RLBase

RLBase provides exploration and analysis of R-loop mapping datasets to identify consensus R-loop regions, assess dataset quality, and support studies of R-loops (RNA–DNA hybrids) in gene regulation and genome stability.

Key Features:

• Reprocessed dataset collection: A curated collection comprising 693 reprocessed R-loop mapping samples and hundreds of additional public R-loop mapping datasets, standardized for comparative analysis.
• Consensus R-loop region analysis: Identification and examination of consensus R-loop sites where R-loops are most frequently formed.
• Quality assessment and reporting: Computational quality assessment of R-loop mapping datasets with generation of detailed HTML quality reports.
• Processed data provision: Provision of processed, standardized data derived from the reprocessed 693 R-loop mapping samples for downstream analyses.
• Support for R-loop mapping techniques: Support for analysis of data from R-loop mapping methodologies including DNA:RNA immunoprecipitation (DRIP) sequencing.
• RLSuite integration: Integration with the RLSuite software collection (MIT license) providing a computational framework for R-loop bioinformatics from data processing to advanced analysis.
• Novel computational methods: Implementation of novel computational methodologies developed for R-loop analysis and interpretation.

Scientific Applications:

• Comparative and meta-analysis: Comparative studies and meta-analyses across experimental conditions and species using standardized R-loop mapping datasets.
• Consensus site characterization: Characterization of consensus R-loop regions to investigate potential regulatory roles and associations with genomic instability.
• Data quality benchmarking: Benchmarking and validation of R-loop mapping experiments and datasets via systematic quality assessment.
• Downstream genomic analyses: Use of processed R-loop mapping data to study gene regulation and genome stability at the sequence and regional level.

Methodology:

Comprehensive reprocessing of 693 R-loop mapping samples, identification of consensus R-loop regions, computational quality assessment with HTML report generation, and implementation within the RLSuite computational framework.