BRGenomics
BRGenomics provides post-alignment processing and analysis of high-resolution genomics data in R/Bioconductor for quantification, normalization, and metagene analyses.
Key Features:
- Bioconductor integration: Leverages Bioconductor infrastructure and GenomicRanges for genomic range operations and compatibility with ecosystem data structures.
- Data importation and processing: Supports efficient import and processing of post-alignment sequencing data with optimization for parallel processing.
- Read counting and aggregation: Implements robust methods for read counting and aggregation to enable accurate quantification across experiments.
- Normalization techniques: Provides spike-in and batch normalization strategies to mitigate technical variability between samples.
- Resampling methods: Includes re-sampling approaches for robust metagene analyses and exploration of aggregate signal profiles.
- Data cleaning and modification: Offers functions to clean and modify sequencing and annotation data for high-quality downstream analysis.
- Efficient storage and quantification: Supports multiple quantification and storage strategies including whole reads, quantitative single-base data, and run-length encoded coverage information.
Scientific Applications:
- ATAC-seq: Analysis of chromatin accessibility datasets such as ATAC-seq for region-level and single-base quantification.
- ChIP-seq/ChIP-exo: Analysis of protein–DNA interaction datasets including ChIP-seq and ChIP-exo for binding and footprinting studies.
- PRO-seq/PRO-cap: Analysis of nascent transcription datasets such as PRO-seq and PRO-cap for transcriptional initiation and elongation profiling.
- RNA-seq: Analysis of gene expression datasets from RNA-seq for quantification and comparative studies.
Methodology:
Implements Bioconductor-based genomic range operations via GenomicRanges, parallelized data import and processing, read counting and aggregation algorithms, spike-in and batch normalization methods, re-sampling for metagene analyses, and support for run-length encoded coverage representations.
Topics
Details
- License:
- Artistic-2.0
- Cost:
- Free of charge
- Tool Type:
- library
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- R
- Added:
- 1/2/2024
- Last Updated:
- 11/24/2024
Operations
Publications
DeBerardine M. BRGenomics for analyzing high-resolution genomics data in R. Bioinformatics. 2023;39(6). doi:10.1093/bioinformatics/btad331. PMID:37208173. PMCID:PMC10278936.