcripts, exons other annotations (DNAse, etc) Score features of guide itself annotations We ll cover these in detail for a few popular approaches

4 Finding guides = finding PAMs GCAGAGTCCAAGCAGAAGAAGAACGGGTCGTA look for PAMs count backwards 20 bases GAGTCCAAGCAGAAGAAGAA CGG AGTCCAAGCAGAAGAAGAAC GGG ACGACCGTTCTTCTTCTGCT CCA AGCAGAAGAAGAACGGTCGT TGG

5 How might we map guides? Whole sea of a genome Where is a 20mer? (or seqs like it) Analogous to short-read next gen sequencing BWA Cas-OFFinder Bae et al., 2014, Bioinformatics Li and Durbin, 2009, Bioinformatics Langmead et al., 2009, Genome Biology Langmead et al., 2011, Curr Protocols Bioinformatics

6 Now we know guide coordinates chr : chr : Resources to find what s there Choice of database outside the scope of this workshop Make sure you re using consistent genome versions! (e.g. GRCh37 vs GRCh38)

7 We want guides to be active Easy things to avoid, but sometimes forgotten: 4+ T/U in a row à Pol III terminator Avoid homopolymers in general GC > 30% or < 80% Avoid stable secondary structure in protospacer (Vienna fold prediction no structure in protospacer also protospacer doesn t interfere with constant region But surely it s more complicated than that Do active guides have certain sequences?

8 A screen to find properties of active guides (single cut gene knockout) Doench et al., Nat Biotech 2014 Make every guide across a surface marker gene Turn into a lentiviral library Sort for loss of protein Next-gen sequencing à active vs inactive guides? good guides CD33 guide activity across 3 lines bad guides Guide activity can be cell-line dependent Local sequence effects might be a problem But what about on average?

9 Training features of active guides What sequences are enriched/depleted in 20% most active guides? position 20 is good position 20 is bad Complex à We need a predictor Turn each base at each position (30mer) into a feature Also include dimers and GC content What separates top 25% guides from all others? Use machine learning to find sequence preferences à Rule Set 1 Doench et al., Nat Biotech 2014

10 Machine learning can be odd BEST learned properties GC TC TA GA AC CC CG A A G G G G A C G WORST learned properties GG GC G C G GG Matches what the plot above (trained on this data) Seems to do well in back-prediction Calls good guides 50-75% of the time TC T C Doench et al., Nat Biotech 2014

11 Maybe we need even more features and training? Contribution Highest ranked features Proximity to 5 end of gene Tm single and double base features e.g. G at position Feature # à Rule Set 2 Doench et al., Nat Biotech 2016

12 A tool to find active ko guides Machine learning works best when use = training Trained on Knockout by single indel Mouse and human cell lines Cell surface expression, survival Finds highly active guides for ko pretty well Can false-report good guides as bad Assumes One True Guide Sequence May not be good for CRISPRi/a Code is available on the website! Does what is advertised

13 But sequence is not the whole story???? Cas9 preference human mouse Doench et al. zebrafish Moreno-Mateos et al. some guides in different cells correlate well other cell lines correlate poorly

14 DNA repair also affects apparent guide activity -NOE +NOE NOE Richardson and Ray et al., in Nature Communications

15 Finding active CRISPRi/a guides Gilbert and Horlbeck et al., Cell 2014 Tile guides around transcription start site Look for phenotype dcas9-krab = CRISPRi Plus many more features à +300 dcas9-sun-vp64 = CRISPRa -400 à -50

16 We want guides to be specific Easy things to avoid: repeat sequences targeting highly conserved part of a gene family But is it more complicated? Based on data from Hsu et al., Nat Biotech 2013

17 Hsu et al, Nat Biotech 2013 Target-focused specificity measurements Either Make 1 guide, find all targets Make many guides, measure activity at one target Target EMX1(3 different places) Make all possible guide variants Which ones still target EMX1?

18 Guide seed region is stringent Non-seed is permissive Hsu et al, Nat Biotech 2013 single mismatches can be tolerated in non-seed region alternative seed vs non-seed AGTCCGAGCAGAAGAAGAA NGG non-seed seed

19 Mismatches >= 3 are not tolerated Close mismatches are worse? mismatches together mismatches apart >= 3 mismatches apart Hsu et al, Nat Biotech 2013

20 Turning data in to a score Trained weights from heatmap (training details a bit unclear) (likeσ, but products) penalizes mismatches close together penalizes total #of mismatches pos W[e] GAGTCCGAGCAGAAGAAGAA GAATCCGAGCAGAAGAAGAA ( ) GAGTCCGAGCAGAAGAAGGA ( ) GAGTCCGAGCAGAAGTAGGA ( )*( ) Hsu et al, Nat Biotech 2013

21 Total guide score Calculate score across all offtargets unclear how a sequence is chosen as potential offtarget, but uses bowtie2 Score normalized to 100 Many high-scoring off-targets decreases score Off-targets in exons are noted but not extra penalized Reports exon hits, not introns, UTRs, etc.

22 We can get fancier ~28,000 sgrnas targeting human CD33 all on-target all 1-base bulges all 1-base mismatches 65 on-targets w/ phenotype ~10,000 variants CFD Cutting Frequency Determination Doench et al, Nat Biotech 2016

23 How well off-target prediction work? Shengdar et al. Nat Biotech 2014 Both on- and off-target prediction are still in their infancy Plenty of room for improvement What s wrong?

24 Just finding OTs makes a big difference??!! bowtie2 (MIT website) has trouble finding all potential offtargets to score Use Cas-OFFinder to find offtargets, score with Hsu-Zhang ( score vs website ) Haeussler et al Genome Biology 2016 Doench et al, Nat Biotech 2016

25 Some easily accessible tools

26 crispr.mit input bp

27 crispr.mit output

28 Other tools CHOPCHOP Heuristic ( arbitary ) scoring Not necessarily worse than trained! Allows any length sequence Easy T7E1 primer design

29 CHOPCHOP input

30 CHOPCHOP output

31 CCTop input

32 CCTop Output Super fast Good enough(?)

33 Cas-OFFinder input Takes GUIDES as input Very slow Very methodical

34 There are a lot of tools out there

35 Benchling or DesktopGenetics demo as meta guide designers

36 Questions?

37 crispr.mit.edu Based on Hsu et al., Nat Biotech 2013 Find all sgrnas in a nt sequence What are likely off-targets? Supports many organisms human, mouse, zfish, worm, fly, etc Nice graphical interface We ll talk more about this in detail

38 CHOPCHOP described in Montague et al. NAR 2014