Biology subjects

Montemayor, D.

Publications and source records attributed to Montemayor, D..

3 recordsLinked to original sources

bioRxiv2022

RLSuite: An integrative R-loop bioinformatics framework

R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids. While R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, the analysis and quality control of these data is a non-trivial task for which few bioinformatic tools exist. Herein we describe RLSuite, an integrative R-loop bioinformatics framework for pre-processing, quality control, and downstream analysis of R-loop mapping data. RLSuite enables users to compare their data to hundreds of public datasets and generate a user-friendly analysis report for sharing with non-bioinformatician colleagues. Taken together, RLSuite is a novel analysis framework that should greatly benefit the emerging R-loop bioinformatics community.

bioinformatics↗

bioRxiv2021

Quality-controlled R-loop meta-analysis reveals the characteristics of R-Loop consensus regions

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during transcription. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 693 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate method for R-loop data quality control, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called "R-loop regions" (RL regions). In the process, we revealed the stark divergence between S9.6 and dRNH-based R-loop mapping methods and identified biologically meaningful subtypes of both constitutive and variable R-loops. Taken together, this work provides a much-needed method to assess R-loop data quality and reveals intriguing aspects of R-loop biology.

bioinformatics↗

bioRxiv2021

Exploration and analysis of R-loop mapping data with RLBase

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during nascent transcription. In 2012, Ginno et al. introduced the first R-loop mapping method, DNA:RNA immunoprecipitation (DRIP) sequencing. Since that time, dozens of studies have implemented R-loop mapping and new high-resolution techniques have been developed. The resulting datasets have tremendous potential to reveal the causes and consequences of R-loops genome-wide. However, poor quality and variability between mapping approaches pose serious barriers to the meta-analysis of these data. In our recent work, we reprocessed 693 R-loop mapping samples, devising new quality methods, defining a set of high-confidence mapping samples, and then deriving R-loop regions, consensus sites of R-loop formation. This analysis yielded the largest R-loop data resource to date along with novel computational approaches for R-loop mapping analysis. Now, we introduce RLBase, an innovative web server which builds upon those data and software by providing users with the capability to (1) explore hundreds of public R-loop mapping datasets, (2) explore consensus R-loop regions, (3) analyze user-supplied datasets to generate an HTML quality report, and (4) download all the processed data for the 693 samples we previously reprocessed and standardized. In addition to RLBase, we also describe the other software which, along with RLBase, provides a computational framework for R-loop bioinformatics. RLBase, and the rest of these software (termed "RLSuite"), are provided freely under an MIT license and made publicly available: https://gccri.bishop-lab.uthscsa.edu/rlsuite/. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/rlbase/.

bioinformatics↗