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Differential peak calling software tools | CLIP sequencing data analysis

Although comparison of RNA-protein interaction profiles across different conditions has become increasingly important to understanding the function of RNA-binding proteins (RBPs), few computational approaches have been developed for quantitative comparison of CLIP-seq datasets.

Source text:
(Wang et al., 2014) dCLIP: a computational approach for comparative CLIP-seq analyses. Genome Biol.

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A peak-calling tool based on the zero-truncated negative binomial regression model that is able to incorporate external information to guide the site identification process. Piranha can also optionally use additional external covariates into the peak-calling process, and identify sites of differential binding occupancy between cell types, conditions or development stages. Transcript abundance influences the read counts at sites in IP datasets and Piranha can successfully incorporate RNA-seq control data to ameliorate this bias. By considering this additional information, more accurate peak calls are arrived at.
DRME / Differential RNA MEthylation
A software package for the differential RNA methylation analysis at small sample size scenario from MeRIP-Seq data. The proposed model can effectively describe within-group biological variability at small sample size scenario and handles the impact of transcriptional regulation on RNA methylation. We tested the newly developed DRME algorithm on simulated and 4 MeRIP-Seq case-control studies and compared it with Fisher's exact test. It is in principle widely applicable to several other RNA-related data types as well, including RNA Bisulfite sequencing and PAR-CLIP.
Applies enrichment analysis on any type of sequencing data from two conditions. User can find out how significant the difference of these two conditions are in terms of the number of reads overlapping regions of interest. Pyicoenrich is a command of Pyicoteo, a suite of tools for the analysis of high-throughput sequencing data. Also, it provides a count file with the already calculated read counts for the interesting regions. Pyicoenrich included several standard normalization methods for the counts.
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