1 - 9 of 9 results


A package for sketching the transcriptomic view of RNA-related biological features represented by genome based coordinates. Guitar extracts the standardized RNA coordinates with respect to the landmarks of RNA transcripts, with which hundreds of millions of RNA-related genomic features can then be efficiently analyzed within minutes. Built upon the highly efficient GRangesList structure and GenomicFeatures R/Bioconductor packages, Guitar can efficiently process millions of genomic features within minutes for efficient transcriptomic analysis. It may automatically download gene information from UCSC genome browser, including neighborhood DNA regions, and allocate the weight of ambiguous features.


A graphical model-based peak calling method for transcriptome-wide detection of m6A sites from MeRIP-seq data. MeTPeak models read count of m6A site and introduces a hierarchical layer of Beta variables to capture the variances and a Hidden Markov model (HMM) to characterize the reads dependency across a site. MetPeak prediction on real MeRIPseq datasets have suggested that it precisely recapitulates the motif and distribution of m6A sites, as well as correctly predicting the methylation differences among these methyltransferases.

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.


Predicts differential m6A methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. MeTDiff consists of two major steps: (1) determining and statistical modeling putative differentially m6A sites; (2) and significance test for the inferred regions. For the first step, it determines the putative differential m6A methylation sites (DMSs) to be tested for differential methylation. And for the second step, it computes the statistical significance using a likelihood ratio test.


An R package for uncovering the potential types of m6A methylation by clustering the degree of m6A methylation peaks in MeRIP-Seq data. This algorithm utilizes a hierarchical graphical model to model the reads account variance and the underlying clusters of the methylation peaks. MeTCluster is evaluated on both simulated and real MeRIP-Seq datasets and the results demonstrate its high accuracy in characterizing the clusters of methylation peaks. In addition, MeTCluster is a method for understanding the mechanisms and functions of m6A methylation.