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The next version of bwa-mem (WIP; not recommended for production uses at the moment)
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README.md

BWA-Mich

BWA-Mich builds upon BWA-MEM2 and includes performance improvements to the seeding and mate-rescue steps. It uses the Enumerated Radix Tree (ERT) index which is ~60 GB for the human genome. BWA-Mich produces identical results as BWA-MEM2 and is 1.2-1.4x faster.

Getting Started

# Compile from source
git clone https://github.com/arun-sub/bwa-mem2.git ert
cd ert

# To find out vectorization features supported in your machine
cat /proc/cpuinfo

# If AVX512BW (512-bit SIMD) is supported
make clean
make -j<num_threads> arch=avx512

# If AVX2 (256-bit SIMD) is supported
make clean
make -j<num_threads> arch=avx2

# If SSE4.1 (128-bit SIMD) is supported (default)
make -j<num_threads>

# Build index (Takes ~2 hr for human genome with 56 threads. 1 hr for BWT, 1 hr for ERT)
./bwa-mem2 index -a ert -t <num_threads> -p <index prefix> <input.fasta>

# Perform alignment
./bwa-mem2 mem -Y -K 100000000 -t <num_threads> -Z <index prefix> <input_1.fastq> <input_2.fastq> -o <output_ert.sam>

# To verify output with BWA-MEM
git clone https://github.com/lh3/bwa.git
cd bwa
make

# Perform alignment
./bwa mem -Y -K 100000000 -t <num_threads> <index prefix> <input_1.fastq> <input_2.fastq> -o <output_mem.sam>

# Compare output SAM files
diff <output_mem.sam> <output_ert.sam>

# To diff large SAM files use https://github.com/unhammer/diff-large-files

Notes

  • BWA-Mich has been tested for read lengths up to 251 bp. For larger read lengths, please update READ_LEN (default = 151) in src/macro.h and rebuild the index. Note that variable read-lengths in the input data are supported, so READ_LEN can be conservatively set higher, and the index need not be rebuilt every time read length is changed.
  • BWA-Mich requires atleast 70 GB RAM. For WGS runs on human genome (>32 threads), it is recommended to have 128-192 GB RAM.

Performance Results

  • Evaluation performed on 25 publicly available whole human genome paired-end datasets from Illumina Platinum Genomes, Illumina BaseSpaceHub and 1000 Genomes Project-Phase3.
  • Human reference genome was downloaded from here.


Citation

If you use BWA-Mich, please cite the following paper:

Arun Subramaniyan, Jack Wadden, Kush Goliya, Nathan Ozog, Xiao Wu, Satish Narayanasamy, David Blaauw, Reetuparna Das. Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees. https://doi.org/10.1101/2020.03.23.003897

BWA-MEM2 (old)

Important Information

Index structure has changed since commit 6743183. Rebuild the Index if you are using a later commit.

Added MC flag in the output sam file in commit a591e22. Output should match original bwa-mem version 0.7.17.

Getting Started

# Use precompiled binaries (recommended)
curl -L https://github.com/bwa-mem2/bwa-mem2/releases/download/v2.0pre2/bwa-mem2-2.0pre2_x64-linux.tar.bz2 \
  | tar jxf -
bwa-mem2-2.0pre2_x64-linux/bwa-mem2 index ref.fa
bwa-mem2-2.0pre2_x64-linux/bwa-mem2 mem ref.fa read1.fq read2.fq > out.sam

# Compile from source (not recommended for general users)
git clone https://github.com/bwa-mem2/bwa-mem2
cd bwa-mem2
make
./bwa-mem2

Introduction

Bwa-mem2 is the next version of the bwa-mem algorithm in bwa. It produces alignment identical to bwa and is ~80% faster.

The original bwa was developed by Heng Li (@lh3). Performance enhancement in bwa-mem2 was primarily done by Vasimuddin Md (@yuk12) and Sanchit Misra (@sanchit-misra) from Parallel Computing Lab, Intel. Bwa-mem2 is distributed under the MIT license.

Installation

For general users, it is recommended to use the precompiled binaries from the release page. These binaries were compiled with the Intel compiler and runs faster than gcc-compiled binaries. The precompiled binaries also indirectly support CPU dispatch. The bwa-mem2 binary can automatically choose the most efficient implementation based on the SIMD instruction set available on the running machine. Precompiled binaries were generated on a CentOS6 machine using the following command line:

make CXX=icpc multi

Usage

The usage is exactly same as the original BWA MEM tool. Here is a brief synopsys. Run ./bwa-mem2 for available commands.

# Indexing the reference sequence (Requires 28N GB memory where N is the size of the reference sequence).
./bwa-mem2 index [-p prefix] <in.fasta>
Where 
<in.fasta> is the path to reference sequence fasta file and 
<prefix> is the prefix of the names of the files that store the resultant index. Default is in.fasta.

# Mapping 
# Run "./bwa-mem2 mem" to get all options
./bwa-mem2 mem -t <num_threads> <prefix> <reads.fq/fa> > out.sam
Where <prefix> is the prefix specified when creating the index or the path to the reference fasta file in case no prefix was provided.

Performance

Datasets:

Alias Dataset source No. of reads Read length
D1 Broad Institute 2 x 2.5M bp 151bp
D2 SRA: SRR7733443 2 x 2.5M bp 151bp
D3 SRA: SRR9932168 2 x 2.5M bp 151bp
D4 SRA: SRX6999918 2 x 2.5M bp 151bp

Machine details:
Processor: Intel(R) Xeon(R) 8280 CPU @ 2.70GHz
OS: CentOS Linux release 7.6.1810
Memory: 100GB





Citation

Vasimuddin Md, Sanchit Misra, Heng Li, Srinivas Aluru. Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. IEEE Parallel and Distributed Processing Symposium (IPDPS), 2019.

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