Building Mitochondrial anchor-based graphical genome from long reads. Filter Numts reads, assemble major haplotypes, call homoplasmic and heteroplasmic variants, analyze methylation signals
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
git clone https://github.com/broadinstitute/Himito.git
cd Himito
cargo build --release
# filter NUMTs-derived reads
./target/release/Himito filter -i <input.bam> -c <chromosome in bam, e.g. "chrM"> -m <mt_output.bam> -n <numts_output.bam>
# construct graph
./target/release/Himito build -i <mt_output.bam> -k <kmer_size> -r <NC_012920.1.fasta> -o <output.gfa>
The graph served as the foundation for downstream assembly, variant calling and methylation analysis.
If you have paired short reads data, you can refine the graph by using Himito correct.
Himito correct trims graph paths according to the occurrence counts of path kmers in the paired srWGS data. We recommend to first subset the srWGS BAM file to reads aligned to chrM, then compressed these reads into a msBWT.
msbwt2-build -o sr_msbwt.npy <srWGS.chrM.fasta.gz>
./target/release/Himito correct -g <output.gfa> -b <bwt_file, e.g. sr_msbwt.npy> -o <corrected.gfa> -m <minimal_supporting_sr> -q <query_length, should be less than short read length>
# call variants from graph
./target/release/Himito call -g <output.gfa> -r <NC_012920.1.fasta> -k <kmer_size> -s <sampleid> -o <output.vcf>
# extract major haplotype from graph
./target/release/Himito asm -g <output.gfa> -o <output.majorhaplotpe.fasta> -s <header string, e.g. "HG002 major haplotype">
# call methylation signals
./target/release/Himito methyl -g <output.annotated.gfa> -p <min_prob> -b <mt_test.bam> -o <methyl.bed>
# enumerate all possible haplotypes within windows
./target/release/Himito minorhap -g <output.gfa> -o <output.allhaplotype.fasta> -s <sample_id>