collapse-bubble

SV merging for pangenome VCF generated by minigraph-cactus pipeline and decomposed by vcfwave. It merges similar SVs within the same bubble, e.g., highly polymorphic INS at the same position.

• Overview
• Dependency
• Input
• Analysis pipeline
  1. Preprocessing
  2. SV merging
  3. Duplicates merging

Note: I will consolidate the entire pipeline into a single script once I have time. For now, please follow the step-by-step instructions.

Overview

This pipeline uses Truvari's API to merge SVs and genotypes. As compared to truvari collapse, it is optimized for pangenome VCF:

1. Only SVs within the same bubble are compared for merging.
2. When merging genotypes, the phase of genotypes is checked and retained:
   • 0|0 and 1|0: consistent, merge into 1|0
   • 0|1 and 1|0: consistent, merge into 1|1
   • 1|0 and 1|0: inconsistent, don't merge if found in any sample
3. If a SV has large REF and ALT alleles, its size is determined by REFLEN (length of REF) instead of SVLEN (length difference between REF and ALT). This works better for large inversions or complex SVs. For example, 100bp INV and 1000bp INV have the same SVLEN but different REFLEN

Dependency

All scripts have been tested with Python 3.10. Please install the following python modules to run the scripts.

truvari
pysam
pandas
numpy

We also use the below tools to process VCF:

• bcftools: +fill-tags plugin is used, please export BCFTOOLS_PLUGINS to configure it
• vcfwave: please use vcfwave v1.0.12 or later, the previous versions can output incorrect genotypes for a few variants.

Input

• Multiallelic graph VCF generated by minigraph-cactus and processed by vcfbub. The ID field of this VCF should be the unique bubble ID, e.g. <73488<73517 . This is default output VCF of minigraph-cactus pipeline.
• Reference genome fasta file for normalization

Analysis pipeline

1. Preprocessing:

The preprocessing step aims to generate a VCF file meet the following requirements:

• decomposed by vcfwave
• biallelic, sorted VCF
• all variants have unique ID
• only variants from the same bubble have identical INFO/BUBBLE_ID
• AC/AN/AF have been updated based on the genotypes

If you already have such a VCF, it can be directly used without preprocessing. To start with the multiallelic graph VCF, we will do:

• split multiallelic records into biallelic, update AC, AN, AF (bcftools)
• annotate variants' bubble ID, rename variants with unique names (annotate_var_id.py)
• decompose VCF (vcfwave)
• left-align and sort (bcftools)

# suppose the name of input VCF is "mc.vcf.gz"

# split into biallelic
bcftools norm -m -any mc.vcf.gz -Oz -o mc.biallele.vcf.gz

# annotate VCF and assign unique variant ID
python scripts/annotate_var_id.py \
-i mc.biallele.vcf.gz \
-o mc.biallele.uniq_id.vcf.gz

# optional: first drop INFO/AT (useless after decomposition, suggested by cactus)
bcftools annotate -x INFO/AT -Ou mc.biallele.uniq_id.vcf.gz | \
vcfwave -I 1000 | bgzip -c > mc.biallele.uniq_id.vcfwave.vcf.gz

# normalization and sort, update AC/AN/AF
bcftools norm -f ref.fa mc.biallele.uniq_id.vcfwave.vcf.gz | \
bcftools +fill-tags -- -t AC,AN,AF | \
bcftools sort --max-mem 4G -Oz -o mc.biallele.uniq_id.vcfwave.sort.vcf.gz

annotate_var_id.py:

After spliting the multiallelic graph VCF into a biallelic VCF, all variants from the same bubble (i.e., same multiallelic record) should have the same value in ID field.This script copies this ID to INFO/BUBBLE_ID, and renames variants with a unique ID in format of [BUBBLE_ID].[TYPE].[No.].

usage: annotate_var_id.py [-h] -i VCF -o VCF

options:
  -i VCF, --input VCF   Input VCF
  -o VCF, --output VCF  Output VCF

2. SV merging

To perform SV merging, run collapse_bubble.py:

python collapse_bubble.py \
-i mc.biallele.uniq_id.vcfwave.sort.vcf.gz \
-o merged.vcf.gz \
--map merged.mapping.txt

This script uses Truvari API to merge SVs have the same INFO/BUBBLE_ID, it output 2 files:

1. VCF:

The output VCF is not sorted and has merged variants and genotypes. Similar SVs are merged into the one with the highest MAF. The following fields of the merged SV are added or modified:

• INFO/ID_LIST: comma-separated list of variants merged into this variants
• INFO/TYPE: type of variant (SNP, MNP, INS, DEL, INV, COMPLEX)
• INFO/REFLEN: len(ref)
• INFO/SVLEN: len(alt) - len(ref)
• FORMAT/GT: merged genotypes

For example:

#input:
chr1  10039  >123>456.INS.1  A  ATTTTTT  AC=2;AN=6;AF=0.333;BUBBLE_ID=>123>456  0|1  1|0  0|0
chr1  10039  >123>456.INS.2  A  ATTTTTG  AC=3;AN=6;AF=0.500;BUBBLE_ID=>123>456  1|0  0|1  1|0

#output
chr1  10039  >123>456.INS.2  A  ATTTTTG  AC=3;AN=6;AF=0.500;BUBBLE_ID=>123>456;ID_LIST=>123>456.INS.1;TYPE=INS;REFLEN=1;SVLEN=6  1|1  1|1  1|0

2. SV merging table:

A tsv file to map the original SVs (Variant_ID) to merged SVs (Collapse_ID), for example:

CHROM   POS      Bubble_ID   Variant_ID       Collapse_ID
chr1    10039    >123>456    >123>456.INS.1   >123>456.INS.2
chr1    10039    >123>456    >123>456.INS.2   >123>456.INS.2

Moreover, VCF INFO fields can also be added as columns by specifying --info. If --info SVLEN is used, the output SVLEN in the tsv file will be REFLEN for COMPLEX and INV to indicate the value used for comparison. While in the output VCF, INFO/SVLEN is always set as len(alt) - len(ref) for all SVs.

Arguments:

usage: collapse_bubble.py [-h] -i VCF -o VCF -m TSV [--chr CHR] [--info TAG] [-l 50] [-r 100] [-p 0.9] [-P 0.9] [-O 0.9]

Input / Output arguments:
  -i VCF, --invcf VCF   Input VCF
  -o VCF, --outvcf VCF  Output VCF
  -m TSV, --map TSV     Write SV mapping table to this file. Default: None
  --chr CHR             chromosome to work on. Default: all
  --info TAG            Comma-separated INFO/TAG list to include in the output map. Default: None

Collapse arguments:
  -l 50, --min-len 50   Minimum allele length of variants to be included, 
                        defined as max(len(alt), len(ref)). Default: 50
  -r 100, --refdist 100
                        Max reference location distance. Default: 100
  -p 0.9, --pctseq 0.9  Min percent sequence similarity (REF for DEL, ALT for other SVs). Default: 0.9
  -P 0.9, --pctsize 0.9
                        Min percent size similarity (SVLEN for INS, DEL; REFLEN for INV, COMPLEX). Default: 0.9
  -O 0.9, --pctovl 0.9  Min pct reciprocal overlap. Default: 0.9

3. Duplicates merging:

TODO: I re-write merge_duplicates.py and significantly changes its algorithm. Will update the instructions for latest pipeline. For now, please refer to docs to see how it works, or use the previous version commit dcbb4c8.