SeqSaw

SeqSaw maps spliced reads and detects novel splice junctions in RNA-seq data to enable comprehensive identification of canonical GT-AG and non-canonical splicing events for transcriptome analysis.

Key Features:

• Spliced-read mapping: Maps spliced RNA-seq reads to identify exon–exon alignments.
• Unbiased junction detection: Detects novel splice junctions without relying solely on existing annotations.
• Canonical and non-canonical support: Identifies splice junctions with canonical GT-AG signals and non-canonical splice motifs.
• High sensitivity and specificity: Provides enhanced sensitivity and specificity for splice junction detection as reported in testing.
• ENCODE benchmarking: Was evaluated on two ENCODE RNA-seq datasets to assess performance against established techniques.
• De novo splicing analysis: Enables discovery of previously unannotated splicing events from high-throughput RNA sequencing.
• Tissue-specific event detection: Facilitates identification of tissue-specific splice junctions and novel transcripts.

Scientific Applications:

• Novel splice junction discovery: Identification of previously unannotated exon–exon junctions from RNA-seq data.
• Transcriptome annotation expansion: Expansion of known transcript catalogs by incorporating detected novel splice events.
• Tissue-specific splicing studies: Analysis of tissue-specific alternative splicing patterns and differential junction usage.
• Benchmarking splice-detection methods: Comparative evaluation of splice junction detection performance using ENCODE RNA-seq datasets.

Methodology:

Maps spliced reads and performs unbiased detection of splice junctions, including canonical GT-AG and non-canonical signals; performance was evaluated on two ENCODE RNA-seq datasets.