NASTIseq

NASTIseq identifies cis-natural antisense transcripts (cis-NATs) from strand-specific RNA sequencing (ssRNA-seq) data to detect and characterize sense–antisense transcript pairs and their regulatory associations.

Key Features:

• Model Comparison Framework: NASTIseq employs a model-comparison computational method to analyze ssRNA-seq data and account for variability in strand-specific library generation.
• Strand-Specific RNA Sequencing (ssRNA-seq): Leverages ssRNA-seq to profile both sense and antisense transcription and determine strand orientation of transcripts.
• Validation and Discovery: Applied to whole-root and cell-type-specific Arabidopsis ssRNA-seq datasets, confirming known cis-NAT pairs and identifying 918 additional cis-NAT pairs supported by polyadenylation data, alternative splicing patterns, and RT-PCR validation.
• Cell-Type-Specific Expression: Identified 209 cis-NAT pairs exhibiting opposite expression levels in neighboring cell types.
• Integration with Epigenetic Data: Integrates a genome-wide Arabidopsis epigenetic profile to identify a chromatin signature associated with cis-NAT transcription.
• Small-RNA Sequencing Analysis: Analysis of small-RNA sequencing data indicated that approximately 4% of identified cis-NAT pairs produce putative cis-NAT-induced siRNAs.

Scientific Applications:

• cis-NAT Discovery and Characterization: Identification and characterization of cis-NATs in plant transcriptomes using ssRNA-seq data.
• Cell-Type-Specific Regulatory Analysis: Investigation of opposing expression of cis-NAT pairs across neighboring cell types to study cellular regulatory roles.
• Epigenetic Association Studies: Linking cis-NAT transcription to chromatin signatures using genome-wide epigenetic profiles in Arabidopsis.
• siRNA Biogenesis Investigation: Detection of putative cis-NAT-induced siRNA production through small-RNA sequencing analysis.

Methodology:

Computational methods explicitly include model-comparison analysis of ssRNA-seq data, comparison with polyadenylation and alternative splicing patterns, integration of genome-wide epigenetic profiles, and analysis of small-RNA sequencing datasets.