A Scalable GPU System for Pairwise Whole Genome Alignments based on LASTZ's seed-filter-extend paradigm.

Table of Contents

• Overview
• Dependencies
• How to run SegAlign
  • Running a test
• How to run SegAlign repeat masker
  • Running a test
• Running Docker Image
  • Running segalign
  • Running segalign_repeat_masker
• Citing SegAlign

Overview

The system has been tested on all the AWS G3 and P3 GPU instances with AMI Ubuntu Server 18.04 LTS (HVM), SSD Volume Type (ami-0fc20dd1da406780b (64-bit x86))

• Clone SegAlign repository (https://github.com/gsneha26/SegAlign)

git clone https://github.com/gsneha26/SegAlign.git
export PROJECT_DIR=$PWD/SegAlign

Dependencies

The following dependencies are required by SegAlign:

• NVIDIA CUDA 10.2 toolkit
• CMake 3.8
• Intel TBB library
• libboost-all-dev
• parallel
• zlib
• LASTZ 1.04.15
• faToTwoBit, twoBitToFa (from kentUtils)

The dependencies can be installed with the given script as follows, which might take a while (only installs the dependencies not present already). This script requires sudo to install most packages at the system level. Using the -c option skips CUDA installation [the CUDA toolkit binaries should be in $PATH for SegAlign].

cd $PROJECT_DIR
./scripts/installUbuntu.sh

How to run SegAlign

• Run SegAlign

run_segalign target query [options]

• For a list of options

run_segalign --help

Running a test

cd $PROJECT_DIR
mkdir test
cd test
wget https://hgdownload.soe.ucsc.edu/goldenPath/ce11/bigZips/ce11.2bit
wget https://hgdownload-test.gi.ucsc.edu/goldenPath/cb4/bigZips/cb4.2bit 
twoBitToFa ce11.2bit ce11.fa
twoBitToFa cb4.2bit cb4.fa
run_segalign ce11.fa cb4.fa --output=ce11.cb4.maf

How to run SegAlign repeat masker

• Run SegAlign repeat masker

run_segalign_repeat_masker sequence [options]

• For a list of options

run_segalign_repeat_masker --help

Running a test

cd $PROJECT_DIR
mkdir test_rm
cd test_rm
wget https://hgdownload.soe.ucsc.edu/goldenPath/ce11/bigZips/ce11.2bit
twoBitToFa ce11.2bit ce11.fa
run_segalign_repeat_masker ce11.fa --output=ce11.seg

Running Docker Image

Running segalign

wget https://hgdownload.soe.ucsc.edu/goldenPath/ce11/bigZips/ce11.2bit
wget https://hgdownload-test.gi.ucsc.edu/goldenPath/cb4/bigZips/cb4.2bit 
sudo docker run -v $(pwd):/data -it gsneha/segalign:v0.1.2 \
                           twoBitToFa \
                           /data/ce11.2bit \
                           /data/ce11.fa
sudo docker run -v $(pwd):/data -it gsneha/segalign:v0.1.2 \
                           twoBitToFa \
                           /data/cb4.2bit \
                           /data/cb4.fa
sudo docker run --ipc=host --gpus all -v $(pwd):/data -it gsneha/segalign:v0.1.2 \
                           run_segalign \
                           /data/ce11.fa \
                           /data/cb4.fa \
                           --output=/data/ce11.cb4.maf

Running segalign_repeat_masker

wget https://hgdownload.soe.ucsc.edu/goldenPath/ce11/bigZips/ce11.2bit
sudo docker run -v $(pwd):/data -it gsneha/segalign:v0.1.2 \
                           twoBitToFa \
                           /data/ce11.2bit \
                           /data/ce11.fa
sudo docker run --ipc=host --gpus all -v $(pwd):/data -it gsneha/segalign:v0.1.2 \
                           run_segalign_repeat_masker \
                           /data/ce11.fa \
                           --output=/data/ce11.seg

Citing SegAlign

S. Goenka, Y. Turakhia, B. Paten and M. Horowitz, "SegAlign: A Scalable GPU-Based Whole Genome Aligner," in 2020 SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Atlanta, GA, US, 2020 pp. 540-552. doi: 10.1109/SC41405.2020.00043