MEME-ChIP is a web service for analyzing ChIP-seq peak regions to identify transcription factor (TF) binding motifs. It performs five types of analysis: (i) ab initio motif discovery, (ii) motif enrichment analysis, (iii) motif visualization, (iv) binding affinity analysis, and (v) motif identification. It uses two motif discovery algorithms, MEME and DREME, which complement each other. MEME uses expectation maximization to discover probabilistic models of DNA-binding, while DREME uses regular expressions for shorter motifs. The service also uses AME for motif enrichment analysis, which can detect low levels of enrichment of known TF binding sites. MEME-ChIP allows large datasets to be analyzed without size restrictions. The output provides a multifaceted view of TF binding patterns and potential interactions. The service also compares discovered motifs to a database of known TF motifs to identify possible regulatory partners. MEME-ChIP complements other ChIP-seq motif analysis tools by offering additional analyses such as binding affinity and motif enrichment. It uses MAST and AMA algorithms for motif visualization and binding strength analysis. The service was tested on SCL ChIP-seq data, where it identified known SCL binding motifs and a composite motif involving SCL and GATA-1. It also identified novel motifs that could be further studied. MEME-ChIP is available as part of the MEME Suite at http://meme.nbcr.net. Funding was provided by the ARC Centre of Excellence in Bioinformatics and the National Institutes of Health. No conflicts of interest were declared.MEME-ChIP is a web service for analyzing ChIP-seq peak regions to identify transcription factor (TF) binding motifs. It performs five types of analysis: (i) ab initio motif discovery, (ii) motif enrichment analysis, (iii) motif visualization, (iv) binding affinity analysis, and (v) motif identification. It uses two motif discovery algorithms, MEME and DREME, which complement each other. MEME uses expectation maximization to discover probabilistic models of DNA-binding, while DREME uses regular expressions for shorter motifs. The service also uses AME for motif enrichment analysis, which can detect low levels of enrichment of known TF binding sites. MEME-ChIP allows large datasets to be analyzed without size restrictions. The output provides a multifaceted view of TF binding patterns and potential interactions. The service also compares discovered motifs to a database of known TF motifs to identify possible regulatory partners. MEME-ChIP complements other ChIP-seq motif analysis tools by offering additional analyses such as binding affinity and motif enrichment. It uses MAST and AMA algorithms for motif visualization and binding strength analysis. The service was tested on SCL ChIP-seq data, where it identified known SCL binding motifs and a composite motif involving SCL and GATA-1. It also identified novel motifs that could be further studied. MEME-ChIP is available as part of the MEME Suite at http://meme.nbcr.net. Funding was provided by the ARC Centre of Excellence in Bioinformatics and the National Institutes of Health. No conflicts of interest were declared.