BamSlam

This script was written for long-read Oxford Nanopore Technologies direct RNA/cDNA sequencing data. It uses BAM files produced after mapping with minimap2 to the reference transcriptome. It will output a summary file and plots from your aligned reads. This script was used in: https://doi.org/10.1093/nar/gkab1129.

Inputs:

To obtain a BAM file align your FASTQ/FASTA files to the transcriptome with minimap2.

minimap2 -ax map-ont --sam-hit-only transcriptome.fasta reads.fastq | samtools view -bh > aligned_reads.bam

If minimap2 is not run with --sam-hit-only you should remove unmapped reads prior to running BamSlam to avoid slowing it down. You can also input a BAM file output from NanoCount: https://github.com/a-slide/NanoCount.

Requirements:

R (tested with v4.2.2) R packages:

• GenomicAlignments (Bioconductor)
• dplyr
• tidyr
• tibble
• data.table
• ggplot2
• viridis
• hexbin

Usage:

Download/copy the Rscript from this repository and run it from the terminal as follows:

Rscript BamSlam.R [DATA_TYPE] [BAM_FILE] [OUT_PREFIX]
Rscript BamSlam.R rna undiff1_5Y.bam undiff1

DATA_TYPE, enter either: cdna, rna
BAM_FILE, a BAM file of alignments to the transcriptome
OUT_PREF, output file prefix

The script takes approximately 5 minutes per million reads.

Outputs:

Data

• A summary CSV file of your alignments.
• A CSV file of all input alignments (primary and secondary) for downstream analysis.
• A CSV file of each unique transcript identified in the data with its corresponding median read coverage.

Plots

• Histogram of read coverages (full-length reads cutoff/dashed line = 0.95):

  

• Histogram of known transcript lengths (per distinct transcript in the data):

  

• Histogram of known transcript lengths (per read):

  

• Histogram density plot of known transcript length vs coverage fractions:

  

• Bar chart of the secondary alignments:

  

• Density plot of the read accuracies:

  

Details of summary file metrics

• Total number of reads
• Number of reads representing full-length transcripts (reads with coverage fractions > 0.95)
• Percentage of reads representing full-length transcripts
• Median read coverage fraction (primary alignments)
• Median alignment length (primary alignments)
• Median accuracy (primary alignments) (calculated from CIGAR strings as: (nbrM+nbrI+nbrD-NM)/(nbrM+nbrI+nbrD))
• Number of reads with no secondary alignments
• Percentage of reads with no secondary alignments
• Total number of distinct transcripts identified in the data
• Median coverage fraction of all unique transcripts (median value of the median read coverages per transcript)
• Median length of all unique transcripts identified (median length in nt of the annotated length of transcripts identified)