Dataset Information

RLSuite: An Integrative R-Loop Bioinformatics Framework.

ABSTRACT: We recently described the development of a database of 810 R-loop mapping datasets and used this data to conduct a meta-analysis of R-loops. R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids and we were able to verify that 30% of expressed genes have an associated R-loop in a location conserved manner.. Moreover, intergenic R-loops map to enhancers, super enhancers and with TAD domain boundaries. This work demonstrated that R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, however 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 in a rapidly expanding aspect of epigenetic control that is still poorly understood.

SUBMITTER: Miller HE

PROVIDER: S-EPMC10827345 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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RLSuite: An Integrative R-Loop Bioinformatics Framework.

Miller H E HE Montemayor D D Levy S S Sharma K K Frost B B Bishop A J R AJR

Journal of bioinformatics and systems biology : Open access 20231221 4

We recently described the development of a database of 810 R-loop mapping datasets and used this data to conduct a meta-analysis of R-loops. R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids and we were able to verify that 30% of expressed genes have an associated R-loop in a location conserved manner.. Moreover, intergenic R-loops map to enhancers, super enhancers and with TAD domain boundaries. This work demonstrated that R-loop mapping via high-throughput sequencin ...[more]

PMID: 38292828

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Project description:BackgroundWe present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development.DescriptionThe Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations.ConclusionThe Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data models, enforcing the relationships between data types. Second, integration is achieved through a combination of APIs, ontology, and tools. The Atlas software is freely available under the GNU General Public License at: http://bioinformatics.ubc.ca/atlas/

Dataset Information

RLSuite: An Integrative R-Loop Bioinformatics Framework.

Publications

RLSuite: An Integrative R-Loop Bioinformatics Framework.

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