# Clone this repo on o2
git clone git@github.com:agorelick/lpASCN.git
This pipeline should be run from FASTQ files in order to ensure that all preprocessing is standardized. In case you only have access to BAM files (and you aren't sure exactly how they were generated), you can convert them back to FASTQs with this script. Note: this assumes paired-end reads.
First, copy convert_bams_to_fastqs.sh from this repo to a location on o2 where you can save large data files. Then create a symbolic link to a directory with the bam files you will use:
# create sym-link to the bam directory
ln -s [PATH TO DIRECTORY WITH BAM FILES] original_bams
Then, modify the convert_bams_to_fastqs.sh script to include the names of the bam files in the "samples" array. You may also need to change the bam file name in the BAM="original_bams/${sample}_aligned.bam" to match how your pre-existing bam files are named.
Finally, run the convert_bams_to_fastqs.sh script to regenerate paired-end fastq.gz files for each sample. These jobs will be run very quickly, ~ 5 min per sample.
sbatch convert_bams_to_fastqs.sh
This step uses the run_processing.sh slurm script to generate analysis-ready bam files based on GATK's Best Practices for Data pre-processing for variant discovery (see: https://gatk.broadinstitute.org/hc/en-us/articles/360035535912).
Copy the run_processing.sh script to a location on o2 containing the subdirectory "00_fastq/" which contains paired-end FASTQ files with suffixes: "_R1_001.fastq.gz" and "_R2_001.fastq.gz" (this is the default provided by Azenta).
Next, modify the script to use your sample names and vial names (which refer to the name from the fastq file). Also edit the number of jobs in the header #SBATCH --array=0-8 for your number of samples. These are indexed at 0, so this example is for 9 samples to be run parallel.
Note: the adapter sequences in the cutadapt section are for Illumina universal adapter sequences, which is what Azenta uses for lpWGS. You can check this by running FASTQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) on your FASTQ files, and then changing the adapter sequences if necessary.
Also note: This script is setup for alignment to human reference genome b37 (Broad's version of GRCh37/hg19). This can be changed to b38/GRCh38/hg38, but as of 12/16/2023 Alex's haplotype-aware copy number calling pipeline requires b37.
Total run-time per sample is around 6 hours.
Run the slurm job on o2 via the command sbatch run_preprocessing.sh.
Once the preprocessing step has completed (or when a normal sample for the patient has finished preprocessing), you can start the next step to obtained haplotype phased common SNPs. We will use GLIMPSE2 (https://odelaneau.github.io/GLIMPSE/), a software designed for genotyping common SNPs using low-coverage WGS. This works by using a reference panel of genotypes from haplotype-phased whole genomes along with genomic linkage maps to impute common SNPs missing from our low-coverage bam and estimate their phasing orientation to each other. The accuracy of this process depends on the number of whole genomes in the reference panel, the frequency of the imputed SNP in the population, and coverage in the given bam file. According to the GLIMPSE2 paper, using 1x WGS and data from the 1000 genomes consortium as a reference panel, we can expect greater than 80% accuracy when imputing/phasing SNPs in 1% of the population. This seems to be quite sufficient for copy number uses. However, it is not sufficient evidence for genotyping specific germline variants in a patient's genome! Also note: statistical phasing is less accurate for SNPs that are further apart. Roughly, heterozygous germline SNPs less than 20kb apart can be phased with 90% accuracy.
Download pre-built executable files for GLIMPSE2 onto o2
mkdir ~/.GLIMPSE2; cd ~/.GLIMPSE2
wget https://github.com/odelaneau/GLIMPSE/releases/download/v2.0.0/GLIMPSE2_chunk_static
wget https://github.com/odelaneau/GLIMPSE/releases/download/v2.0.0/GLIMPSE2_concordance_static
wget https://github.com/odelaneau/GLIMPSE/releases/download/v2.0.0/GLIMPSE2_ligate_static
wget https://github.com/odelaneau/GLIMPSE/releases/download/v2.0.0/GLIMPSE2_phase_static
wget https://github.com/odelaneau/GLIMPSE/releases/download/v2.0.0/GLIMPSE2_split_reference_static
Next, copy run_glimpse.sh to the directory containing the newly-created preprocessing/ subdirectory, which contains a bam file from a normal tissue sample. Edit the script to replace the name of the normal sample (in the example, it is C157N1). Then run the script with sbatch run_glimpse.sh.
Azenta's lpWGS analysis pipeline includes genotype imputation: https://web.azenta.com/genotyping-arrays-low-pass-genome-sequencing. These are provided in files named analysis/[samplename].vcf.gz (with genome build b37). You can subset these for only high-confidence heterozygous SNPs via the following bcftools command. Note that we need -c 0 to be added to make sure that the output has field "AC" required by SHAPEIT4 for phasing.
bcftools view -i "%FILTER='PASS'" -g het -O z -c 0 [samplename].vcf.gz > [samplename]_hetsnps.vcf.gz
bcftools index [samplename]_hetsnps.vcf.gz
files=''
for chr in {1..22}; do files=$files" "[samplename]_hetsnps_phased_chr${chr}.bcf; done
bcftools concat -o [samplename]_hetsnps_phased.bcf $files
bcftools index [samplename]_hetsnps_phased.bcf
Filter phased VCF for heterozygous SNPs with high confidence (GP>=0.9) and remove FORMAT/DS,GP fields from GLIMPSE2
bcftools view -O b -g het C157N1_Normal1_ligated.bcf -i 'FORMAT/GP>=0.90' | bcftools annotate -x FORMAT/DS,FORMAT/GP -O b > C157N1_Normal1_ligated_cleaned.bcf
bcftools view -i "%FILTER='PASS' | %FILTER='.'" MDA11_filtered.vcf.gz | bcftools view -I - -O z -o MDA11_filtered_passed.vcf.gz
medicc2 --bootstrap-nr 200 --bootstrap-method chr-wise --events --plot both MDA11_q=10_Q=20_P=0_1000kbs_segments_for_medicc2.tsv medicc2_bs=200_chr_events