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First, below is a snapshot of what a RIP-Seq "peak" region might look like. This data was from a well-known PRC2 RIP-Seq dataset included in the same paper that the RIP-Seq schematic diagram was taken from:
Here
, the reads were filtered to remove all duplicates (i.e. reads aligning to the exact same genomic coordinates). This is a strategy sometimes used to counteract the problem of PCR artifacts, particularly when quantitative measurements are less important than whether the protein and RNA are associated (a binary classification problem). Thus, each 'read' as it is depicted above could represent several actual reads from the raw data. The reads occur mostly in exons, The reads occur mostly in exons (though you can't see that in this view) and are distributed over the length of both Tsix and Xist, a known binding partner of PRC2both validated binding partners of PRC2. Though the identification of a binding site is not really possible from this data, the presence of reads distributed along relatively long stretches of the transcript increases confidence that the observed enrichment is not a technical artifact of alignment (or something similar).
In a CLIP or PAR-seq CLIP experiment, this view "peak" would look quite different, something like this:
Figure from Hafner M, Lianoglou S, Tuschl T, Betel D. Genome-wide identification of miRNA targets by PAR-CLIP. Methods. 2012;58(2):94-105.
Data from Lipchina I, Elkabetz Y, Hafner M, et al. Genome-wide identification of microRNA targets in human ES cells reveals a role for miR-302 in modulating BMP response. Genes Dev. 2011;25(20):2173-86.
This is a peak from an Ago2 PAR-CLIP experiment in human embryonic stem cells. The bars indicate the read count at each position, where red indicates a matching base and yellow indicates a mismatching base. As the scale bar indicates, libraries with extensive digestion ("footprinting"), traceable mutations, and known motifs allow the discovery of extremely narrow target sites at base pair resolution. Those additional criteria also help filter out false positives, which short RNA libraries are susceptible to since filtering for PCR duplicates is complicated by the expectation of low-complexity read distributions.