bio-variant-calling-structural-variant-calling

name	bio-variant-calling-structural-variant-calling
description	Call structural variants (>=50 bp deletions, insertions, inversions, duplications, translocations) from short- or long-read data by reconstructing four orthogonal signals (discordant pairs, split reads via the SA tag, read depth, local assembly). Covers Manta, DELLY, LUMPY/smoove, GRIDSS2, SvABA for short reads and Sniffles2, cuteSV, pbsv, dipcall/PAV for long reads, each mapped to the signals it fuses and the blind spots that follow. Use when choosing an SV caller from its signal set and failure modes, decoding the SVLEN-sign / symbolic-vs-BND / CIPOS VCF representation minefield, force-genotyping a cohort matrix instead of unioning discovery VCFs, merging populations with sequence-aware Truvari vs position-only SURVIVOR, parameterizing a Truvari benchmark, or deciding when short-read insertion recall forces a switch to long reads. Not for pure copy-number dosage (see copy-number/cnvkit-analysis).
origin	openai4s
category	bioskills/variant-calling
metadata	{"tool_type":"cli","primary_tool":"manta","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: Manta 1.6+, DELLY 1.2+, GRIDSS 2.13+, smoove 0.2+, SvABA 1.1+, bcftools 1.19+, samtools 1.19+, SURVIVOR 1.0.7+, Truvari 4.0+, Sniffles2 2.2+, cuteSV 2.0+

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

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

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Note: DELLY 1.x and Truvari 4.x renamed flags/defaults from earlier lines (Truvari's alt-sequence-similarity param was --pctsim, now --pctseq); always confirm against --help before trusting old muscle memory.

Structural Variant Calling

"Call structural variants from my WGS data" -> Reconstruct large rearrangements (DEL, INS, INV, DUP, BND) that no single read reports directly, by fusing evidence from how alignments break, disagree, or deviate from expectation.

• CLI (short read): configManta.py (Manta), delly call, smoove call (LUMPY), gridss, svaba run
• CLI (long read): sniffles, cuteSV, pbsv, dipcall.kit/pav (assembly-based)

The governing principle: SV discovery is signal reconstruction, not base-calling

An SV caller is not a pileup genotyper. SVs are never observed directly by a read; they are inferred from four orthogonal signals, and each caller fuses only a subset. Its blind spots follow directly from which signals it omits (Mahmoud 2019 Genome Biol 20:246 is the canonical signal-to-blind-spot map).

1. Discordant read pairs (RP) - a pair whose insert size or orientation departs from the library distribution. A DEL inflates the apparent insert; a tandem DUP gives everted (+/+/-/-) pairs; an INV flips one strand; an interchromosomal event splits mates across chromosomes. RP never sees the junction base, so it localizes a breakpoint only to fragment-size uncertainty (~+/-300-500 bp).
2. Split reads / soft-clips (SR) - one read aligning in two pieces to non-contiguous positions, recorded via the BAM SA (supplementary alignment) tag. The clip point IS the breakpoint at base resolution (~+/-0-10 bp). Requires a read that straddles the junction with unique anchor on both sides - impossible inside a repeat longer than the read or for an INS larger than (read length - anchor).
3. Read depth (RD) - copy-number shifts local coverage (het DEL ~0.5x, DUP ~1.5x, hom DEL ~0). The ONLY signal for large CNVs whose breakpoints fall in unmappable repeats; blind to balanced events (INV, balanced BND) that leave dosage unchanged. Resolution is bin-size-limited (100 bp-1 kb), the worst of any signal.
4. Local assembly (AS) - reconstruct the breakpoint-spanning contig de novo from clipped/discordant reads, then realign it. Recovers the exact junction sequence, microhomology, and inserted bases; resolves events no single read spans. Most powerful and most expensive; separates GRIDSS/Manta/SvABA from LUMPY.

Caller	RP	SR	RD	Assembly	Primary blind spot
Manta	Yes	Yes	filter/score only	Yes (targeted, breakend-local)	Large INS beyond breakend-local contig; balanced events in repeats
DELLY	Yes	Yes (SR realign for refinement)	Yes (delly cnv module)	No de novo	INS (only small, SR-anchored); precision below assembly callers
LUMPY / smoove	Yes	Yes	No	No	INS entirely (no representation for novel inserted sequence)
GRIDSS2	Yes	Yes	via GRIPSS/PURPLE downstream	Yes (genome-wide positional de Bruijn graph)	Pure RD-only CNVs with no junction reads; long-repeat interiors
SvABA	Yes	Yes	No	Yes (genome-wide SGA local assembly)	Large INS still assembly-limited; multi-kb DEL/DUP rely on RP linkage
CNVnator	No	No	Yes (mean-shift on RD bins)	No	Everything balanced; no breakpoint resolution; no small SVs

The real tradeoff is sensitivity vs breakpoint resolution (not vs specificity)

• RP-heavy calling maximizes SENSITIVITY (a pair spanning a junction is easy, works at low depth) but yields IMPRECISE breakpoints (+/-hundreds bp, CIPOS=-289,301).
• SR / assembly calling maximizes RESOLUTION (base-precise, CIPOS=0,0) but LOSES sensitivity wherever a read cannot cleanly straddle the junction (repeats, low depth, large INS).

Below ~30x the SR signal thins (fewer reads straddle any junction) and callers silently drift into the low-resolution RP-only regime. A caller demanding SR confirmation reports beautiful breakpoints and misses the repeat-mediated events that matter clinically; a caller accepting RP-only calls finds more but hands back wide confidence intervals.

Caller selection

Caller	Signals fused	Best when	Fails / do NOT use when
Manta	RP+SR+targeted AS	Default germline/somatic; fastest assembly-capable caller (sub-hour on 30x)	WES/panel WITHOUT --exome (high-depth filter silently drops true SVs); large INS beyond local assembly
DELLY	RP+SR (+RD CNV module)	Cohorts (site-list-then-regenotype); INV/BND	General large-INS calling; needs base-precise breakpoints in repeats
smoove (LUMPY)	RP+SR (probabilistic)	Simple, sane-default germline pipeline at low-moderate depth	ANY INS-critical pipeline (structurally zero INS recall)
GRIDSS2	RP+SR+genome-wide AS	Highest precision; somatic; single-breakends (viral integration, centromeric)	Using raw VCF as a callset (it is a breakpoint GRAPH - run GRIPSS/PURPLE/LINX); need speed
SvABA	RP+SR+genome-wide AS	The 20-300 bp indel/SV "twilight zone"; templated-insertion detection in cancer	Large balanced events; when Manta speed suffices
CNVnator / CNVpytor	RD only	Dosage CNVs with breakpoints in unmappable repeats	Balanced SVs; any breakpoint-precision need (see copy-number/cnvkit-analysis)
Sniffles2 / cuteSV / pbsv	long-read alignment	INS, repeat-mediated SVs, phased SVs, population .snf merging	Only short reads available

Consensus recipe: run DELLY + Manta + SvABA + GRIDSS2 (the latter GRIPSS-filtered first, since its raw VCF is a breakpoint graph, not a callset) and require >=2/4 agreement - singletons are enriched for false positives, so intersection trades a little recall for a large precision gain. Caveat: this caps INS recall (LUMPY-class blind spots drag the intersection down); for INS-heavy work prefer one assembly caller + a graph genotyper, or long reads. Methods evolve - verify current ensemble practice against tool docs before committing.

SV detection limits by platform

SV type	Short read	Long read	Key limitation
Deletion	Good (RP+SR)	Excellent	Short reads miss DELs buried in repeats
Duplication	Moderate (RP+RD)	Good	Tandem vs dispersed unreliable with short reads
Inversion	Moderate (RP)	Good	Breakpoints in repeats cause false negatives
Insertion	Poor (~30-50% recall)	Excellent (~90%+)	Physics limit: no short read spans an INS > read length
Translocation	Moderate (discordant)	Good	High FP rate near centromeres/telomeres
Complex/nested	Poor	Good (assembly)	Overlapping SVs confound short-read signals

Insertions are a physics limit, not a tuning failure. Placing and sizing an INS requires reads spanning both junctions plus the novel bases; when the INS exceeds read length (Illumina 150 bp), no read spans it and the inserted sequence is unrecoverable without assembly - and short-read assembly fails again when the insertion is a repeat (mobile element, VNTR). GIAB HG002 Tier 1 contains MORE INS than DEL (7,281 INS vs 5,464 DEL >=50 bp; Zook 2020 Nat Biotechnol 38:1347), yet INS are the hardest short-read class. Ebert 2021 (Science 372:eabf7117) found 68% of 107,590 assembly-discovered SVs were missed by short reads. Do NOT benchmark short-read INS against a long-read truth set and blame the caller - ~30-50% is the ceiling by construction.

The VCF representation minefield

The VCF spec represents SVs three ways and callers disagree on all three - this is where careful people lose days.

• SVTYPE - the class (DEL/INS/DUP/INV/BND).
• END (INFO) - the other breakpoint on the same chromosome. POS is the last unaffected anchor base, so a DEL at POS=1000, END=2000 removes 1001-2000. Off-by-one here corrupts every length calc. (VCF 4.4 began deprecating INFO/END for symbolic alleles; bcftools/GATK still emit and consume the classic field.)
• SVLEN - SIGNED length: DEL is NEGATIVE, INS/DUP positive by historical convention (Manta, DELLY follow it; some tools emit absolute values). NEVER filter on raw SVLEN without ABS() or a naive SVLEN >= 50 drops every deletion.
• CIPOS / CIEND - confidence intervals encoding the RP-vs-SR resolution story: an SR-resolved breakpoint is CIPOS=0,0, an RP-only one is CIPOS=-289,301. This is the ground truth for "how much do I trust this breakpoint" and exactly what a population merger must respect.
• IMPRECISE (flag) - set when the breakpoint is RP/depth-derived, not junction-resolved; its absence implies PRECISE. An all-IMPRECISE callset cannot be merged tightly.

Breakend (BND) notation - the part everyone gets wrong. A rearrangement whose two sides are not a simple same-chromosome span is written as PAIRED BND records linked by MATEID, with an ALT string whose bracket direction encodes the join orientation:

2   321681  bnd_V  T  ]13:123456]T   MATEID=bnd_U
13  123456  bnd_U  A  A[2:321681[    MATEID=bnd_V

One reciprocal translocation is 2 BND records; chromothripsis is a graph of dozens. The SAME biological inversion can appear as <INV> in Manta, as 2+ BND records in GRIDSS, and as a DEL+DUP artifact pair in a naive RD caller - a merger that does not understand this triple-counts or drops the event.

Manta

Manta builds a genome-wide breakend association graph from RP+SR, then does targeted local assembly of each candidate breakend and realigns the contig for base-resolution breakpoints. candidateSmallIndels.vcf.gz is the recommended indel-candidate input for Strelka2.

configManta.py --bam sample.bam --referenceFasta reference.fa --runDir manta_run
manta_run/runWorkflow.py -j 8
# results/variants/: diploidSV.vcf.gz (germline), candidateSV.vcf.gz (unscored superset),
#   candidateSmallIndels.vcf.gz (Strelka2 input)

WES/panel and RNA need explicit modes - default depth filtering assumes WGS-uniform coverage and silently drops true SVs at high-depth targeted loci:

# --exome disables the high-depth filter; --rna handles split alignments across splice junctions
configManta.py --bam sample.bam --referenceFasta reference.fa --exome \
    --callRegions regions.bed.gz --runDir manta_exome

Tumor-normal somatic mode adds somaticSV.vcf.gz:

configManta.py --tumorBam tumor.bam --normalBam normal.bam \
    --referenceFasta reference.fa --runDir manta_somatic
manta_somatic/runWorkflow.py -j 8

DELLY

BCF output by default; the scalable cohort pattern is site-list-then-regenotype (Section below), not one big multi-BAM call.

delly call -g reference.fa -o sv_calls.bcf sample.bam
bcftools view sv_calls.bcf > sv_calls.vcf

# Per-type calling (INS recovers only small, SR-anchored insertions)
delly call -t DEL -g ref.fa -o deletions.bcf sample.bam   # also DUP, INV, BND, INS

# Somatic: call tumor+normal, then classify with a sample sheet
delly call -g reference.fa -o svs.bcf tumor.bam normal.bam
printf 'tumor\ttumor\nnormal\tcontrol\n' > samples.tsv
delly filter -f somatic -o somatic_svs.bcf -s samples.tsv svs.bcf

smoove (LUMPY, with genotyping + depth annotation)

Nobody runs raw lumpyexpress anymore. smoove wraps LUMPY + svtyper + duphold with sane defaults and a cohort workflow. duphold annotates each DEL/DUP with three depth fold-changes: over the whole event (DHFC), over GC/mappability-matched bins (DHBFC), and over the immediate flanks (DHFFC) - a cheap, high-value filter for RP-only calls lacking depth support. duphold's own recommended false-positive filters are DHFFC < 0.7 for deletions (flanking fold-change, its most robust metric) and DHBFC > 1.3 for duplications.

smoove call --name sample --fasta reference.fa --outdir smoove_out -p 8 sample.bam
# smoove_out/sample-smoove.genotyped.vcf.gz

# Filter RP-only false positives that lack depth corroboration (duphold's recommended fields)
bcftools view -i '(SVTYPE!="DEL" && SVTYPE!="DUP") || (SVTYPE="DEL" && FMT/DHFFC<0.7) || (SVTYPE="DUP" && FMT/DHBFC>1.3)' \
    smoove_out/sample-smoove.genotyped.vcf.gz > smoove.dhfc.vcf

GRIDSS: the raw VCF is a breakpoint graph, not a callset

GRIDSS2 is the only short-read caller doing genome-wide break-end assembly (positional de Bruijn graph) before calling, and the only one reporting single break-ends (SGL) where just one side maps - viral integrations, centromeric/telomeric junctions. But its raw VCF is deliberately noisy low-level breakpoints; it is NOT usable directly. The intended chain is GRIDSS -> GRIPSS (filtering/linkage) -> PURPLE (purity/ploidy/copy-number) -> LINX (interprets breakpoints into chromothripsis, breakage-fusion-bridge, fusions).

# --assembly must be a writable path; reference needs .fai and .dict
gridss --reference reference.fa --output gridss_raw.vcf \
    --assembly gridss_assembly.bam --threads 8 sample.bam

# Somatic: GRIDSS2 tumor+normal, then GRIPSS filtering
gridss --reference reference.fa --output gridss_raw.vcf --assembly asm.bam \
    --labels normal,tumor --threads 8 normal.bam tumor.bam
# GRIPSS (launched as `java -jar gripss.jar` in practice) also requires PON inputs
# (-pon_sgl_file, -pon_sv_file, -known_hotspot_file); confirm the full flag set with its docs.
gripss -sample tumor -reference normal -ref_genome reference.fa \
    -ref_genome_version 38 -pon_sgl_file sgl.pon -pon_sv_file sv.pon \
    -vcf gridss_raw.vcf -output_dir gripss_out/

Genotyping is not discovery (do NOT union discovery VCFs into a population matrix)

Discovery answers "is there an SV here, and what/where?"; genotyping answers "what is each sample's GT (0/0, 0/1, 1/1) here?". These are different statistical problems. A sample recorded 0/0 in a discovery-VCF union may simply not have had that event DISCOVERED in it - a false "missing", not a true reference genotype. Building an allele-frequency-quality cohort matrix requires FORCE-GENOTYPING every sample at every discovered site.

Correct cohort workflow: discover per-sample -> merge to a non-redundant SITE list -> re-genotype ALL samples at ALL sites -> merge genotypes.

Genotyper	Model	Use when
svtyper (in SpeedSeq, Chiang 2015)	Bayesian RP+SR at known breakpoints	LUMPY/smoove sites; DEL/DUP/INV/BND (NOT insertions)
Paragraph (Chen 2019)	realign reads to a per-SV sequence graph (ref+alt paths)	Modern short-read choice; genotypes INS (alt path contains the inserted bases)
GraphTyper2 (Eggertsson 2019)	joint SNV+SV over a pangenome graph	N in the tens of thousands (genotyped 49,962 Icelanders)

# DELLY's native squared-off pattern: per-sample call -> merge SITES -> regenotype at sites
delly call -g ref.fa -o s1.bcf s1.bam        # ... one per sample
delly merge -o sites.bcf s1.bcf s2.bcf s3.bcf
delly call -g ref.fa -v sites.bcf -o s1.geno.bcf s1.bam   # regenotype each at the union sites
bcftools merge -m id -Ob -o cohort.bcf s1.geno.bcf s2.geno.bcf s3.geno.bcf

Population merging: the merge parameters ARE the result

"The same SV" across samples/callers is a fuzzy concept because breakpoints disagree by CIPOS margins. Position-only merging inflates allele frequency by up to 2.2x versus sequence-aware merging, because it collapses distinct nearby alleles into one (English 2022 Genome Biol 23:271). For any AF-dependent analysis (constraint, association, catalogs), always report the merger and its parameters.

Merger	Matching	Use when
SURVIVOR (Jeffares 2017)	position + type + strand agreement, min-callers; NOT sequence-aware	Fast caller-consensus on ONE sample; 1000 bp max_dist cheerfully merges two different 300 bp DELs 800 bp apart
Jasmine (Kirsche 2023)	(breakpoint, length) proximity graph via KD-tree + constrained Kruskal	Long-read cohort merging at population scale
Truvari collapse (English 2022)	SEQUENCE-aware (refdist + size + alt-sequence similarity)	Any AF work or population catalog where allelic diversity must be preserved

# SURVIVOR: max_dist=1000 min_callers=2 type_agree=1 strand_agree=1 est_dist=0 min_size=50
ls manta.vcf delly.vcf gridss.vcf smoove.vcf > vcf_list.txt
SURVIVOR merge vcf_list.txt 1000 2 1 1 0 50 merged.vcf   # single-sample caller consensus only

Benchmarking: an SV F1 is meaningless without its Truvari parameters

Every SV F1/recall/precision figure is a function of the matching parameters; papers routinely report incomparable numbers. A Truvari bench true positive must match a truth variant under ALL of:

Flag	Default	What loosening it does
--refdist	500	larger -> RP-only IMPRECISE calls start matching
--pctsize	0.70	lower -> size-sloppy calls pass (reciprocal size similarity)
--pctseq (was --pctsim)	0.70	0 disables alt-sequence checking entirely - the quiet trick that inflates short-read INS scores
--sizemin / --sizemax	50 / -	choosing the window can hide a caller weak at one size class

# Report EVERY parameter; --pctseq 0 would make this uninterpretable
truvari bench -b truth.vcf.gz -c calls.vcf.gz -o bench_out/ --passonly \
    --refdist 500 --pctsize 0.70 --pctseq 0.70 --sizemin 50

Three escalating bars are routinely conflated: (1) event detection (loose, RP-only callers pass), (2) breakpoint accuracy (tight --refdist, only SR/assembly pass; matters at exon/splice boundaries), (3) genotype accuracy (add genotype-aware comparison; a caller can detect an event yet call het-as-hom, fatal for Mendelian work). Also stratify by region: run GIAB-CMRG (Wagner 2022 Nat Biotechnol 40:672), not just Tier 1 - CMRG covers the medically relevant repetitive genes Tier 1 EXCLUDES, and false duplications in GRCh37/38 (e.g. CBS, KCNE1) cause reference-specific misses that masking raised from 8% to 100% recall.

Filter and annotate

bcftools view -i 'ABS(SVLEN) >= 50' svs.vcf > svs.min50.vcf   # ABS() is mandatory (DEL sign); see examples/svlen_sign_filter.py
bcftools view -i 'SVTYPE="DEL"' svs.vcf > deletions.vcf        # or INS/INV/DUP/BND
bcftools view -f PASS svs.vcf > svs.pass.vcf

AnnotSV -SVinputFile svs.vcf -genomeBuild GRCh38 -outputFile annotated_svs
# gene overlap, DGV/gnomAD-SV population AF, ClinVar pathogenicity

Long reads: near-direct observation

A long read (HiFi ~15-25 kb, ONT tens of kb to Mb) physically spans the SV and both flanks in one molecule, converting inference-over-fragments into near-direct observation: INS become trivial (the read carries the inserted bases), repeat-mediated SVs resolve, and heterozygous variants on one read are natively phased. Switch to long reads when INS/complex/repeat SVs or phased haplotyping matter more than per-sample cost.

Caller	Approach	Best for
Sniffles2 (Smolka 2024)	signature + per-sample .snf population merge	ONT/HiFi general; mosaic/low-VAF SVs; linear-in-N cohorts
cuteSV (Jiang 2020)	signature clustering + refinement	Highest recall on noisy ONT
pbsv	official PacBio, tandem-repeat-aware	HiFi (pair with pbmm2 alignments)
SVIM (Heller 2019)	reports origin AND destination of duplications	tandem-vs-interspersed DUP discrimination
dipcall (Li 2018) / PAV (Ebert 2021)	assembly-vs-reference from phased haplotype assemblies	highest-quality callsets and truth sets
Severus (Keskus 2025)	phased somatic breakpoint graph	complex somatic rearrangements from long reads

# minimap2/pbmm2 -> sort -> caller. Sniffles2 population design: per-sample .snf, then combine
sniffles --input sample.bam --reference reference.fa --snf sample.snf
sniffles --input s1.snf s2.snf s3.snf --vcf population.vcf   # combine scales linearly in N

Common Errors

Symptom	Cause	Fix
Default Manta on WES/panel loses true SVs	high-depth filter assumes WGS-uniform coverage	add --exome
Cohort "0/0" genotypes wrong; AF too low	took a UNION of discovery VCFs	force-genotype at merged sites (Paragraph/GraphTyper2/delly -v)
AF up to 2.2x too high vs another catalog	position-only merge (SURVIVOR-1000)	use sequence-aware Truvari for AF work
ABS(SVLEN)>=50 filter drops all deletions	filtered raw SVLEN (DEL is negative)	always wrap in ABS()
Zero INS recall	LUMPY/smoove has no INS representation	use an assembly caller or long reads
Short-read INS recall "only ~40%"	benchmarked vs a long-read truth set	physics limit, not caller fault; ~30-50% is the ceiling
GRIDSS raw VCF looks like noise	it is a breakpoint GRAPH, not a callset	run GRIPSS -> PURPLE -> LINX
-H 1 haplotype consensus is chimeric	genotypes were unphased	phase first (see variant-calling/consensus-sequences)
Truvari F1 not reproducible	reported without parameters	state refdist/pctsize/pctseq/sizemin and whether GT was compared

Related Skills

• copy-number/cnvkit-analysis - read-depth CNV detection for dosage changes with breakpoints in unmappable repeats (complements junction-based SV callers)
• long-read-sequencing/structural-variants - full long-read SV pipelines (Sniffles2, cuteSV, pbsv, assembly-based)
• variant-calling/consensus-sequences - applying variants to a reference; phasing before -H haplotype extraction; why symbolic SVs are not consensus-able
• variant-calling/vcf-manipulation - view, query, and reshape SV VCFs
• variant-calling/filtering-best-practices - general VCF filtering principles applicable to SV callsets
• variant-calling/variant-annotation - functional annotation of SVs (gene overlap, population AF, pathogenicity)
• alignment-files/alignment-filtering - BAM preparation and quality filtering before SV calling

References

• Mahmoud M, Gobet N, Cruz-Davalos DI, Mounier N, Dessimoz C, Sedlazeck FJ. Structural variant calling: the long and the short of it. 2019 Genome Biology 20:246.
• Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Kallberg M, Cox AJ, Kruglyak S, Saunders CT. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. 2016 Bioinformatics 32:1220-1222.
• Rausch T, Zichner T, Schlattl A, Stutz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. 2012 Bioinformatics 28:i333-i339.
• Layer RM, Chiang C, Quinlan AR, Hall IM. LUMPY: a probabilistic framework for structural variant discovery. 2014 Genome Biology 15:R84.
• Cameron DL, Schroder J, Penington JS, Do H, Molania R, Dobrovic A, Speed TP, Papenfuss AT. GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly. 2017 Genome Research 27:2050-2060.
• Cameron DL, Baber J, Shale C, Valle-Inclan JE, Besselink N, van Hoeck A, et al. GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing. 2021 Genome Biology 22:202.
• Wala JA, Bandopadhayay P, Greenwald NF, O'Rourke R, Sharpe T, Stewart C, et al. SvABA: genome-wide detection of structural variants and indels by local assembly. 2018 Genome Research 28:581-591.
• Abyzov A, Urban AE, Snyder M, Gerstein M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. 2011 Genome Research 21:974-984.
• Chiang C, Layer RM, Faust GG, Lindberg MR, Rose DB, Garrison EP, Marth GT, Quinlan AR, Hall IM. SpeedSeq: ultra-fast personal genome analysis and interpretation. 2015 Nature Methods 12:966-968. (introduces the svtyper genotyper)
• Chen S, Krusche P, Dolzhenko E, Sherman RM, Petrovski R, Schlesinger F, et al. Paragraph: a graph-based structural variant genotyper for short-read sequence data. 2019 Genome Biology 20:291.
• Eggertsson HP, Kristmundsdottir S, Beyter D, Jonsson H, Skuladottir A, Hardarson MT, et al. GraphTyper2 enables population-scale genotyping of structural variation using pangenome graphs. 2019 Nature Communications 10:5402.
• Jeffares DC, Jolly C, Hoti M, Speed D, Shaw L, Rallis C, Balloux F, Dessimoz C, Bahler J, Sedlazeck FJ. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. 2017 Nature Communications 8:14061. (introduces SURVIVOR)
• Kirsche M, Prabhu G, Sherman R, Ni B, Battle A, Aganezov S, Schatz MC. Jasmine and Iris: population-scale structural variant comparison and analysis. 2023 20:408-417.