ChIP-chip was the star of chromatin analysis until ChIP-seq came along and stole the limelight. ChIP-seq uses the same chromatin IP procedures are ChIP-chip; however, it couples it with quantitative next-generation sequencing technology to detect enrichment peaks. After the IP sample is generated, it is prepared for sequencing using any of the next-gen sequencing technologies (see Hui, 2014 for review of these methods).
Similar to ChIP-chip, the input sample or an IP with a non-specific antibody is also analyzed as a control. This allows the sequencing of the sample of interest to be normalized (Liang and Keles, 2012). The advantages and disadvantages of ChIP-seq stem from next-gen sequencing. The major advantage is the increase in resolution and reduction in noise over ChIP-chip (Massie and Mills, 2012). Also, as next-gen technologies advance, the cost is becoming permissive for larger scale studies to use ChIP-seq.
The biggest disadvantage of ChIP-seq is the cost compared to ChIP-chip. Also, ChIP-seq requires a lot of tissue, which can be prohibitive for some rare sample types (Gilfillan et al., 2012). ChIP-seq was the primary technology used in the ENCODE project, designed to fully map the human epigenome (Landt et al., 2012). ChIP-seq has also become very important in the study of human disease (Falk, 2010).
ChIP-seq Additional Reading
This review does a great job of covering the basics of ChIP-seq including workflow as well as data analysis. The author also goes into many of the advantages and disadvantages of the technology.
This paper is an excellent resource for all aspects of ChIP-seq data analysis. The authors explain not only the details of each step, but also popular software used and major challenges faced.
This review covers all the major next-gen sequencing technologies and how each works in detail. It also give great examples of the applications for each as well as strengths and weaknesses.
Reference List
- Falk, J. (2010). Using ChIP-based technologies to identify epigenetic modifications in disease-relevant cells. IDrugs 13, 169-174.
- Gilfillan, G.D., Hughes, T., Sheng, Y., Hjorthaug, H.S., Straub, T., Gervin, K., Harris, J.R., Undlien, D.E., and Lyle, R. (2012). Limitations and possibilities of low cell number ChIP-seq. BMC Genomics 13, 645-2164-13-645.
- Landt, S.G., Marinov, G.K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B.E., Bickel, P., Brown, J.B., Cayting, P., et al. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813-1831.
- Liang, K., and Keles, S. (2012). Normalization of ChIP-seq data with control. BMC Bioinformatics 13, 199-2105-13-199.
- Massie, C.E., and Mills, I.G. (2012). Mapping protein-DNA interactions using ChIP-sequencing. Methods Mol. Biol. 809, 157-173.