CCIVR

CCIVR identifies cis-natural antisense transcripts (cis-NATs) from RNA-seq data and genome annotations to map overlapping antisense transcription and its effects on gene regulation and epigenetic modification.

Key Features:

• Implementation: Implemented as a Python-based bioinformatics package.
• Input data: Leverages RNA-seq data in conjunction with genome annotations and genome databases.
• Comprehensive identification: Detects all types of cis-NATs transcribed from the same genomic locus on the opposite DNA strand and overlapping partner gene transcripts.
• Multi-species analysis: Applied to genome databases to identify total cis-NAT pairs across 11 model organisms.
• Context-specific analysis: Supports analysis of RNA-seq data from specific biological contexts such as parthenogenetic and androgenetic embryonic stem cells.
• Imprinting detection: Identifies imprinted cis-NAT pairs exemplified by KCNQ1/KCNQ1OT1.
• Stimulus-responsive discovery: Detects cis-NAT pairs with inversely correlated expression upon TGFβ stimulation.
• Functional association: Identifies cis-NATs that can repress partner genes by introducing epigenetic changes at gene promoters.

Scientific Applications:

• Cataloging cis-NATs: Mapping and characterizing structural types of cis-NATs across species and datasets.
• Imprinting studies: Investigating imprinted antisense pairs such as KCNQ1/KCNQ1OT1 in embryonic contexts.
• Regulatory mechanism research: Studying cis-NAT-mediated repression and associated epigenetic modifications at promoters.
• Stimulus-response analysis: Identifying antisense pairs with inverse expression dynamics in response to TGFβ stimulation.
• Comparative genomics: Comparative identification of cis-NAT pairs across 11 model organisms using genome databases.

Methodology:

Implemented in Python and uses RNA-seq data together with genome annotations and genome databases to identify cis-NAT pairs.