In addition to measuring levels of DNA and RNA in a cell, there are certain applications where it is important to know not just the level of particular sequences but also how these sequences are interacting with the proteome. These interactions can strongly influence the regulation of gene expression and are, therefore, critical for a complete understanding of how the cell is operating. For example, it can be used to determine which sequences are associated with a particular transcription factor or to reveal more information about the overall structure of the chromatin.
The most popular of these methods, ChIP-Seq (chromatin immunoprecipitation sequencing), is used for analyzing protein-DNA interactions. In this procedure the proteins are crosslinked with DNA to preserve their in vivo association. The DNA is then sheared and a protein-specific antibody is used to isolated the DNA fragments (via immunoprecipitation) that are closely linked with the protein of interest. The crosslinks are then reversed and the DNA is purified in preparation for sequencing.
A related method, CLIP-Seq (cross-linking immunoprecipitation sequencing), is used to determine which RNA sequences are associated with the protein of interest. The steps are similar, but the crosslinking occurs between RNA and protein and the resulting RNA molecules are converted to DNA prior to sequencing. CLIP-Seq is also known as RIP-Seq (RNA immunoprecipitation sequencing) and HITS-CLIP (high-throughput sequencing – cross-linking immunoprecipitation).
(See also MeDIP-Seq)
ChIP-Seq: Johnson DS, Mortazavi A et al. (2007) Genome-wide mapping of in vivo protein–DNA interactions. Science 316: 1497–1502
ChIP-Seq: Robertson G et al.(2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods 4: 651–657
CLIP-Seq: Sanford et al. (2009) Splicing facor SFRS1 recognizes a functionally diverse landscape of RNA transcripts. Genome Research 19: 381-394
Key Platform Characteristics
|# of reads||Critical||Benefits the most from having a large number of reads as it increases the sensitivity and precision of the measurements|
|Read length||Not Important||While having longer reads can help with alignment of the sequence, it is generally not a driving factor|
|Error rate||Not Important||Most applications benefit from lower error rates, but ChIP-Seq is one of the most forgiving applications in this regard|
|Paired-end reads||Nice to have||Paired-end reads may help to define the binding sites more accurately|
|Mate-pair reads||Irrelevant||The large insert sizes of mate-pair libraries aren’t useful for the relatively short binding regions|
|Multiplexing||Irrelevant||As the number of reads per run increases on the leading platforms, multiplexing becomes necessary to maintain a low cost per sample (while still providing enough reads per sample)|
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