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Defines peaks in CLIP-seq dataset. CLIPper combines features from many CLIP peak-finding algorithms. To reduce false-positives, it employes a three-pass filter on our peaks. For each gene it calculates the false-discovery rate threshold (FDR), which is the "height" of reads mapped at a single genomic position that is likely to be noise, determined by randomly scattering the same number of faux reads as real reads across a faux transcript that is the same effective length as the real transcript.
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.
Enables robust peak detection even in low abundance transcripts. ASPeak is a fast and efficient expression-sensitive peak caller for CLIP- and RIP-Seq data that is sensitive to differential expression levels of target transcripts. Implementation of this method permits to run on multiple processors resulting in a significant speedup when used on high performance computing centers. It is supported with extensive documentation that allows experienced bioinformaticians to customize their analyses using detailed parameter files.
PARA-suite / PAR-CLIP analyzer suite
A toolkit for processing and aligning short and error-prone sequencing reads. PARA-suite is implemented in Java using HTSjdk, a Java API for high-throughput sequencing data formats. The PARA-suite allows the user to estimate a sequencing run-specific error profile, combine the results of multiple reference database alignments, cluster an aligned sequencing read dataset (‘PAR-CLIP read simulator and hierarchical clustering’), run the PAR-CLIP read simulator, benchmark an alignment of simulated PAR-CLIP sequencing reads and run a full processing pipeline for error-prone short read alignments.
A model-based approach to detect RNA-RBP binding sites in PAR-CLIP. PAR-CLIP HMM integrates models to identify enriched regions and high-confidence binding sites into one rigorous statistical model. An advantage of our integrative modeling is that the posterior probability of being a binding site is estimated based on data with less information loss, as compared with two-stage modeling approaches. This facilitates more accurate statistical inference, so our method would provide more reliable binding sites based on the false discovery rate.
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