Fig. 1

Overview of MOCCS profiles for human TF ChIP-seq samples across TFs and cell types. A and B Procedure for obtaining MOCCS profiles. Human TF ChIP-seq samples across diverse TFs and cell types were obtained from ChIP-Atlas. Subsequently, MOCCS2, a previously developed k-mer-based motif discovery method, was applied to the ChIP-seq dataset. Each ChIP-seq sample was represented as a profile of TF-binding specificity scores (MOCCS2scores) for each k-mer sequence, designated as a MOCCS profile. C Similarities in MOCCS profiles between ChIP-seq samples were marked by similarities in TFs (TF families), and interactions with other TFs. D Comparing the MOCCS profiles for the same TF in different cell type classes showed cell-type-dependent TF-binding specificities. Half of the analyzed TFs exhibited differences in DNA-binding specificity across cell types. For the TFs that we could not perform statistical tests on due to a lack of data, etc., they are marked as Not Applicable (N.A.). E Differential k-mer detection. Differential analysis of the MOCCS profiles revealed differentially bound k-mers between ChIP-seq samples of different cell types or TFs. F The ΔMOCCS2score for a single-nucleotide polymorphism (SNP) was defined as the difference in the MOCCS2score between k-mers on reference and alternative alleles (ref-k-mers and alt-k-mers) in a single ChIP-seq. The ΔMOCCS2score was used to predict the effects of the SNP on TF binding, which were validated with the results of in vitro and in vivo assay data. G: ΔMOCCS2score can be used to interpret how significant non-coding SNPs from GWAS studies affect the binding of TFs in specific cell types