ATACAmp

Searching for co-amplified regions on the genome from ATAC-seq data

Table of Contents

• Backgroud
• Install
• Usage
• Example
• Output
• License

  Backgroud

  High expression of oncogenes in cancer cells is an important cause of rapid tumor progression and drug resistance. Recent tumor genome research reveals that oncogenes and regulatory elements can be amplified as extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneous staining regions (HSRs). These genome-level variants lead to over-expression of corresponding oncogenes, resulting in poor prognosis. Most existing methods detects ecDNA by utilizing whole genome sequencing data. They usually detect many false positive regions, due to the interference of chromosomal DNA.Here, we describe an algorithm called ‘ATACAmp’ that can find out ecDNA/HSR in tumor genomes by utilizing ATAC-seq data. High chromatin accessibility is one of the characteristics of ecDNA, which makes ATAC-seq naturally enriched ecDNA and reduces the interference of chromosomal DNA. We validated the algorithm using ATAC-seq data from three cell lines that have experimentally determined ecDNA regions. ATACAmp successfully identified the majority of validated ecDNA regions. We also used AmpliconArchitect, the widely used ecDNA detecting tool, to detect ecDNA regions based on WGS data of the same cell line. Results shows that ATACAmp exhibited higher accuracy than AmpliconArchitect. In addition, ATACAmp also support the analysis of single-cell ATAC-seq data, which links ecDNA to a specific cell. This can facilitate the study of ecDNA at single cell level.

  Install

  conda create -n atacamp
  conda activate atacamp
  python3 -m pip install pysam interval argparse networkx matplotlib psutil

  Github source code:

  git clone https://github.com/chsmiss/ATAC-amp.git

  Usage

  usage: python3 AtacAmp.py [-h] [--bam BAM] [--name NAME] [--isize_value ISIZE] [--interval_size INTERVAL] [--mapq MAQP] [--mode {0,1,2}] [--discbk DISCBK] [--type LIB] [--gtf GTF] [--threads THREADS]

optional arguments: -h, --help show this help message and exit
--bam BAM input the bam file
--name NAME, -n NAME prefix of output files
--isize_value ISIZE, -i ISIZE
judge a pair of reads whether is discordant
--interval_size INTERVAL, -s INTERVAL
size of interval when compute breakpoint nearby coverage
--mapq MAQP, -q MAQP
reads maqp threshold
--mode {0,1,2}, -m {0,1,2}
choose the analysis mode,0/1/2
--discbk DISCBK, -d DISCBK
if you choose mode 1,you need to input discbk file
--type LIB
choose library:sc/bulk
--gtf GTF gtf file
--threads THREADS threads

Example

python ../../../10.other/AtacAmp/AtacAmp.py --bam ../../10.atac_seq_data/SRR8236755.q20.sorted.bam -i 1000 -s 1000 --gtf ../../../01.human_reference/hg38.refGene.gtf --threads 12 -n GBM39_1 -m 0 --type bulk

Output

example output(.result)

example output figure(png format):The links of highest scoring co-amplified regions in the colo320DM cell line.

MYC expression heterogeneity in the colo320dm cell line

License

Citation

Cheng, H., Ma, W., Wang, K. et al. ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data. BMC Genomics 24, 678 (2023). https://doi.org/10.1186/s12864-023-09792-6