bioconductor-genomicalignments

name	bioconductor-genomicalignments
description	Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working wit
when_to_use	Use when: Counting aligned reads overlapping genomic features (e.g., exons, genes) for differential expression analysis using summarizeOverlaps.; Loading and manipulating paired-end alignments from BAM files using readGAlignmentPairs.; Analyzing splice junctions, insertions, and deletions in RNA-seq reads using the njunc, cigar, and cigarOpTable functions.; Filtering alignments based on mapping quality, dup. Not for: For ultra-fast, memory-efficient read counting on massive datasets, use Rsubread::featureCounts because it is written in C and optimized for speed.; For low-level BAM file indexing or header manipulation without loading alignments, use Rsamtools beca
user-invocable	false

GenomicAlignments

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 1.48.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Depends: BiocGenerics, S4Vectors, IRanges, Seqinfo, GenomicRanges, SummarizedExperiment, Biostrings, Rsamtools
• Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel, cigarillo
• Install: BiocManager::install("GenomicAlignments")

When to Use

• Counting aligned reads overlapping genomic features (e.g., exons, genes) for differential expression analysis using summarizeOverlaps.
• Loading and manipulating paired-end alignments from BAM files using readGAlignmentPairs.
• Analyzing splice junctions, insertions, and deletions in RNA-seq reads using the njunc, cigar, and cigarOpTable functions.
• Filtering alignments based on mapping quality, duplicates, or specific genomic regions using ScanBamParam and scanBamFlag.

When NOT to Use

• For ultra-fast, memory-efficient read counting on massive datasets, use Rsubread::featureCounts because it is written in C and optimized for speed.
• For low-level BAM file indexing or header manipulation without loading alignments, use Rsamtools because it provides direct wrappers to samtools functionality.

Data Requirements

• Input Format: Sorted and indexed BAM files.
• Features: Genomic features provided as GRanges or GRangesList objects for overlap counting.
• Structure: Alignments are loaded into GAlignments (single-end) or GAlignmentPairs (paired-end) objects.

Key Parameters

• use.names (TRUE): Logical indicating whether to load query names (QNAME) from the BAM file in readGAlignments.
• param: A ScanBamParam object specifying filtering criteria (e.g., flags, genomic regions) when reading alignments.
• mode ("Union"): The overlap resolution mode for summarizeOverlaps (e.g., "Union", "IntersectionStrict", "IntersectionNotEmpty").
• isDuplicate (FALSE): Flag in scanBamFlag to filter out PCR or optical duplicates.
• isNotPassingQualityControls (FALSE): Flag in scanBamFlag to filter out low-quality reads.
• isFirstMateRead (TRUE): Flag in scanBamFlag to specifically load only the first segment of paired-end reads.

Best Practices

• Use ScanBamParam with scanBamFlag to filter out PCR duplicates and reads failing quality controls before loading alignments into memory.
• When working with paired-end data, use readGAlignmentPairs and filter for proper pairs using isProperPair to ensure high-quality alignments.
• Use summarizeOverlaps with a GRangesList (e.g., exons grouped by transcript or gene) to correctly count reads spanning multiple exons of the same feature.

Common Pitfalls

• Out of memory errors: Running out of memory when loading entire BAM files at once. Fix: Use ScanBamParam to restrict loading to specific genomic regions (using which) or specific fields (using what).
• Incorrect paired-end counting: Counting paired-end reads as independent single-end reads. Fix: Ensure paired-end BAM files are loaded with readGAlignmentPairs or handled appropriately in counting functions.
• Assuming unique read names: Assuming read names are unique in the loaded GAlignments object, which fails when aligners report multiple alignments per read. Fix: Handle duplicated query names by checking duplicated(names(gal)).

Alternatives

• Rsubread: For extremely fast, multi-threaded read counting (featureCounts) in R.
• Rsamtools: For low-level BAM/SAM file access, indexing, and manipulation.
• GenomicRanges: For general genomic interval operations once alignments are converted to ranges.

Citations

• Lawrence, M., Huber, W., Pagès, H., Aboyoun, P., Carlson, M., Gentleman, R., ... & Carey, V. J. (2013). Software for computing and annotating genomic ranges. PLoS Computational Biology, 9(8), e1003118.

References

• Homepage: https://bioconductor.org/packages/GenomicAlignments
• Vignette: vignette_0_5516405c.txt

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