Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.
Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.
[{"after_qc":"Read N50 >10kb, mean quality >Q10 (ONT R10) / HiFi rq>0.99"},{"after_alignment":"Mapping rate >90%, coverage >=15x for confident SV calling"},{"after_calling":"SV count in the expected range, INS/DEL ratio sane, genotypes concordant"},{"after_benchmark":"Truvari F1 reported WITH its refdist/pctsize/pctseq, on Tier 1 AND CMRG"}]
Before using code patterns, verify installed versions match. If versions differ:
CLI: <tool> --version then <tool> --help to confirm flags
Use minimap2 >= 2.28: the lr:hq accurate-read preset was added in 2.27, and 2.28 fixes the 2.27 --MD regression. Supply a reference-matched tandem-repeat BED to the caller - it is the single biggest false-positive lever in repeats. Truvari renamed the alt-sequence-similarity param from --pctsim to --pctseq at v4; confirm against truvari bench --help.
If code throws an error, introspect the installed tool and adapt the example to the actual API rather than retrying.
Long-Read SV Pipeline
"Detect structural variants from my long-read sequencing data" -> Chain basecalling, platform-matched minimap2 alignment, an SV caller selected by platform and goal, an optional assembly-based branch, cohort merging, and a parameterized Truvari benchmark - with the SV mechanism delegated to the component skills.
This is a workflow (orchestration) skill: it makes the stage-to-stage decisions and quality gates that connect the component skills. It does NOT re-teach how a caller sees an SV or the VCF representation minefield - that lives in variant-calling/structural-variant-calling and long-read-sequencing/structural-variants.
Why long reads for SV: the physics that justifies this pipeline
Run this pipeline instead of a short-read SV workflow for one mechanistic reason: a single long read (PacBio HiFi ~15-25 kb, ONT tens of kb to >Mb ultralong) physically spans the SV and both flanks in one molecule, turning SV detection from an inference-over-fragments problem into near-direct observation. Two consequences decide whether long reads earn their per-sample cost:
Insertions become tractable. Placing and sizing an insertion needs reads that carry the novel bases; when an INS exceeds Illumina read length (150 bp) no short read spans it, so short-read INS recall is stuck at ~30-50% while long reads reach ~90%+. Ebert 2021 (Science 372:eabf7117) found 68% of 107,590 assembly-discovered SVs were missed by short reads. If insertions matter, this pipeline is the answer, not a tuning knob.
Repeat-mediated junctions resolve. A 20 kb read anchored in unique sequence on both flanks spans a breakpoint buried in a 5 kb repeat that no 150 bp read can straddle, bringing segmental-duplication NAHR, mobile-element insertions, and VNTR/STR expansions into reach.
The governing pipeline principle: an SV call is a of choices made upstream. The aligner preset, the tandem-repeat BED handed to the caller, and the Truvari matching parameters move precision/recall as much as the caller does. Chaining decisions - not the caller name - are what this skill is about.
representation artifact
Pipeline map
POD5/FAST5 (ONT only)
| [Step 0] Dorado basecall -> long-read-sequencing/basecalling
v (model + methylation are IRREVERSIBLE choices; sup model for SV)
FASTQ (ONT / PacBio HiFi)
| [QC] NanoPlot / NanoComp -> long-read-sequencing/long-read-qc
v gate: read N50 >10 kb, sane quality, chimera screen
[Step 1] minimap2 alignment -> long-read-sequencing/long-read-alignment
| preset by platform/chemistry; -Y keeps breakpoint seq on split reads
v gate: mapping rate >90%, coverage >=15x
[Step 2] SV calling -> long-read-sequencing/structural-variants
| Sniffles2 / cuteSV / pbsv, caller by platform + goal
| + tandem-repeat BED (biggest FP lever) variant-calling/structural-variant-calling
v
[Step 3, optional] assembly-based SV (dipcall / PAV against a phased diploid assembly)
| highest-quality callset; the way truth sets are built
v
[Step 4] cohort merge -> two-step Sniffles2 .snf (per-sample -> combine)
|
v
[Step 5] benchmark -> Truvari vs GIAB HG002 Tier 1 + CMRG
an F1 is meaningless without refdist/pctsize/pctseq
Platform decision: ONT vs PacBio HiFi
The platform sets the preset, the caller options, and what bonus channels are available. Decide before basecalling.
highest base accuracy, small variants + SV in one run
SV caller
Sniffles2 or cuteSV
Sniffles2, cuteSV, or pbsv (official, TR-aware)
Bonus channel
5mCG/6mA methylation if requested AT basecall time
5mCG via kinetics; phasing native from HiFi length
R10.4 chemistry moved ONT simplex to ~Q20, which is why lr:hq (not the noisy-read map-ont) is the right preset for modern ONT - it rewrites the scoring/chaining model for accurate reads and runs faster at equal accuracy. Older R9 data still needs map-ont.
SV caller selection (by platform and goal)
Do not re-derive the caller mechanism here; pick by goal and hand tuning to the component skill.
Goal / platform
Caller
Why / cross-reference
ONT/HiFi germline, cohorts, mosaic
Sniffles2
field standard; two-step .snf population merge scales linearly in N; --mosaic for low-VAF (Smolka 2024)
Highest recall on noisy ONT
cuteSV
signature clustering; MUST pass the per-platform param set and --genotype (Jiang 2020)
PacBio HiFi, official, TR-aware
pbsv
expects pbmm2 alignments; single- and joint-sample modes
tandem-vs-interspersed DUP detail
SVIM
reports origin AND destination of duplications (Heller 2019)
Highest-quality callset / truth set
dipcall or PAV
assembly-vs-reference from a phased diploid assembly (Li 2018; Ebert 2021)
Somatic (tumor-normal)
Severus / nanomonsv
matched-normal subtraction; do NOT use Sniffles --mosaic for somatic (Keskus 2025)
Methods evolve; verify current best practice against each tool's docs before committing. Deeper caller tuning (cuteSV per-platform params, the tandem-repeat BED, aligner effects) lives in long-read-sequencing/structural-variants.
Step 0: Basecalling (ONT only)
Goal: Convert raw POD5/FAST5 signal into reads suitable for SV calling, capturing methylation if it will ever be needed.
Approach: Basecall with Dorado using a chemistry-matched sup (super-accuracy) model; request modified bases at basecall time because methylation cannot be recovered later. PacBio HiFi arrives as reads already, so this step is skipped.
# sup model maximizes accuracy for SV; 5mCG_5hmCG requested now (irreversible if omitted).# See long-read-sequencing/basecalling for model selection and duplex.
dorado basecaller sup pod5_dir/ --modified-bases 5mCG_5hmCG > reads.bam
samtools fastq -T MM,ML reads.bam | gzip > reads.fastq.gz # -T carries methylation tags through
Step 1: Alignment
Goal: Produce a sorted, indexed BAM whose split (supplementary) alignments retain the breakpoint sequence SV callers reconstruct from.
Approach: Align with the platform-matched minimap2 preset; keep -Y so supplementary alignments are soft-clipped (not hard-clipped), which preserves the junction bases on split reads. SV calling rides on supplementary, not secondary, alignments.
QC checkpoint (gate before spending compute on calling):
samtools flagstat aligned.bam # mapping rate should be >90%
samtools depth -a aligned.bam | awk '{s+=$3} END{print "mean cov:", s/NR}'# Gate: >=15x for confident SV calling; below ~10x callers drift toward false negatives.
Step 2: SV calling
Goal: Call SVs (>=50 bp DEL/INS/DUP/INV/BND) from the aligned reads with a caller matched to the platform.
Approach: Run Sniffles2 (the default) with a reference-matched tandem-repeat BED - it clusters the repeat-driven false positives that otherwise dominate the callset. --minsvlen 50 enforces the GIAB >=50 bp SV convention (Sniffles2 defaults to 35).
# Sniffles2: the tandem-repeat BED is the single biggest false-positive lever in repeats.
sniffles --input aligned.bam --reference reference.fa \
--tandem-repeats human_GRCh38_TR.bed \
--vcf svs.vcf.gz --threads 8 --minsvlen 50 --output-rnames
cuteSV as an alternative - its defaults are NOT platform-appropriate, and --genotype is off by default:
# ONT param set shown. HiFi: 1000/0.9/1000/0.5. CLR: 100/0.3/200/0.5. See structural-variants.mkdir -p work_dir # cuteSV requires the work dir to pre-exist; it does not create it
cuteSV aligned.bam reference.fa svs.vcf work_dir/ --threads 8 --genotype \
--max_cluster_bias_INS 100 --diff_ratio_merging_INS 0.3 \
--max_cluster_bias_DEL 100 --diff_ratio_merging_DEL 0.3
Step 3 (optional): Assembly-based SV
Goal: Produce the highest-quality SV callset by comparing a phased diploid assembly to the reference, rather than inferring from read alignments.
Approach: Assemble the genome (hifiasm/verkko), then call variants from the two haplotype assemblies aligned to the reference. dipcall (the syndip method) and PAV (the HGSVC method) are the assembly-vs-reference callers; this is how the GIAB and HGSVC truth sets themselves are built. Use it when an assembly already exists or when callset quality outranks turnaround.
# dipcall needs two haplotype assemblies (hap1/hap2) plus minimap2/k8/htsbox on PATH.
run-dipcall prefix reference.fa hap1.fa hap2.fa > prefix.mak
make -j2 -f prefix.mak # emits prefix.dip.vcf.gz + prefix.dip.bed
Step 4: Cohort merging (the two-step .snf design)
Goal: Build a joint-genotyped multi-sample SV matrix, not a union of per-sample discovery VCFs.
Approach: Sniffles2's population design processes each sample independently into a compact .snf, then combines the .snf files in a second pass - scaling linearly in N. A union of per-sample discovery VCFs is wrong: a sample recorded 0/0 may simply not have had that event discovered in it (a false missing), which corrupts allele frequencies. The two-step .snf combine force-genotypes every sample at every merged site.
# Pass 1: per-sample .snf (each sample processed once, independently).for s in sample1 sample2 sample3; do
sniffles --input ${s}.bam --reference reference.fa \
--tandem-repeats human_GRCh38_TR.bed --snf ${s}.snf
done# Pass 2: combine into a jointly genotyped cohort VCF (linear in N).
sniffles --input sample1.snf sample2.snf sample3.snf --vcf cohort.vcf.gz
For sequence-aware AF work across callsets, prefer Truvari collapse over position-only merging (position-only mergers inflate allele frequency by up to 2.2x; English 2022) - see variant-calling/structural-variant-calling for the merger decision table.
Step 5: Benchmarking - an SV F1 is meaningless without its parameters
Goal: Report a defensible, reproducible accuracy figure - not a number that looks good because of loose matching.
Approach: Truvari bench counts a call as a true positive only if it matches a truth variant under ALL of --refdist, --pctsize, and --pctseq simultaneously. Every one of these moves the score, so a bare F1 is uninterpretable. Report the full parameter set, run truvari refine for a harmonized re-comparison, and stratify by region.
Three escalating bars are routinely conflated, and a pipeline can pass the first while failing the ones that matter:
Event detection - something of about the right type/size near the right place (loose refdist, no sequence check). Easy.
Breakpoint accuracy - POS/END within a few bp (tight --refdist, --pctseq on). Matters at exon/splice boundaries.
Genotype accuracy - the sample GT (het/hom) is correct (genotype-aware comparison). A caller can detect an event perfectly and still call het-as-hom, which is fatal for Mendelian analyses.
Region stratification is decisive: Tier 1 (Zook 2020 Nat Biotechnol 38:1347) is conservative isolated SVs, while CMRG (Wagner 2022 Nat Biotechnol 40:672) covers the repetitive medically relevant genes Tier 1 leaves out - where GRCh38 false duplications cause reference-specific misses that masking raised from 8% to 100% recall. A good Tier 1 F1 certifies nothing about the genes clinicians care about; run both.
Filtering and annotation
bcftools view -i 'QUAL>=20 && ABS(SVLEN)>=50' svs.vcf.gz -Oz -o svs.filtered.vcf.gz
bcftools index svs.filtered.vcf.gz # ABS() is mandatory: DEL SVLEN is negative by convention
bcftools stats svs.filtered.vcf.gz > sv_stats.txt
AnnotSV -SVinputFile svs.filtered.vcf.gz -genomeBuild GRCh38 -outputFile annotated_svs
# gene overlap, DGV/gnomAD-SV population AF, ClinVar pathogenicity
Phased and methylation-aware SV (bonus channels)
Heterozygous variants on the same long read are physically phased, so SVs can be assigned to haplotypes with no statistical phasing. Haplotag the BAM (whatshap/sniffles --phase) before or during calling to get haplotype-resolved SVs; see long-read-sequencing/haplotype-phasing. If methylation was requested at basecall time (Step 0), the MM/ML tags ride through alignment (via minimap2 -y / samtools fastq -T) and give a per-haplotype methylation channel alongside the SV call at no extra sequencing cost - useful for imprinting and allele-specific silencing, but it must be captured at basecall time or it is gone.
SV types detected
Type
ALT
Notes for long reads
Deletion
DEL
excellent recall; breakpoints base-precise when a read spans the junction
Insertion
INS
the reason to use long reads; the read carries the inserted sequence
Duplication
DUP
tandem vs interspersed distinguishable (SVIM reports origin + destination)
Inversion
INV
resolved when unique anchors flank the repeat-embedded breakpoints
Translocation
BND
paired breakend records linked by MATEID; complex events are BND graphs
Common Errors
Symptom
Cause
Fix
Few SVs / missing known INS
coverage <10x or missing tandem-repeat BED
raise depth to >=15x; pass --tandem-repeats
Many false positives in repeats
no tandem-repeat BED supplied
provide a reference-matched TR BED (biggest FP lever)
map-ont on R10 data is slow/less accurate
wrong preset for accurate reads
use lr:hq for R10/Q20 ONT; map-ont only for R9
Split reads lost breakpoint sequence
aligned without -Y (hard-clipped supplementaries)
re-align with -Y
Methylation channel gone
not requested at basecall time
rebasecall with --modified-bases; it is irreversible
cuteSV recall poor / no genotypes
ran defaults; --genotype off
pass the per-platform param set and --genotype
Cohort "0/0" wrong, AF too low
took a union of per-sample discovery VCFs
use the two-step .snf combine (force-genotypes all sites)
ABS(SVLEN)>=50 filter drops all deletions
filtered raw SVLEN (DEL is negative)
always wrap in ABS()
Truvari F1 not reproducible / suspiciously high
reported without params, or --pctseq 0
state refdist/pctsize/pctseq/sizemin; never disable pctseq to look good
Passed Tier 1 but clinical genes fail
benchmarked only on Tier 1
also run CMRG (Tier 1 excludes those genes)
Related Skills
long-read-sequencing/basecalling - Dorado model choice and requesting methylation at basecall time (Step 0)
long-read-sequencing/long-read-alignment - minimap2 preset selection, -Y soft-clipping, MM/ML tag passthrough
long-read-sequencing/long-read-qc - read-length/quality QC and chimera screening before alignment
long-read-sequencing/structural-variants - caller tuning (cuteSV per-platform params, tandem-repeat BED, Truvari) - the SV mechanism for long reads
long-read-sequencing/haplotype-phasing - haplotag the BAM for phased/somatic SVs
variant-calling/structural-variant-calling - the SV signal model, SVLEN-sign / symbolic-vs-BND / CIPOS representation, force-genotyping, sequence-aware merging (also short-read SV)
variant-calling/consensus-sequences - why symbolic <DEL>/<INS> alleles are not directly consensus-able
References
Li H. Minimap2: pairwise alignment for nucleotide sequences. 2018 Bioinformatics 34:3094-3100.
Sedlazeck FJ, Rescheneder P, Smolka M, Fang H, Nattestad M, von Haeseler A, Schatz MC. Accurate detection of complex structural variations using single-molecule sequencing. 2018 Nature Methods 15:461-468. (Sniffles v1 + NGMLR)
Smolka M, Paulin LF, Grochowski CM, Horner DW, Mahmoud M, Behera S, et al. Detection of mosaic and population-level structural variants with Sniffles2. 2024 Nature Biotechnology. doi:10.1038/s41587-023-02024-y. (two-step .snf population merge; mosaic SVs)
Jiang T, Liu Y, Jiang Y, Li J, Gao Y, Cui Z, et al. Long-read-based human genomic structural variation detection with cuteSV. 2020 Genome Biology 21:189.
Heller D, Vingron M. SVIM: structural variant identification using mapped long reads. 2019 Bioinformatics 35:2907-2915.
Li H, Bloom JM, Farjoun Y, Fleharty M, Gauthier L, Neale B, MacArthur D. A synthetic-diploid benchmark for accurate variant-calling evaluation. 2018 Nature Methods 15:595-597. (dipcall/syndip)
Ebert P, Audano PA, Zhu Q, Rodriguez-Martin B, Porubsky D, Bonder MJ, et al. Haplotype-resolved diverse human genomes and integrated analysis of structural variation. 2021 Science 372:eabf7117. (PAV; 68% of SVs missed by short reads)
Keskus AG, et al. Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing. 2025 Nature Biotechnology. doi:10.1038/s41587-025-02618-8.
English AC, Menon VK, Gibbs RA, Metcalf GA, Sedlazeck FJ. Truvari: refined structural variant comparison preserves allelic diversity. 2022 Genome Biology 23:271. (defaults refdist 500, pctsize 0.70, pctseq 0.70, sizemin 50; up to 2.2x AF inflation from position-only merging)
Zook JM, Hansen NF, Olson ND, Chapman L, Mullikin JC, Xiao C, et al. A robust benchmark for detection of germline large deletions and insertions. 2020 Nature Biotechnology 38:1347-1355. (GIAB HG002 SV Tier 1)
