Additional file 1 of Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses
Creators: name: Zou, Zhaonan
name: Iwata, Michio
name: Yamanishi, Yoshihiro
name: Oki, Shinya
Publisher: name: figshare
Year: 2022
Subjects: subject: Biochemistry
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Neuroscience
subject: Pharmacology
subject: 39999 Chemical Sciences not elsewhere classified
subject: FOS: Chemical sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: Cancer

description: Additional file 1: Fig. S1 Comparison of gene expression profiles and annotated proteins contained in different databases. a, c Venn diagram showing genes shared between the chemical–gene (CTD and L1000) and disease–gene (CREEDS) association databases (a), and proteins shared between the ChIP-seq experiment (ChIP-Atlas) and gene/protein–disease association (DisGeNET) databases (c). b, d Bar charts showing the frequencies with which the genes appeared in CTD and CREEDS, with L1000 genes colored in orange (CTD) and green (CREEDS) (b), and the frequencies with which proteins were defined as disease-associated proteins in DisGeNET, with ChIP-Atlas TFs colored in blue (d). Fig. S2 Factors potentially affecting the distribution of AUROC scores. AUROC scores, sorted according to the highest enrichment score for each (a, c) chemical and (b, d) the number of DEGs that were used to predict (a, b) chemical–TF and (c, d) chemical–disease associations using the proposed ChIPEA-based approach. Fig. S3 Visual manual for GUI-based ChIPEA. a. Submission form for ChIPEA on the website. GUI-based ChIPEA is provided on the ChIP-Atlas website (termed “Enrichment Analysis” tool; https://chip-atlas.org/enrichment_analysis ). When used to identify pivotal TFs involved in drug MoAs, genome assembly should be set as “hg19/hg38” (hg19 was used in this paper). “TFs and others” needs to be selected in panel “1. Antigen Class”. “2. Cell type Class” and “3. Threshold for Significance” may be changed by the user according to demand. The “4. Enter dataset A” dialog box is to be filled in with the list of up-regulated genes, and the box “5. Enter dataset B” is for down-regulated genes. After specifying the “Distance range from TSS” in the “6. Analysis description” panel, the user can press the “Submit” button to submit the parameters to the server, and ChIPEA will initialize immediately. b. Interpretation of the results. The overlaps between the genomic loci (originating from panels 4 and 5 of the submission form) and reference peak call data (specified on upper panels 1–3 of the submission form) are counted using the bedtools intersect command (BedTools2; ver 2.23.0). The results are returned in html and tsv format. p-values are calculated using a two-tailed Fisher’s exact probability test. The null hypothesis is that the intersection between the reference peaks and the data submitted in panel 4 occurs at the same proportion as for the data in panel 5 of the submission form. q-values are calculated using the Benjamini &amp; Hochberg method. Fold enrichment was calculated by dividing the result from column 6 by the result from column 7 for a given row of data. If the ratio &gt; 1, the rightmost column is “TRUE”, meaning that the protein from column 3 is more likely to bind to the variable from panel 4 than to that from panel 5. Fig. S4 Identification of target TFs using a motif analysis-based method. TF binding profiles of three chemicals predicted by motif analysis using DEGs from CTD (testosterone, vorinostat, and JQ1). Dots indicate individual TFs and are colored orange only if they match the chemical–target associations recorded in KEGG DRUG database. AUROC scores are shown in the upper right corner of the volcano plot of each chemical.