MoAIMS

MoAIMS employs a mixture negative-binomial model to detect enriched regions in MeRIP-Seq data and infer signal proportions for analyses of N6-methyladenosine (m6A) across transcriptomes.

Key Features:

• Efficient Detection: Employs a mixture negative-binomial model to identify enriched regions in MeRIP-Seq data while maintaining high processing speed.
• Strand-aware Analysis: Utilizes RNA sequencing strand information to reduce ambiguity and improve accuracy in calling enriched regions.
• Compatibility and Flexibility: Designed for transcriptome immunoprecipitation sequencing experiments and compatible with various RNA sequencing protocols.
• Treatment Effect Inference: Infers signal proportions from MeRIP-Seq treatment datasets to indicate perturbations such as changes in m6A methyltransferase activity.

Scientific Applications:

• m6A mapping: Detection of enriched regions associated with N6-methyladenosine (m6A) to support studies of RNA modification across species.
• Treatment and perturbation analysis: Quantification of signal proportion changes to evaluate effects of perturbing methylation processes, including m6A methyltransferases.

Methodology:

Implements a mixture negative-binomial model that uses RNA-seq strand information to call enriched regions and infers signal proportions from MeRIP-Seq treatment datasets; implemented in R.