monaLisa MOtif aNAlysis with Lisa European Bioconductor Meeting 2019 Dania Machlab Lukas Burger Michael Stadler Friedrich Miescher Institute for Biomedical Research Background and Motivation Co-binding Chromatin remodeling Use monaLisa to: Blocking • Identify Enriched motifs repositioning • Select motifs explaining observed changes Architectural role Francois Spitz & Eileen E. M. Furlong (2012) Nature Reviews Genetics Background and Motivation Enhancer Gene A Genome ATAC-seq Condition 1 RNA-seq Condition 1 ATAC-seq Condition 2 RNA-seq Condition 2 Predicted TFBS Identify Enriched Motifs d ensity of promoters delta methylation enrichment (log2) FDR (−log10) CTCF CTCFL RARAvar2 Rarbvar2 KLF4 Percent G+C Klf1 100 80 Klf12 60 E2F7 40 20 BHLHE41 0 log2 enrichment KLF13 2 ZEB1 1 ERG 0 −1 ETS1 −2 FDR ETV5 10 ELK3 8 6 ETV1 4 ETV4 2 0 FEV FLI1 ERF ETV3 ID4 KLF14 SP4 enrichment (log2) FDR (-log10) Select Motifs using Stability Selection Randomized lasso stability selection weakness Lasso with Lasso Randomized Lasso parameter Cross Validation Stability Selection Stability Selection observed logFC predicted TFBS regularization parameter Y X perform ~ regularized regression large � small � true signal noise Meinshausen & Bühlmann (2010) Journal of the Royal Statistical Society Select Motifs Explaining Observed Changes in Accessibility NFATC1 TEAD2 TEAD3 NKX2−8 Nkx2−5(var.2) NFIC Pear. Cor. KLF5 1 0.5 GATA3 0 −0.5 −1 Gata1 GATA1::TAL1 HNF1A Nr2f6 Hnf4a 8 − NFIC KLF5 Nr2f6 Hnf4a Gata1 GATA3 TEAD2 TEAD3 HNF1A 5(var.2) NFATC1 NKX2 − GATA1::TAL1 Nkx2 0.0 0.2 0.4 0.6 0.8 1.0 glmnet::glmnet and staBs::staBsel used proBaBility selection Summary and Outlook • We can identify TFs enriched in regions of interest that display certain log-fold changes • We can select TFs that are likely to explain the observed log-fold changes using stability selection • We can be use any fold-change defined on regions of interest (ATAC-seq, methylation, expression, ChIP-seq …) to select motifs explaining the observed logFC • We want to look at motif enrichment without using existing databases (unbiased view) • Enriched k-mers, grouping them, aligning them to predict the motif • Submit to Bioconductor • https://github.com/fmicompbio/monaLisa.