Genomics & Variant Analysis
CNV and SV calling from BAM depth and split-read evidence returns segment tables, SV VCFs, and gene overlaps with pinned Manta or DELLY workflows [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | Pre-aligned BAMs or CRAMs from Illumina NovaSeq X / 6000 / NextSeq 2000, HiSeq 2500/4000; MGI DNBSEQ (BGI) short-read WGS/WES; Element Biosciences AVITI and Ultima Genomics UG100 when scoped at kickoff. WGS preferred; WES capture-aware. Re-alignment from FASTQ scoped separately (BWA-MEM2 2.2.1) |
| Input requirements | WGS: ≥30× mean autosomal depth and >95% callability recommended [7]. WES: ≥50-100× on-target depth; all callers perform poorly on sparse WES relative to WGS [4]. gCNV cohort model: ≥100 technically matched WGS samples [3]. Smaller cohorts or single-sample: CNVkit 0.9.11 or GATK-SV single-sample mode with a pre-computed reference panel [3] |
| Reference builds supported | Human GRCh38 primary (GATK resource bundle 4.6); legacy GRCh37/hg19 on request; mouse GRCm39; custom references scoped at kickoff |
| Primary tools (with versions) | GATK 4.6.0.0 (GermlineCNVCaller / gCNV); Manta 1.6.0; DELLY 1.2.6; Lumpy 0.2.13 via smoove 0.2.8; CNVkit 0.9.11 when scoped; cn.MOPS 1.56.0 for cohort depth CNVs; samtools/bcftools 1.21; mosdepth 0.3.3; bedtools 2.31.1; Picard 3.2.0; MultiQC 1.25 |
| Typical turnaround time | 2-4 weeks (single-sample from BAM); 4-8 weeks (multi-sample cohort with gCNV model training and multi-caller merge). Confirmed at kickoff |
| Deliverable formats | .cnv.vcf.gz, .sv.vcf.gz, segment .bed/.tsv; PDF/SVG figures; HTML QC report; documented bash/R/Python scripts; Methods draft |
| Key cited best-practice reference | Gabrielaite et al. [4], Cancers; Collins et al. [1], Nature; Mahmoud et al. [2], Genome Biology |
| Custom / bespoke analysis | Non-standard inputs, outputs, and methods scoped at kickoff, e.g. somatic tumor-normal CNV/SV, locus-specific re-calling, client reference panels, array validation overlays, CytoScan comparison tables, or non-standard export formats |
CNV and structural variation analysis segments read-depth profiles to call copy-number gains and losses, then uses discordant read pairs and split reads to detect rearrangements typically ≥50 bp [2, 3]. It measures dosage and large-scale architecture, not SNVs and indels, which need separate variant calling [4]. Pepkio returns annotated segment tables and SV VCFs from client BAMs or scoped alignments, with sensitivity limits documented per assay. See the CNV and structural variation glossary.
CNV/SV analysis fits when SNV/indel calls are not enough: undetected dosage changes, translocations, inversions, or a negative WES/WGS SNV panel in rare disease, and when aligned WGS or WES data already exist or are planned.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| CNV/SV from WGS/WES BAMs (this service) | Genome-wide dosage changes, breakpoint-resolved SVs, undiagnosed disease after negative SNV panels | WES misses many non-coding and large CNVs; short-read data often miss complex SVs and repeat-rich loci [4, 5] | Bioinformatics-only; usually lower than a full FASTQ pipeline when BAMs exist |
| SNV/indel variant calling only | Base-pair mutations, cohort SNV harmonization | Does not reliably detect most CNVs or balanced SVs | Lower compute when CNV/SV is not required |
| SNP-array / CytoScan CNV | Clinical-grade CNV at established loci with array validation history | Fixed probe coverage; limited breakpoint resolution and novel SV discovery | Lowest genotyping cost; resolution limited by probe density |
| Long-read SV (separate spoke) | Complex, repeat-rich, phased structural events | Higher per-base sequencing cost than short-read WGS | Library prep + sequencing + bioinformatics vary by platform and depth |
Pepkio verifies MD5 checksums, coordinate-sorted BAM or CRAM structure, indexed .bai/.crai presence, and @RG tags. Reference build, sample sex, assay type, platform, and batch are recorded in sample_manifest.csv. Mismatched builds or missing indices are flagged before calling.
Tools used: md5sum; samtools 1.21 quickcheck; custom validation scripts
Output: sample_manifest.csv with sample IDs, reference build, assay type, platform, input path, and QC flags
mosdepth and Picard CollectWgsMetrics or CollectHsMetrics report mean coverage, pct at ≥15×/≥20×, duplicate rate, and insert-size distribution. Sub-threshold samples are flagged [7, 8].
Tools used: mosdepth 0.3.3; Picard 3.2.0 CollectWgsMetrics or CollectHsMetrics
Output: coverage_summary.csv; mosdepth.global.dist.txt; wgs_metrics.txt or hs_metrics.txt; coverage histogram and CDF plots
Genomic bins match the assay: uniform bins for WGS (GATK defaults start at ~1 kb; larger when scoped) and capture-aware intervals for WES [9]. GC bias and mappability are assessed per bin; blacklisted regions and sex chromosomes are handled per project design.
Tools used: bedtools 2.31.1; mosdepth 0.3.3; custom bin-generation scripts
Output: {project}.wgs_bins.bed or {project}.capture_targets.bed; depth_by_bin.tsv; GC bias diagnostic plots
For cohorts with ≥100 matched germline WGS samples, GATK GermlineCNVCaller trains a cohort gCNV model [3]. Smaller cohorts, somatic projects, or WES use CNVkit or GATK-SV single-sample mode with a pre-computed reference panel [3]. Training samples should match cases in library prep and batch [1].
Tools used: GATK 4.6.0.0 GermlineCNVCaller model training; CNVkit 0.9.11 batch reference build when scoped
Output: {batch}.gcnv_model/ or {project}.cnvkit_reference.cnn; model training QC report; cnv_reference_manifest.csv
GATK gCNV infers copy-number states from cohort-trained depth models; CNVkit applies GC correction and CBS segmentation [9]; cn.MOPS models read counts across cohort samples [10]. Per-sample CNV counts are checked before merging.
Tools used: GATK 4.6.0.0 PostprocessGermlineCNVCalls; CNVkit 0.9.11 batch / call; cn.MOPS 1.56.0 when scoped
Output: {sample}.gcnv_calls.vcf.gz; {sample}.cnvkit.cns; {sample}.cnmops_segments.bed; per-sample CNV count summary
Manta and DELLY call SVs from discordant pairs, split reads, and depth evidence by default; Lumpy (via smoove) is added when scoped [11, 12, 13]. Per-caller VCFs retain QUAL, FILTER, and evidence tags for merge decisions.
Tools used: Manta 1.6.0; DELLY 1.2.6; smoove 0.2.8 (Lumpy 0.2.13) when scoped
Output: {sample}.manta.sv.vcf.gz; {sample}.delly.sv.vcf.gz; {sample}.lumpy.sv.vcf.gz when scoped; per-caller variant count summaries
CNV segments and SV calls are merged using reciprocal overlap thresholds agreed at kickoff [4]. Size, quality, and bin-count filters are tuned per assay [9].
Tools used: bcftools 1.21; bedtools 2.31.1; SURVIVOR 1.0.6 or custom merge scripts when scoped
Output: {cohort}.merged_cnv.bed; {cohort}.merged_sv.vcf.gz; cnv_sv_filter_summary.csv; caller overlap Venn data
CNV segments and SV breakpoints are intersected with Ensembl/GENCODE gene models. gnomAD-SV allele frequencies and ClinVar overlap are added when reference databases are configured [1].
Tools used: bedtools 2.31.1; bcftools 1.21; Ensembl BioMart or custom annotation scripts
Output: cnv_gene_overlap.tsv; sv_annotation_master.tsv; annotated .vcf.gz when requested
Per-sample CNV burden, SV type breakdown, and size distributions are compared across the cohort. Outlier call counts or batch clustering trigger review before delivery [4]. CytoScan or MLPA truth sets are overlaid when clients provide coordinates.
Tools used: custom R/Python QC scripts; bcftools 1.21 stats
Output: cnv_sv_qc_summary.csv; CNV burden and SV type bar charts; batch stratification report
MultiQC aggregates coverage, CNV, and SV metrics across samples. Final scripts, README, Methods draft, and HTML QC report are packaged per agreed retention policy.
Tools used: MultiQC 1.25
Output: MultiQC report; final deliverable bundle with scripts, figures, and Methods draft
For germline CNV/SV from WGS BAMs, Pepkio recommends ~30× mean autosomal depth with >95% callability [7]. WES requires ≥50-100× on-target depth [4]. Sarwal et al. [15] found Manta and LUMPY among the highest F-scores for deletion calling. Thresholds are confirmed at kickoff.
Yes, when on-target depth and capture-kit metadata are documented. WES misses many off-target events that WGS detects [4]. Pepkio applies capture-aware CNVkit binning when scoped and documents sensitivity limits in the Methods draft.
Sometimes, with caveats. Mean depth below ~20× on WGS or sparse on-target coverage on WES can reduce CNV segment confidence and SV evidence counts [7]. Sub-threshold samples are flagged before calling; re-sequencing is discussed when feasible. Partial calling on agreed chromosomal regions or gene lists is possible when full-genome CNV/SV is not justified.
Pepkio processes pre-aligned BAMs from Illumina and MGI DNBSEQ (BGI) platforms when @RG tags and reference build are documented. Element AVITI and Ultima UG100 BAMs are accepted when scoped at kickoff. If only FASTQs are available, re-alignment with BWA-MEM2 2.2.1 is scoped separately or via the WGS/WES spokes.
Single-sample CNV/SV calling from client BAMs typically completes in 2-4 weeks; multi-sample cohorts with gCNV training and merge typically take 4-8 weeks. Timelines depend on sample count, assay, and scope, and are confirmed at kickoff.
CNV depth models are sensitive to library prep, sequencer, and batch-specific coverage biases [1, 3]. Pepkio trains separate gCNV models per batch when dosage scores differ, stratifies batch in QC reports, and flags batch-clustered samples. Batch-aware CNVkit reference panels are constructed from matched normals when available.
Pepkio defaults to GATK gCNV (or CNVkit) plus Manta and DELLY because no single tool captures all CNV classes reliably [4]. Lumpy and cn.MOPS are added when scoped. Single-caller workflows are available with documented trade-offs.
Short-read pipelines detect many complex SVs but cannot resolve all breakpoint architectures that long-read sequencing can resolve [5, 6]. Pepkio reports complex SVTYPE calls when assigned; unresolved events are flagged for long-read follow-up when scoped.
Yes. You retain full ownership of code, scripts, and results. Pepkio delivers commented bash, R, and Python scripts with environment lock files. Jupyter or R Markdown delivery is available on request.
Yes. Checkpoint reviews occur after coverage audit, post-calling sanity checks, and before final delivery. A PhD-level scientific contact leads the project and documents decisions at each stage.
Clarification of methods, merge thresholds, QC metrics, and minor figure or table revisions within agreed scope. Methods and Supplementary drafts are included for analyses we performed; substantial new reviewer requests are scoped separately.
No. Pepkio does not require co-authorship unless that is discussed explicitly. Acknowledgment of bioinformatics support is standard practice.
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