ASAP

Autism Susceptibility Analysis Pipeline with a focus on Structural Variants (SVs). This repository documents the tasks involved in this project, which may be executed either sequentially or asynchronously. The approach used for rare variant or pathogenic candidate discovery in this study can be applied broadly to families affected by any rare disease.

System Requirements

Hardware requirements: Any Processor capable of running x86_64 architecture and at least 128GB of memory. Some steps can process samples in parallel, while the steps that handle all samples together scale logarithmically with sample size. Software requirements: The developed code mainly depends on the Python3 scientific stack and has been tested on the following system: Ubuntu 22.04.

Table of Contents

• Sample
• QC
  • back-reference-qc
  • ntsm
  • VerifyBamID
  • Somalier
  • Merqury
  • sex-verify
• Genome assembly
• Genome alignment
• Variant calling
• SV merging
• Annotation
• Methylation
• Housekeeping
• Citation

Sample

Sample origin/cohort

This study comprised 189 individuals (51 families) from the SSC, SAGE, and Rett-like cohorts, and the methodology is applicable to families with any rare disease.

Count	Sex (proband-sibling)	Family type
12	F-F	quad
16	F-M	quad
3	M-F	quad
5	M-M	quad
13	F	trio
2	M	trio

The sample manifest is available in the supplementary data of the publication.

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QC

back-reference-qc (Kraken2)

• Use this pipeline to check for non-human contamination in reads.
  • Minimal requirement: FASTQ

ntsm

• Use this tool/pipeline to assess inter-sample contamination.
  • Minimal requirement: FASTQ

VerifyBamID

• Use this tool/pipeline to assess both non-human contamination and inter-sample contamination.
  • Minimal requirement: BAM

Somalier

• Use this tool/pipeline to assess inter-sample contamination, as well as ancestry and relatedness.
  • Minimal requirement: BAM

Merqury

• Use this tool/pipeline to assess genome assembly quality.
  • Minimal requirement: FASTQ and its own Illumina

sex-verify

• Use this to verify sex per cell or sample.
  • Minimal requirement: BAM
  • click here for notes

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Genome assembly

This step produces FASTA files.

hifiasm

• Use this pipeline/tool to assemble sample genomes. Trio-phased assembly requires parental Illumina data as input.

• Version used for all our samples: hifiasm 0.16.1 with HiFi data only.

fix-sex-chromosome

• Use this pipeline to correct partially phased sex chromosomes in autism family fathers.

Contiguous chromosome X/Y

• Use this pipeline to build contiguous sex chromosomes.

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Genome alignment

This step produces aligned BAM files.

pbmm2

• Use this pipeline to align HiFi FASTQ files to the reference genome.

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Variant calling

PAV

• Use this tool to call SVs with assemblies. (instructions)

PBSV

• Use this tool to call SVs with alignment (pbmm2 output).

Sniffles

• Use this tool to call SVs with alignment (pbmm2 output).

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SV merging

These steps are performed using Truvari in sequential order.

1. Intra-sample merge.

bcftools merge --thread {threads} --merge none --force-samples -O z -o {output.vcf.gz} {input.vcf1.gz} {input.vcf2.gz} {input.vcf3.gz}
truvari collapse -i {input.vcf.gz} -c {output.removed.vcf.gz} --sizemin 0 --sizemax 1000000 -k maxqual --gt het --intra --pctseq 0.90 --pctsize 0.90 --refdist 500 | bcftools sort --max-mem 8G -O z -o {output.collapsed.vcf.gz}

2. Inter-sample merge.

bcftools merge --threads {threads} --merge none --force-samples --file-list {input.vcflist} -O z | bcftools norm --threads 15 --do-not-normalize --multiallelics -any --output-type z -o {output.mergevcf.gz}
truvari collapse --input {input.mergevcf.gz} --collapsed-output {output.removed_vcf.gz} --sizemin 0 --sizemax 1000000 --pctseq 0.90 --pctsize 0.90 --keep common --gt all | bcftools sort --max-mem {resources}G --output-type z > {output.collapsed_vcf.gz}

3. Rare SV pool discovery of an example input.

python rareSVpool.py {input.collapsed_sv}

4. De novo validation

• Initial caller support using Truvari
• Callable region evaluation using BoostSV
• Genotyping support using kanpig
• Rare TR expansions/contractions using TRGT
• Multiple sequence alignment (MSA) using MAFFT
• Read-based support validation using subseq or notes here
• Manual inspection using IGV

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Annotation (GRCh38)

• Gene and location annotation using AnnotSV, and then simplified by using sim_annotSV.py
• CADD score using CADD-SV
• Regulatory annotation using REG data and comREG
• Combine all annotations from comREG

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Methylation

This step produces methylation bed files and corresponding bigwig files.

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Citation

For citation, please refer to our paper at: https://www.medrxiv.org/content/10.1101/2025.07.21.25331932v1

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