MOtif aNAlysis with Lisa European Bioconductor Meeting 2019 Dania - - PowerPoint PPT Presentation

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Mar 04, 2024 271 likes •356 views

mo monaLi Lisa 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

SLIDE 1

mo monaLi Lisa

MOtif aNAlysis with Lisa

European Bioconductor Meeting 2019

Dania Machlab Lukas Burger Michael Stadler Friedrich Miescher Institute for Biomedical Research

SLIDE 2

Background and Motivation

Francois Spitz & Eileen E. M. Furlong (2012) Nature Reviews Genetics

Co-binding Chromatin remodeling Blocking repositioning Architectural role Use monaLisa to:

Identify Enriched motifs
Select motifs explaining
bserved changes

SLIDE 3

Background and Motivation

Genome

Gene A Enhancer

ATAC-seq Condition 1 ATAC-seq Condition 2 RNA-seq Condition 1 RNA-seq Condition 2 Predicted TFBS

SLIDE 4

Identify Enriched Motifs

CTCF CTCFL RARAvar2 Rarbvar2 KLF4 Klf1 Klf12 E2F7 BHLHE41 KLF13 ZEB1 ERG ETS1 ETV5 ELK3 ETV1 ETV4 FEV FLI1 ERF ETV3 ID4 KLF14 SP4

enrichment (log2) FDR (−log10)

Percent G+C 20 40 60 80 100 log2 enrichment −2 −1 1 2 FDR 2 4 6 8 10

delta methylation density of promoters

enrichment (log2) FDR (-log10)

SLIDE 5

Select Motifs using Stability Selection

Randomized lasso stability selection

Meinshausen & Bühlmann (2010) Journal of the Royal Statistical Society

bserved logFC

predicted TFBS Lasso with Cross Validation Lasso Stability Selection Randomized Lasso Stability Selection regularization parameter weakness parameter

true signal noise

small 𝜇 large 𝜇

Y ~ X perform regularized regression

SLIDE 6

Select Motifs Explaining Observed Changes in Accessibility

glmnet::glmnet and stabs::stabsel used

NFATC1 TEAD2 TEAD3 NKX2−8 Nkx2−5(var.2) NFIC KLF5 GATA3 Gata1 GATA1::TAL1 HNF1A Nr2f6 Hnf4a NFATC1 TEAD2 TEAD3 NKX2−8 Nkx2−5(var.2) NFIC KLF5 GATA3 Gata1 GATA1::TAL1 HNF1A Nr2f6 Hnf4a

Pear. Cor.

−1 −0.5 0.5 1

1.0 0.8 0.6 0.4 0.2 0.0

selection probability

SLIDE 7

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