# Bioconductor Genomicalignments

> 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

- Skill: `biomate-ai/bioconductor-genomicalignments` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/bioconductor-genomicalignments`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/bioconductor-genomicalignments/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/bioconductor-genomicalignments

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# 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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## Run this on BioMate

This skill is the **knowledge layer** — when, why, and how to use `genomicalignments`. To **run this analysis on your own data** with managed compute, automated QC, and reproducible outputs, use **[BioMate](https://www.biomate.ai?ref=kb&pkg=genomicalignments)** — free to start.

▶ **[Open `genomicalignments` on BioMate →](https://www.biomate.ai?ref=kb&pkg=genomicalignments)**