The ChIA-PET tool, which is the first tool for ChIA-PET data analysis, uses a hypergeometric (HG) distribution to model count data. Among them, the ChIA-PET tool ( 7), ChiaSig ( 8), mango ( 9) and ChIA-PET2 ( 12) are popular ones. To distinguish signal from noise, many tools have been proposed ( 7–11). ![]() Thus, the main goal of ChIA-PET data analysis is to distinguish signal from noise using observed count data for potential pairs. Among these potential pairs, some are true interactive ones containing an interaction signal, while the others are of no interactions and are random noise. For the sake of simplicity, here we call such DNA regions and potentially interactive pairs as anchor regions and potential pairs, respectively. By mapping the paired tags to the reference genome, potentially interactive pairs of DNA regions, together with the counts of paired tags mapped to the pairs, can be identified. The tags generated by the original protocol are short (usually |$20 \pm 1$| base pairs), while the tags generated by the improved protocol are typically longer (the lengths vary and are up to 150 or 250 base pairs). ![]() Each read contains a tag (a piece of DNA sequence from the related genome) and a linker sequence (barcode). Recently, an improved (long read) ChIA-PET protocol was introduced ( 5) and has since been used in a study of genome-wide chromatin interactions mediated by CTCF ( 6) in the human genome.Ī typical ChIA-PET experiment generates tens of millions of paired reads. It has been widely used to study different proteins in different genomes, such as oestrogen receptor alpha in the human genome ( 2), RNA polymerase II in the human genome ( 3), CCCTC-binding factor (CTCF) in the mouse genome ( 4), etc. Chromatin Interaction Analysis by Paired-End Tag Sequencing ( 1) (ChIA-PET), first introduced in 2009, is an experimental assay for studying genome-wide chromatin interactions mediated by a protein of interest.
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