ETCHING

Version 1.4.2

Ultrafast prediction of somatic structural variations by filtering out reads matched to pan-genome k-mer sets

Efficient deTection of CHromosomal rearrangements and fusIoN Genes

ETCHING takes about 3 hours for WGS data with 30X normal and 50X tumor on 30 threads on DELL 930 server. You can also find codes, k-mer set, and DEMO files on our website.

http://big.hanyang.ac.kr/ETCHING/download.html

We recommend using the new version of the pan-genome k-mer set, PGK2. You can download it from our website.

Using PGK2, ETCHING can detect somatic SVs from tumor sequencing data without matched-normal in comparable performance with other cutting-edge SV detection tools.

Recent changes

v1.4.2

• Requirement changed
  • g++ >=7 (>=7.3 recommended)
  • cmake >=3.14
  • samtools >=1.13 (using htslib >=1.13)
• BAM mode accelerated
• CRAM support
• Minor bugs fixed

See CHANGE.md for older updates.

Revised workflow in v1.4.2

ETCHING was a bit slow in removing remaining germline SVs after scoring SVs. We solved this issue by increasing Samtools requirement to >=1.13 for collecting the paired-end reads relevant to the remaining artifacts. As we updated the step, we accelerated the workflow for BAM input along with CRAM support.

Requirements

System

• 64-bit LINUX with >=32GB RAM (at least >=16GB).
• Tested on Fedora workstation, Centos, and Ubuntu

Software

• Required to compile

  • gcc, g++ (>=7), make, cmake (>=3.14), wget

    Note: g++ >=7.3 recommended

• Required to run

  • BWA, samtools (>=1.13) using htslib (>=1.13)

Guide to ETCHING

We prepared a simple guide for CentOS/Fedora or Ubuntu/Debian/Mint users. You can skip this step if all requirements were installed.

Note: We tested this guide on Fedora32/33/34, CentOS7/8, Ubuntu16.04/18.04/20.04/22.04, Mint19/20, Debian11, and MX Linux.

1. Requirements

• CentOS/Fedora (or other Red Hat-based Linux distros)

# Required programs 
sudo yum install -y gcc gcc-c++ make cmake bwa samtools wget

• Ubuntu/Debian/Mint (or other Debian-based distros)

## Required programs 
sudo apt install -y gcc g++ make cmake bwa samtools wget

2. Installation

Once the requirements were solved, you can install ETCHING as follows.

# Download ETCHING
git clone --depth=1 https://github.com/ETCHING-team/ETCHING.git

# Move to /path/to/ETCHING
cd ETCHING

# Installation (g++ >=7 and cmake >=3.14 required)
# g++ >=7.3 recommended
make

# Set your environment for ETCHING
echo "export PATH=$PATH:/path/to/ETCHING/bin" >> ~/.bashrc
exec $SHELL

3. DEMO

# Change directory
cd /wherever/you/want/

# Download and decompress DEMO
wget http://big.hanyang.ac.kr/ETCHING/tiny_demo.tar.gz
# or you can find a demo file from our website
# http://big.hanyang.ac.kr/ETCHING/

# Decompress
tar zxvf tiny_demo.tar.gz
cd tiny_demo

# Run demo
etching -1 tumor_1.fq -2 tumor_2.fq -1c normal_1.fq -2c normal_2.fq -g Chr22.fa -f demo_PGK

You can run with bam or cram.

# or
etching -b tumor.sorted.cram -bc normal.sorted.cram -g Chr22.fa -f demo_PGK -p cram_mode_output

Pan-Genome k-mer set

The Pan-Genome k-mer(PGK) set is used to build PGK filter. Since we updated the PGK to PGK2, you can also download the PGK2 as below. If you have no matched-normal data, you are highly recommended to use the PGK2 instead of PGK to call somatic SVs.

# Move to etching directory
mkdir -p /path/to
cd /path/to

# Download
wget http://big.hanyang.ac.kr/ETCHING/PGK2.tar.gz

# or you can download it from Amazon S3
# wget https://biglabhyu.s3.amazonaws.com/ETCHING/PGK.tar.gz

# Decompress
tar zxvf PGK2.tar.gz
cd PGK2

# Then, you must see PGK2.kmc_pre and PGK2_kmc_suf 
ls /path/to/PGK2/*
/path/to/PGK2/PGK2.kmc_pre
/path/to/PGK2/PGK2.kmc_suf

To use PGK2 (recommended), add -f /path/to/PGK2/PGK2 option. For instance,

etching -1 tumor_1.fq -2 tumor_2.fq -g reference.fa -f /path/to/PGK2/PGK2

Then, etching script will find /path/to/PGK2/PGK2.kmc_pre and /path/to/PGK2/PGK2.kmc_suf.

Alternatively, you can make your custom k-mer set using make_pgk included in ETCHING package.

In the absence of matched-normal sample, we recommend using >=200 genomes to build your custom k-mer set excluding rare k-mers (<1% allele frequency). If you have 600 genomes, use -m 6 option, which is the minimum frequency of k-mer to be included in your k-mer set.

make_pgk -i 600_genome.list -m 6 -o my_pgk

Here, 600_genome.list is a file of FASTA files of genomes in the format of

/path/to/references/hg19.fasta
/path/to/references/GRCh38.fa
/path/to/references/NA12878.chr1.fna
/path/to/references/NA12878.chr2.fna
.
.
.
/path/to/assemblies/someones_genome.scaffold_1.fa
/path/to/assemblies/someones_genome.scaffold_2.fa

ETCHING on a ship (docker)

Requirement

docker

Download docker image

# Download ETCHING docker image
wget http://big.hanyang.ac.kr/ETCHING/ETCHING_v1.4.2.docker.tar

# Load the image
docker load -i ETCHING_v1.4.2.docker.tar

# Check the image
docker images

You can see like below (numbers can be different)

REPOSITORY	TAG	IMAGE ID	CREATED	SIZE
etching	1.4.2	967b3d7fb6f7	40 hours ago	1.5GB

Demo for docker user

Download demo data and run ETCHING with docker

wget http://big.hanyang.ac.kr/ETCHING/tiny_demo.tar.gz

tar zxvf tiny_demo.tar.gz

docker run -i -t --rm -v $PWD/tiny_demo/:/work/ etching:1.4.2 etching \
-1 tumor_1.fq -2 tumor_2.fq -1c normal_1.fq -2c normal_2.fq -g Chr22.fa -f demo_PGK -t 8

Here, etching:1.4.2 is REPOSITORY:TAG of ETCHING docker image.

To use your data, replace $PWD/tiny_demo with /your/data/path/. The directory also should contain k-mer set (PGK2).

Note: Keep /work/ in the above command line, since it's the default working directory.

ETCHING on Amazon Web Service

An older version of ETCHING (v1.4.0) is also available on Amazon Web Service (AMI ID: ami-07c7a7d8934784df9; Region: us-east-1 (Northern Virginia)).

# Lunch EC2 instance from ETCHING AMI
# And connect to EC2 instance
ssh -i Your_Key ubuntu@Your_Instance_Address

# Decompress
tar zxvf ~/resources/DEMO.tar.gz 

# Run demo
cd ~/resources/DEMO
bash example.sh

Related programs

Filtration tool for PacBio long-reads

https://github.com/ETCHING-team/LR_Filter

Benchmarking tool

https://github.com/ETCHING-team/etching_bench

Citing ETCHING

Sohn, Ji., Choi, MH., Yi, D. et al. Ultrafast prediction of somatic structural variations by filtering out reads matched to pan-genome k-mer sets. Nat. Biomed. Eng (2022). https://doi.org/10.1038/s41551-022-00980-5

Contributors

Jang-il Sohn, Min-Hak Choi, Dohun Yi, A. Vipin Menon, and Jin-Wu Nam

Bioinformatics and Genomics Lab., Dept. of Life Science, Hanyang University, Seoul 04763, Korea

Contact

Principal investigator

Jin-Wu Nam ([email protected])

If he is unavailable, please email one of

Jang-il Sohn ([email protected])

Min-Hak Choi ([email protected])

Dohun Yi ([email protected])

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