cis-regulatory enrichment analysis in human, mouse and fly

cis-regulatory enrichment analysis in human, mouse and fly Zeynep Kalender Atak, PhD Laboratory of Computational Biology VIB-KU Leuven Center for Brain & Disease Research

Laboratory of Computational Biology Deciphering gene control Genome control Undifferentiated tissue Differentiated cells

Gene signature co-expressed genes list of differentially expressed genes Enhancer signature TF ChIP-seq peaks ChIP-seq peaks against chromatin marks List of differentially acetylated / methylated regions Single-cell expression profile Cells Genes expression profile of genes across single cells SCENIC

iregulon cis-regulatory sequence analysis on gene signatures

iregulon cis-regulatory sequence analysis on gene signatures Set of co-expressed genes What are the key transcription factors (TFs) and the direct target genes (TGs)? What are the co-factors and their direct target genes? Janky & Verfaillie et al. (2014), PLoS Computational Biology

iregulon cis-regulatory sequence analysis on gene signatures TG4 TG2 TF2 TG3 TG1 Set of co-expressed genes TF1 Janky & Verfaillie et al. (2014), PLoS Computational Biology

iregulon cis-regulatory sequence analysis on gene signatures 20003 candidate motifs motif clustering (CRMs) cross-species conservation whole-genome rankings gene set enrichment AUC 20003 PWMs 1797 human TFs RANKING RECOVERY TG4 TG2 TF2 TG3 TG1 TF1 M4 MOTIF2TF MAPPING TF6 TF7 TF5 M3 M1 TF4 M2 TF5 TF3 1) motif-tf annotations 2) in input 3) orthology 4) motif similarity Janky & Verfaillie et al. (2014), PLoS Computational Biology TF1 TF2

Set of differentially expressed genes Master Regulons & cofactors Human & mouse & fly ChIP-derived targets TF regulon Set of mirna target genes mirna and TF co-regulons Functional network cluster Regulatory network cluster One TF node TF meta-targetome Janky & Verfaillie et al. (2014), PLoS Computational Biology

Using iregulon to decipher the p53 targetome Set of differentially expressed genes Master Regulons & cofactors

iregulon Using iregulon to decipher the p53 targetome the guardian of genome one of the most studied TFs downstream GRN is not clear Bieging & Attardi et al. (2012), Trends in Cell Biology

iregulon Using iregulon to decipher the p53 targetome Nutlin-3a 801 up-regulated genes MCF7 cells 790 downregulated genes Janky & Verfaillie et al. (2014), PLoS Computational Biology

iregulon Using iregulon to decipher the p53 targetome 801 up-regulated genes iregulon results panel

iregulon Using iregulon to decipher the p53 targetome Motifs

iregulon Using iregulon to decipher the p53 targetome Tracks

iregulon Using iregulon to decipher the p53 targetome

iregulon Using iregulon to decipher the p53 targetome

iregulon Using iregulon to decipher the p53 targetome Nutlin-3a 801 up-regulated genes TF NES # of targets # of motifs TP53 5.682 307 6 AP1 (FOS/JUN) 5.046 232 4 FOX 4.046 108 8 NFY 3.541 50 4 TF NES # of targets # of motifs MCF7 cells 790 downregulated genes E2F 13.135 653 42 NFY 5.245 493 18 KDM2B 3.703 56 1 TP53 master regulator; AP1, FOX and NFY as co-factors No TP53 motif in the repressive network Janky & Verfaillie et al. (2014), PLoS Computational Biology

iregulon Using iregulon to decipher the p53 targetome

iregulon Validation with p53 ChIP-seq with Nutlin-3a ChIP-seq p53 Stimulated ChIP-seq p53 Input RNA-seq p53 Stimulated RNA-seq p53 Not stimulated Refseq Genes CDKN1A

iregulon Using iregulon to decipher the p53 targetome 100 p53 direct targets are enriched in ChIPseq peaks 80 are novel targets Janky & Verfaillie et al. (2014), PLoS Computational Biology

iregulon More case studies in http://iregulon.aertslab.org/tutorial.html

i-cistarget cis-regulatory sequence analysis on enhancer signatures

i-cistarget cis-regulatory sequence analysis on enhancer signatures Imrichová et al. (2015), Nucleic Acids Research

i-cistarget cis-regulatory sequence analysis on enhancer signatures Similar rank & recovery approach to iregulon Relies on candidate regulatory regions rather than genes Imrichová et al. (2015), Nucleic Acids Research

TF ChIP-seq peaks The motifs and TF ChIP-seq tracks of the ChIP'ped factor Motifs and ChIP-seq tracks of co-factors Correlated DHS/Faire/ Histone marks Set of differentially active regions The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks A set of genes The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks

Analyzing GATA1 ChIP-seq data on K562 cell line TF ChIP-seq peaks The motifs and TF ChIP-seq tracks of the ChIP'ped factor Motifs and ChIP-seq tracks of co-factors Correlated DHS/Faire/ Histone marks

i-cistarget Analyzing GATA1 ChIP-seq data on K562 cell line hg19, mm9, dm3 bed file gene symbol (HGNC) i-cistarget region IDs Imrichová et al. (2015), Nucleic Acids Research

i-cistarget Analyzing GATA1 ChIP-seq data on K562 cell line GATA motifs DHS regions on K562 cell line

TF ChIP-seq peaks The motifs and TF ChIP-seq tracks of the ChIP'ped factor Motifs and ChIP-seq tracks of co-factors Correlated DHS/Faire/ Histone marks Set of differentially active regions The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks A set of genes The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks

Melanoma phenotype switching Set of differentially active regions The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks

i-cistarget Identifying master regulators of melanoma phenotype switching Malignant melanoma development is characterized by a bistable switch between different states Invasive and proliferative states were described in melanoma cell cultures (Hoek et al., 2008) We observed them also in tumour biopsies (TCGA: RNA-seq for 375 samples) Verfaillie & Imrichová & Kalender Atak et al. (2015), Nature Communications

i-cistarget Identifying master regulators of melanoma phenotype switching ChIP-seq against H3K27Ac in 9 proliferative short-term cultures ChIP-seq against H3K27Ac in 2 invasive short-term cultures 20122 differentially active regions 6669 proliferative regions 13452 invasive regions Verfaillie & Imrichová & Kalender Atak et al. (2015), Nature Communications

TF ChIP-seq peaks The motifs and TF ChIP-seq tracks of the ChIP'ped factor Motifs and ChIP-seq tracks of co-factors Correlated DHS/Faire/ Histone marks Set of differentially active regions The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks A set of genes The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks

p53 targetome revisited A set of genes The most correlated motifs and TF ChIP-seq tracks Correlated DHS/Faire/ Histone marks

i-cistarget p53 targetome revisited Treatment of MCF-7 cells with Nutlin-3a Library Preparation & Sequencing Differential Expression Analysis & Filtering 801 up (790 down) Control (2x) / Nutlin-3a (2x) 20-30 million reads/sample FDR<0.05 & log2fc >1 Rank Feature NES Logo 1 transfac_pro-m01656 Description: V$P63_01 Possible TFs: TP63, TP53, TP73 6.19819 167 candidate regions 2 taipale-racatgycnngrcatgty-tp53-dbd Description: RACATGYCNNGRCATGTY-Tp53-DBD Possible TFs: TP63, TP53 5.97630 3 transfac_pro-m01651 Description: V$P53_03 Possible TFs: TP63, TP53, TP73 5.79805 7 lcbtfbs_mcf7_p53_nutlin Description: lcbtfbs_mcf7_p53_nutlin 5.34445 111 ENCFF001WPZ Description: DNase-seq on human MCF-7 3.11541

i-cistarget p53 targetome revisited ChIP-seq p53 Stimulated ChIP-seq p53 Input RNA-seq p53 Stimulated RNA-seq p53 Not stimulated Refseq Genes CDKN1A

i-cistarget More case studies in https://gbiomed.kuleuven.be/ apps/lcb/i-cistarget-nar/index.php#examples

SCENIC Single Cell regulatory Network Inference and Clustering

SCENIC Single Cell regulatory Network Inference and Clustering Aibar et al., Submitted

SCENIC Mapping regulatory cell states of melanoma with SCENIC tsne on expression data tsne on binary regulon activity SCENIC 1252 malignant cells from 14 biopsies (Tirosh, I. et al. (2016) Science)

SCENIC Mapping regulatory cell states of melanoma with SCENIC tsne on expression data tsne on binary regulon activity SCENIC 1252 malignant cells from 14 biopsies (Tirosh, I. et al. (2016) Science) Containing cancer cells from 10 different tumors

SCENIC Mapping regulatory cell states of melanoma with SCENIC

SCENIC Mapping regulatory cell states of melanoma with SCENIC

SCENIC Mapping regulatory cell states of melanoma with SCENIC

LCB aertslab.org