MAAPER

MAAPER applies a probabilistic model to 3' end-linked (nearSite) reads to predict polyadenylation sites (PASs) and quantify alternative polyadenylation (APA) isoform abundance for analysis of APA events.

Key Features:

• Probabilistic model-based PAS prediction: Uses a probabilistic model to predict polyadenylation sites (PASs) from 3' end-linked (nearSite) reads.
• nearSite read modeling: Explicitly models nearSite reads generated by 3' end-linked RNA sequencing to extract APA information.
• APA event scope: Analyzes APA events occurring within 3' untranslated regions (UTRs) and introns.
• Data types supported: Applicable to both bulk and single-cell transcriptome data.
• Experimental design support: Supports unpaired and paired experimental designs.
• Statistical methodologies: Implements statistical methodologies to ensure robust analysis across experimental contexts.
• Accuracy and sensitivity: Provides accurate and sensitive prediction of PASs and quantification of APA isoform abundance.

Scientific Applications:

• PAS identification: Identification and mapping of polyadenylation sites from 3' end-linked sequencing data.
• APA isoform quantification: Quantification of relative abundances of APA isoforms from nearSite reads.
• Regional APA analysis: Investigation of APA events in 3' UTRs and introns to study gene regulation.
• Bulk and single-cell studies: Application to both bulk and single-cell transcriptome datasets.
• Comparative APA analysis: Comparative analysis of APA between conditions using unpaired or paired experimental designs.

Methodology:

MAAPER employs a probabilistic, model-based analysis of 3' end-linked (nearSite) reads and applies statistical methodologies to predict PASs and analyze APA events in 3' UTRs and introns across bulk or single-cell and unpaired or paired experimental designs.