miRMaster

miRMaster processes and analyzes small non-coding RNA sequencing (sncRNA-seq) data to identify, quantify, annotate, and perform downstream analyses including differential expression, batch-effect assessment, and embedding-based visualization across multiple RNA classes and species.

Key Features:

• Multi-Species Support: Supports eight species, including humans, mice, chickens, dogs, and cows.
• Comprehensive RNA Class Analysis: Analyzes ten non-coding RNA classes, including microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), and circular RNAs (circRNAs).
• Enhanced Downstream Analysis Modules: Performs batch-effect analysis and computes sample embeddings using Uniform Manifold Approximation and Projection (UMAP).
• Updated Annotation Databases: Uses miRBase, Ensembl, and GtRNAdb for annotation.
• Single Cell RNA Sequencing Support: Processes unique molecular identifiers (UMIs) for single-cell small RNA sequencing data.
• Standardized Output Formats: Exports results in community-standard formats including miRGFF3.
• Integrated Differential Expression Analysis: Integrates differential expression analysis with the miRNA enrichment analysis tool miEAA.

Scientific Applications:

• sncRNA-seq studies: Analysis of small non-coding RNA sequencing datasets across multiple species and RNA classes.
• Comparative and functional genomics: Comparative genomics, functional genomics, and systems biology investigations using multi-class sncRNA profiles.
• High-throughput analyses: Large-scale analyses of extensive sncRNA-seq datasets for high-throughput research.

Methodology:

Annotation against miRBase, Ensembl, and GtRNAdb; batch-effect analysis; sample embeddings via Uniform Manifold Approximation and Projection (UMAP); processing of unique molecular identifiers (UMIs) for single-cell data; integration of differential expression results with miEAA; and export in miRGFF3 format.