Version Compatibility

Reference examples tested with: Sniffles 2.2+, cuteSV 2.1+, minimap2 2.28+, samtools 1.19+, truvari 4.0+.

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags

Results depend on inputs that outlive the binary version - record them:

• The reference-matched tandem-repeat BED supplied to the caller (Sniffles --tandem-repeats) drives the FP rate in repeats more than any other setting. Record which TR BED was used.
• Benchmark numbers depend on the region set + TR handling + Truvari params; record all three.
• cuteSV parameters are platform-specific (ONT vs HiFi vs CLR); the defaults are not platform-appropriate.

If code throws an error, introspect the installed tool (sniffles --help, cuteSV --help) and adapt the example to the actual API rather than retrying.

Long-Read Structural Variants

"Find structural variants in my long reads" -> Map with the SV-ready preset (soft-clipped supplementaries), call with a TR-aware caller, and benchmark stating the region set and Truvari params.

• CLI: sniffles --input aln.bam --vcf svs.vcf --reference ref.fa --tandem-repeats TR.bed

Long reads are the killer app for SVs: a single read spans the breakpoint (within-read CIGAR or split alignment) and resolves repeats short reads cannot. By convention SV = >=50 bp; the 30-100 bp range is a VNTR-dominated gray zone where callers disagree most.

The Single Most Important Modern Insight -- An SV Call Is a Representation Artifact as Much as a Biological Fact

In tandem repeats and segmental duplications, the same biological event has many valid VCF encodings - a deletion can be written as the reciprocal insertion on the other allele, and a VNTR expansion's breakpoints slide freely across repeat units. Consequently:

1. The tandem-repeat BED, the aligner, and the Truvari parameters decide precision/recall as much as the caller does. A claim like "caller X has F1 0.95" is meaningless without also stating the region set, the TR BED supplied to the caller, and the Truvari params - change any one and the number moves more than the gap between callers.
2. Without a TR BED, one event fragments into several false-positive calls with inconsistent breakpoints. --tandem-repeats makes clustering repeat-aware (widening the merge window inside annotated TRs) - the single biggest FP-reduction lever, not a nicety.
3. truvari refine exists precisely to re-harmonize representations within TR regions; benchmarking TR-dense regions without it systematically understates recall.

Caller Taxonomy

Tool	Regime	Best for	Citation
Sniffles2	germline + population + mosaic	the default germline workhorse; cohort joint genotyping; .snf merge	Smolka 2024 Nat Biotechnol 42:1571
cuteSV	germline	high sensitivity, speed; per-platform tuning required	Jiang 2020 Genome Biol 21:189
SVIM	germline	scores (not hard-filters) SVs; good INS detection	Heller 2019 Bioinformatics 35:2907
pbsv	germline (PacBio)	two-step discover->call; official PacBio tool	PacBio (no journal paper)
NanoVar	germline, low-depth	4-8x ONT clinical	Tham 2020 Genome Biol 21:56
dipcall / SVIM-asm / PAV	assembly-based germline	most accurate single sample with phased HiFi; truth-set generation	Li 2018; Heller 2021; Ebert 2021
Severus	somatic (tumor-normal)	cancer T/N, complex/subclonal	Keskus 2026 Nat Biotechnol
nanomonsv	somatic (tumor-normal)	precise somatic breakpoints, MEI	Shiraishi 2023 NAR 51:e74
SVision-pro	de novo + somatic, complex	resolving nested CSVs	Wang 2025 Nat Biotechnol 43:181

Decision Tree by Scenario

Scenario	Recommended	Why
Single ONT/HiFi germline sample	Sniffles2 + --tandem-repeats	TR-aware, auto support, fast
Cohort germline	Sniffles2 per-sample .snf -> merge	re-genotypes from raw signal; true joint genotypes
Maximum sensitivity / speed	cuteSV with the platform-matched param set	per-platform tuning is mandatory
Phased HiFi, want best per-sample accuracy	assembly-based (dipcall/SVIM-asm) -> hifi-assembly	resolves the alt haplotype directly
Tumor-normal somatic SVs	Severus or nanomonsv	paired callers; Sniffles --mosaic is single-sample only
Low-VAF mosaic in one sample	Sniffles2 --mosaic	lowers support, reports VAF (not a T/N caller)
Low coverage (4-8x)	NanoVar	designed for low-depth clinical
Benchmarking	Truvari (+refine) vs GIAB Tier1/CMRG	the field standard; state region + params

Alignment for SV Calling

Map with minimap2 (the modern default; NGMLR is a higher-precision/slower legacy niche for Sniffles). Use the platform preset and keep soft-clipped supplementary alignments - split-read callers reconstruct breakpoints from the clipped sequence on those records.

minimap2 -ax map-ont --MD -Y ref.fa ont.fq.gz | samtools sort -o aln.bam && samtools index aln.bam
#   -Y keeps SEQ on supplementaries (the SV substrate); --MD for cuteSV; map-hifi/map-pb for PacBio

Sniffles2 - germline and the .snf population workflow

# Single sample (always supply --reference for INS sequence and --tandem-repeats for repeats)
sniffles --input aln.bam --vcf svs.vcf --reference ref.fa --tandem-repeats human_GRCh38_TR.bed

# Cohort: per-sample .snf signature index, then merge + joint-genotype
sniffles --input s1.bam --snf s1.snf --reference ref.fa --tandem-repeats TR.bed
sniffles --input s2.bam --snf s2.snf --reference ref.fa --tandem-repeats TR.bed
sniffles --input s1.snf s2.snf --vcf cohort.vcf --reference ref.fa

# Force-call / regenotype a known SV set in a new sample
sniffles --input new.bam --genotype-vcf known_svs.vcf --vcf genotyped.vcf

# Single-sample low-VAF / mosaic (NOT a tumor-normal caller)
sniffles --input tumor.bam --vcf mosaic.vcf --mosaic

The .snf is a binary signature index (NOT a VCF - never bcftools it); it retains sub-threshold signatures so the merge re-genotypes an SV even in a sample that did not independently pass support.

cuteSV - the per-platform parameter trap

cuteSV's defaults are not platform-appropriate; the README gives distinct sets by error rate. --genotype is OFF by default. Positional args: cuteSV <bam> <ref> <out.vcf> <work_dir>. Force-calling moved to the separate cuteFC tool.

Platform	--max_cluster_bias_INS	--diff_ratio_merging_INS	--max_cluster_bias_DEL	--diff_ratio_merging_DEL
ONT	100	0.3	100	0.3
PacBio HiFi/CCS	1000	0.9	1000	0.5
PacBio CLR	100	0.3	200	0.5

mkdir cutesv_work
cuteSV aln.bam ref.fa cutesv.vcf cutesv_work --genotype \
  --max_cluster_bias_INS 100 --diff_ratio_merging_INS 0.3 \
  --max_cluster_bias_DEL 100 --diff_ratio_merging_DEL 0.3   # ONT set

Benchmarking with Truvari

truvari bench --base giab_tier1.vcf.gz --comp calls.vcf.gz \
  --includebed tier1_regions.bed --pctseq 0.7 --refdist 500 --passonly -o bench/
truvari refine bench/        # re-harmonize TR-region representations for a fair comparison

--pctseq (default 0.7) compares the actual inserted/deleted sequence, not just coordinates - set 0 for depth-based callers lacking alt sequence, keep 0.7 for long-read callers. Region set dominates the headline: Tier1 (resolvable INS/DEL >=50 bp) overstates whole-genome performance; CMRG reflects hard clinical loci. Tier1 v0.6 is INS/DEL only - do not report INV recall against it.

Per-Method Failure Modes

One VNTR fragments into many false positives

Trigger: calling in tandem repeats without a TR BED. Mechanism: the breakpoint slides across repeat units, scattering signatures. Symptom: several calls with inconsistent breakpoints where one event exists. Fix: supply --tandem-repeats to the caller; truvari refine when benchmarking.

cuteSV defaults inflate or fragment calls

Trigger: running cuteSV with one parameter set across platforms. Mechanism: HiFi settings over-merge ONT noise; ONT settings fragment clean HiFi signatures. Symptom: FP inflation or split calls. Fix: use the platform-matched set; remember --genotype is off by default.

Missing insertion sequence / breakpoints

Trigger: Sniffles without --reference, or alignment without -Y. Mechanism: no reference -> no ALT sequence; hard-clipped supplementaries -> lost breakpoint sequence. Symptom: INS lack sequence; imprecise breakpoints. Fix: add --reference and align with -Y.

Treating Sniffles --mosaic as a cancer caller

Trigger: somatic SV calling with single-sample --mosaic. Mechanism: mosaic mode lowers support in one sample; it has no normal to subtract. Symptom: germline SVs reported as somatic; FP at low VAF. Fix: Severus or nanomonsv (paired tumor-normal).

Comparing F1 across studies that handled repeats differently

Trigger: quoting F1 without region + TR BED + Truvari params. Mechanism: representation handling moves the number more than the caller. Symptom: apples-to-oranges comparisons. Fix: fix the region set, TR BED, and Truvari params; run truvari refine.

Quantitative Thresholds

Threshold	Source	Rationale
SV >= 50 bp	GIAB convention	30-100 bp is a VNTR gray zone where callers disagree
Sniffles --minsvlen 35, --mapq 25, --minsupport auto	Sniffles2 manpage	the actual defaults (support is coverage-derived, not a fixed 3)
Coverage ~20-30x germline; >30-60x mosaic/somatic	SV practice	large SVs callable from 5-10x; low-VAF needs depth
Truvari --pctseq 0.7, --refdist 500	English 2022	sequence-aware INS matching; loosen refdist to 1000 only for fuzzy callers
cuteSV params per platform	cuteSV README	error rate sets cluster bias / merge ratio

Common Errors

Error / symptom	Cause	Solution
Many FP calls in repeats	no TR BED	supply --tandem-repeats
cuteSV VCF has no GT	--genotype off by default	add --genotype
Cannot bcftools the .snf	.snf is a binary signature index	use it as Sniffles input, not a VCF
INS records lack sequence	--reference not supplied	add --reference ref.fa
Imprecise/missing breakpoints	supplementaries hard-clipped	align with minimap2 -Y
Looking for cuteSV force-calling flag	moved to cuteFC	use the cuteFC tool
Somatic SVs from a single sample	germline/mosaic caller	Severus / nanomonsv (paired)

References

• Smolka M, Paulin LF, Grochowski CM, et al. 2024. Detection of mosaic and population-level structural variants with Sniffles2. Nat Biotechnol 42:1571-1580.
• Jiang T, Liu Y, Jiang Y, et al. 2020. Long-read-based human genomic structural variation detection with cuteSV. Genome Biol 21:189.
• Heller D, Vingron M. 2019. SVIM: structural variant identification using mapped long reads. Bioinformatics 35:2907-2915.
• English AC, Menon VK, Gibbs RA, Metcalf GA, Sedlazeck FJ. 2022. Truvari: refined structural variant comparison preserves allelic diversity. Genome Biol 23:271.
• Zook JM, Hansen NF, Olson ND, et al. 2020. A robust benchmark for detection of germline large deletions and insertions. Nat Biotechnol 38:1347-1355.
• Wagner J, Olson ND, Harris L, et al. 2022. Curated variation benchmarks for challenging medically relevant autosomal genes (CMRG). Nat Biotechnol 40:672-680.
• Keskus AG, Bryant A, Ahmad T, et al. 2026. Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing. Nat Biotechnol 44:247-257.

Related Skills

• long-read-alignment - SV-ready mapping (-Y soft-clip, platform preset)
• basecalling - Read accuracy/length that gates breakpoint precision
• clair3-variants - Small variants (<50 bp) are Clair3's job, not an SV caller's
• haplotype-phasing - Haplotag the BAM for haplotype-specific / phased SVs
• genome-assembly/hifi-assembly - Phased assembly for assembly-based SV calling
• variant-calling/structural-variant-calling - The variant-calling-side SV view
• variant-calling/vcf-manipulation - Filter/merge the SV VCFs
• genome-intervals/gtf-gff-handling - Annotate SVs against gene models