ERANGE

ERANGE quantifies transcript abundance and maps splice junctions from RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) data to provide digital measurements of known and novel transcripts and to support gene model refinement.

Key Features:

• RNA-seq and ChIP-seq support: Processes data from RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) for transcript and locus analysis.
• Digital quantification: Records the frequency of gene representation in sequence samples to provide digital counts of transcripts.
• Mapping of short reads: Works with mapped 25-base-pair reads, including datasets of 41–52 million mapped reads from poly(A)-selected RNA.
• Tissue-specific data: Analyzes poly(A)-selected RNA derived from mouse brain, liver, and skeletal muscle.
• Wide dynamic range: Assesses transcript prevalence across a dynamic range spanning five orders of magnitude.
• Validation with RNA standards: Uses RNA standards to validate transcript quantification.
• Gene model refinement: Suggests new gene models and revises existing promoters, exons, and 3' untranslated regions (UTRs) based on mapped reads.
• Novel small RNA detection: Identifies novel microRNA precursors from sequencing data.
• Direct splice detection: Detects RNA splice events by mapping sequence reads that cross splicing junctions.
• Splicing event quantification: Reports approximately 145,000 distinct splicing events and identifies over 3,500 genes with one or more alternate internal splices.
• Exon alignment statistics: Observes that more than 90% of uniquely mapped reads align with known exons.

Scientific Applications:

• Transcriptome quantification: Quantifies transcript abundance across tissues to characterize expression levels of known and novel transcripts.
• Gene model discovery and revision: Proposes and refines gene models, including promoters, exons, and 3' UTR annotations.
• Alternative splicing analysis: Identifies and quantifies alternative splicing events and internal splice variants across genes.
• microRNA precursor identification: Detects candidate novel microRNA precursors from sequencing reads.
• Comparative methodology: Provides direct detection of RNA splice events in contrast to indirect methods such as microarrays or serial analysis of gene expression (SAGE).
• Dynamic range and quantitative validation: Assesses transcript prevalence over a wide dynamic range and validates measurements using RNA standards.

Methodology:

Uses mapped 25-base-pair reads (41–52 million per dataset) from poly(A)-selected mouse brain, liver, and skeletal muscle RNA, records read frequencies for digital quantification, maps reads that cross splicing junctions to detect splice events, and validates quantification with RNA standards; reports that >90% of uniquely mapped reads align to known exons.