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bambamR

End-to-end RNA-seq processing from FASTQ to publication-ready plots.

bambamR provides a streamlined toolkit for RNA-seq analysis covering FASTQ/BAM import, quality control, alignment, read counting, normalization, differential expression analysis, and publication-ready visualizations including onco plots, volcano plots, heatmaps, and PCA.

Two Operating Modes

bambamR never breaks if Bioconductor is missing. Every function that needs an optional package checks first and either falls back gracefully or gives a clear install instruction.

Feature Minimal (CRAN-only) Full (+ Bioconductor)
CPM / TPM normalization Yes Yes
TMM / RLE normalization – Yes (edgeR / DESeq2)
Differential expression – Yes (DESeq2, edgeR, limma-voom)
PCA, volcano, heatmap, MA Yes Yes
Onco plots Yes Yes
FASTQ / BAM import Base-R fallback ShortRead / Rsamtools
Alignment wrappers STAR / HISAT2 / minimap2 STAR / HISAT2 / minimap2
Read counting featureCounts CLI GenomicAlignments
Shiny interactive app Yes Yes

Installation 

# Install from CRAN (when available)
install.packages("bambamR")

# Or install the development version from GitHub
# install.packages("pak")
pak::pak("r-heller/bambamR")

Optionally install Bioconductor packages for full-mode features:

if (!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")

BiocManager::install(c(
  "DESeq2", "edgeR", "limma",
  "ShortRead", "Rsamtools",
  "GenomicAlignments", "GenomicRanges"
))

Bundled Example Datasets

bambamR ships with ready-to-use example data so you can explore every feature without downloading external files or installing Bioconductor:

Function Contents
bb_example_counts() RNA-seq count matrix (200 genes x 10 samples) with sample metadata (condition, batch, sex, age). First 20 genes have simulated DE between control and treatment.
bb_example_mutations() Mutation calls (300 mutations, 50 samples, 20 cancer genes) plus clinical annotations (Stage, Gender, Smoking) for oncoplot demos.
bb_example_de() Pre-computed DE results (500 genes) with log2FC, p-values, and base means – works without Bioconductor.

A small FASTQ file (example_reads.fastq, 100 reads) and gene-length table are also included under inst/extdata/.

Quick Start 

library(bambamR)

# ── 1. Load example data ───────────────────────────────────
ex <- bb_example_counts()
str(ex)
# List of 2
#  $ counts  : int [1:200, 1:10] ...
#  $ metadata: data.frame (10 obs. of 4 variables)

# ── 2. Normalize ───────────────────────────────────────────
cpm <- bb_normalize(ex$counts, method = "cpm")

# ── 3. PCA ─────────────────────────────────────────────────
bb_pca(cpm, ex$metadata, color_by = "condition")

# ── 4. Heatmap of top variable genes ──────────────────────
bb_heatmap(cpm, n_genes = 30)

# ── 5. Differential expression (requires DESeq2) ──────────
de <- bb_deseq2(ex$counts, ex$metadata)
bb_volcano(de)
bb_ma_plot(de)

# ... or use the pre-computed DE results (no Bioconductor!) ─
de <- bb_example_de()
bb_volcano(de, n_label = 10)
bb_ma_plot(de)

# ── 6. Heatmap of top DE genes ────────────────────────────
bb_heatmap(cpm, de_result = de, n_genes = 30)

# ── 7. Oncoplot ───────────────────────────────────────────
mut <- bb_example_mutations()
bb_oncoplot(mut$mutations, n_genes = 15, annotation_df = mut$clinical)

# ── 8. Read a FASTQ file ─────────────────────────────────
fq <- system.file("extdata", "example_reads.fastq", package = "bambamR")
reads <- bb_read_fastq(fq, n = 5)
reads

# ── 9. Export ─────────────────────────────────────────────
bb_export_csv(de, "de_results.csv")

Full Pipeline (FASTQ to Results)

When you have FASTQ files, a genome index, and a GTF annotation, run the entire pipeline in one call:

result <- bb_pipeline(
  fastq_dir    = "raw_reads/",
  genome_index = "STAR_index/",
  annotation   = "genes.gtf",
  sample_info  = sample_metadata,
  aligner      = "STAR",
  de_method    = "DESeq2",
  threads      = 8
)

# The result object bundles everything
result$counts       # count matrix
result$de_results   # DE data.frame
result$plots$pca    # ggplot object
result$plots$volcano

You can also enter the pipeline at any stage:

# From BAM files (skip alignment)
result <- bb_pipeline(bam_dir = "aligned/", annotation = "genes.gtf",
                      sample_info = meta)

# From a count matrix (skip alignment + counting)
result <- bb_pipeline(count_matrix = my_counts, sample_info = meta)

Interactive Shiny App

Upload your count matrix and metadata through the browser, configure normalization and DE parameters, run the analysis, and explore interactive plots. Export results as CSV, RDS, or publication-quality PDF.

Function Reference

All exported functions use the bb_ prefix and return standard R objects (data.frames, matrices, ggplot objects).

Import

Quality Control

Alignment & Counting

  • bb_align() – align reads with STAR, HISAT2, or minimap2
  • bb_count_reads() – count reads per gene (GenomicAlignments or featureCounts)

Normalization

Differential Expression

Visualization (all return ggplot objects)

Pipeline & Export

Example Data

Shiny App

Authors

  • Raban Heller (maintainer) - SingleCellLab, BWK Ulm
  • Hanno Witte - SingleCellLab, BWK Ulm
  • Konrad Steinestel - SingleCellLab, BWK Ulm

Citation

If you use bambamR in your research, please cite:

Heller R, Witte H, Steinestel K (2026). bambamR: End-to-End RNA-Seq
Processing from FASTQ to Publication-Ready Plots. R package version 0.1.0.
https://github.com/r-heller/bambamR

License

MIT