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.
Allows reversible N6-Methyl-Adnosine (m6A)-seq data quality control. trumpet assesses the quality from mainly 3 perspectives, including (1) statistics of sequencing reads distribution with respect to different genomic regions; (2) the strength of the immunoprecipitation signal evaluated by the exome signal extraction scaling (ESES) and other statistical approaches; and (3) comparison between different biological replicates to identify possible outliers. It is applicable to other fragmented RNA immunoprecipitation sequencing techniques, such as m1 A-seq, CeU-Seq, Ψ-seq and hMeRIP-seq.
Identifies functional significant N6-methyladenosine (m6A)-regulated genes and m6A-associated diseases from analyzing an extensive collection of methylated RNA immunoprecipitation sequencing (MeRIP-seq) data. Deep-m6A consists of a convolutional neural network (CNN) model for single-base m6A prediction. It integrates messenger RNA (mRNA) sequence information with MeRIP-seq data and trained on different single-base m6A sites.
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.