bioconductor-cellbarcode

---

name: bioconductor-cellbarcode description: The package CellBarcode performs Cellular DNA Barcode analysis. It can handle all kinds of DNA barcodes, as long as the barcode is within a single sequencing read and has a pattern that can be matched by a regular expression. \code{CellBarcode} can handle barcodes with flexible lengths, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing some amplicon data such as CRISPR gRNA screening, immune repertoire sequencing, and metagenome data. when_to_use: Use when: 10X_Barcode; UMI_Barcode; Quality control and preprocessing (CellBarcode). Not for: Requires R ≥ 4.1.0 and Bioconductor ≥ 3.16 user-invocable: false

CellBarcode

Workflows

Standard Workflow

Perform quality control, filter, extract, cure, and quantify lineage barcodes and UMIs from raw FASTQ files.

library(CellBarcode)

# 1. Load User Inputs
example_data <- system.file("extdata", "mef_test_data", package = "CellBarcode")
fq_files <- dir(example_data, "fastq.gz", full=TRUE)
sample_name <- paste0("sample_", seq_along(fq_files))

# 2. Quality Control
qc_noFilter <- bc_seq_qc(fq_files)
bc_plot_seqQc(qc_noFilter)

# 3. Filtering
fq_filter <- bc_seq_filter(fq_files, min_average_quality = 30, min_read_length = 60, sample_name = sample_name)

# 4. Extraction
pattern <- "([ACGT]{12})CTCGAGGTCATCGAAGTATC([ACGT]+)CCGTAGCAAGCTCGAGAGTAGACCTACT"
bc_obj <- bc_extract(fq_filter, pattern = pattern, pattern_type = c("UMI" = 1, "barcode" = 2), sample_name = sample_name)

# 5. Curing and Quantification
bc_sub <- bc_cure_umi(bc_obj, depth = 2)
bc_sub <- bc_cure_depth(bc_sub, depth = 2)

# 6. Export
df_counts <- bc_2df(bc_sub)
mat_counts <- bc_2matrix(bc_sub)

Inputs/Outputs: Takes raw FASTQ files and a regular expression pattern as inputs, and outputs a BarcodeObj containing cleaned barcode and UMI counts, exportable to a data frame (bc_2df) or matrix (bc_2matrix).

Scrnaseq Sam Lineage Barcoding

Extract lineage barcodes, cell barcodes, and UMIs from 10X Genomics single-cell RNA-seq SAM files.

library(CellBarcode)

# 1. Load User Inputs
sam_file <- system.file("extdata", "scRNASeq_10X.sam", package = "CellBarcode")

# 2. Extract Lineage Barcode
d <- bc_extract_sc_sam(
  sam = sam_file,
  pattern = "AGATCAG(.*)TGTGGTA",
  cell_barcode_tag = "CR",
  umi_tag = "UR"
)

Inputs/Outputs: Takes a SAM file derived from 10X Genomics CellRanger output and a regular expression pattern as inputs, and outputs a data frame containing cell_barcode, umi, barcode_seq, and count.

When to Use

• To perform cellular DNA barcode analysis and lineage tracing from raw FASTQ files using bc_extract.
• To extract lineage barcodes, cell barcodes, and UMIs from single-cell RNA-seq SAM/BAM files using bc_extract_sc_sam.
• To filter sequences by quality and length using bc_seq_filter and perform quality control visualization using bc_plot_seqQc.
• To perform error correction on barcodes and UMIs using bc_cure_umi and bc_cure_depth.

When NOT to Use

• For standard transcriptomic alignment or gene expression quantification (use Rsubread or Salmon instead).
• For single-cell RNA-seq clustering and cell-type annotation (use Seurat or scran instead).

Data Requirements

• Raw sequencing data in FASTQ format, or aligned single-cell RNA-seq data in SAM/BAM format (from CellRanger).
• A regular expression pattern defining the constant regions and the variable barcode/UMI regions (e.g., "([ACGT]{12})CTCGAGGTCATCGAAGTATC([ACGT]+)CCGTAGCAAGCTCGAGAGTAGACCTACT").

Key Parameters

• pattern: A regular expression matching the barcode structure, with brackets () capturing the target sequences.
• pattern_type: A named vector (e.g., c("UMI" = 1, "barcode" = 2)) mapping captured groups to sequence types.
• min_average_quality (30): Minimum average base quality across a read in bc_seq_filter.
• min_read_length (60): Minimum read length in bases in bc_seq_filter.
• depth (2): Minimum read/UMI depth threshold for filtering in bc_cure_umi and bc_cure_depth.
• cell_barcode_tag ("CR"): The SAM file tag for 10X cell barcodes in bc_extract_sc_sam.
• umi_tag ("UR"): The SAM file tag for 10X UMIs in bc_extract_sc_sam.

Best Practices

• Run bc_seq_qc and bc_plot_seqQc before extraction to identify constant and random regions of the sequencing reads.
• Filter low-quality reads using bc_seq_filter to reduce noise and false-positive barcodes.
• Apply bc_cure_umi followed by bc_cure_depth to perform sequence error correction and remove low-abundance barcode artifacts.

Common Pitfalls

• Incorrect regular expression pattern resulting in zero extracted barcodes. Fix: Verify the constant flanking sequences using bc_plot_seqQc base ratio plots.
• High memory usage with large SAM/BAM files. Fix: Pre-filter the BAM file to include only unmapped reads (e.g., using samtools view -f 4) before running bc_extract_sc_sam.

Alternatives

• ShortRead for general FASTQ manipulation and quality control without specialized barcode/UMI extraction.
• scuttle for single-cell preprocessing and QC without lineage barcode extraction.

Citations

• Sun W, Lyne AM (2024). CellBarcode: an R/Bioconductor package for cellular DNA barcode analysis. Nature Computational Science.

References

• Homepage: bioconductor.org/packages/cellbarcode
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/cellbarcode/inst/doc/CellBarcode.html