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.