Genomics & Variant Analysis
WES capture libraries yield coding SNVs and indels through pinned FASTQ-to-VCF analysis with on-target depth QC, figures, scripts, and a Methods draft [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | Illumina NovaSeq X / 6000 / NextSeq 2000, HiSeq 2500/4000; MGI DNBSEQ-T7 / G400 / G99 when scoped at kickoff; Agilent SureSelect, Twist Exome 2.0, IDT xGen Exome, Roche KAPA HyperExome capture kits when bait/target interval files are provided; Element Biosciences AVITI and Ultima Genomics UG100 when scoped at kickoff |
| Input requirements | Paired-end FASTQ (≥2×100 bp or 2×150 bp typical); germline ≥50-100× on-target depth for research (75-100× mean on-target typical for lab-QC-aligned projects per Rehder et al., 2021); somatic tumor-normal scoped separately (≥200× tumor typical, confirmed at kickoff). Capture kit bait, target, and calling .interval_list files required. Cohort VQSR: typically ≥30 exomes or one high-quality WGS per GATK guidance [4] |
| 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) | BWA-MEM2 2.2.1; GATK 4.6.0.0; Picard 3.2.0; samtools 1.21; bcftools 1.21; mosdepth 0.3.3; Ensembl VEP 112; fastp 0.23.4; FastQC 0.12.1; MultiQC 1.25. DeepVariant 1.8.0 optional |
| Typical turnaround time | 2-4 weeks (single-sample germline); 4-8 weeks (multi-sample cohort with joint genotyping). Confirmed at kickoff |
| Deliverable formats | .bam, .g.vcf.gz, filtered .vcf.gz, hs_metrics.txt, annotation .tsv; PDF/SVG figures; HTML QC report; documented bash/R/Python scripts; Methods draft |
| Key cited best-practice reference | Van der Auwera et al. [5], Current Protocols in Bioinformatics; Rehder et al. [2], Genetics in Medicine; Kong et al. [3], Genetics in Medicine |
| Custom / bespoke analysis | Non-standard inputs, outputs, and methods scoped at kickoff, for example client BAMs, custom gene lists, off-target calling, pedigree filtering, GWAS-ready plink exports, or tumor-normal somatic extensions |
WES aligns hybrid-capture-enriched short reads, recalibrates base qualities, and calls SNVs and indels within exome target intervals, not across the full genome. Unlike WGS, WES concentrates depth on coding exons and adjacent intronic regions at lower per-base cost; unlike fixed gene panels, WES surveys most protein-coding genes in one assay [2]. In published clinical vendor benchmarks at ≥120× mean depth, SNV sensitivity was 98.9-99.9% and analytic PPV exceeded 99.1% for SNVs and homozygous indels; heterozygous indels showed lower accuracy [3]. Pepkio starts from FASTQs or client BAMs and returns filtered VCFs with capture-kit-aware on-target QC. Custom inputs and deliverables are agreed at kickoff. See the whole-exome sequencing glossary.
WES is a fit when coding-region SNV/indel discovery is the primary goal and uniform non-coding coverage is not required. The table contrasts WES with WGS and targeted gene panels.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| WES (capture) | Coding-region variant discovery, rare-disease gene identification, cancer predisposition screening at lower sequencing cost than WGS | Gaps in non-coding, mitochondrial, and poorly captured targets; pseudogene mis-mapping in homologous regions [1, 6] | Lower sequencing cost than WGS; capture kit and on-target depth drive sensitivity |
| WGS (short-read) | Non-coding variant discovery, uniform genome-wide coverage, CNV/SV add-ons without capture bias | Higher per-sample sequencing and storage cost than WES | Library prep + sequencing + bioinformatics vary by depth and cohort size |
| Targeted gene panel | Known gene sets, ultra-high depth for low-VAF mosaicism in specific loci | Limited to panel genes; misses novel loci outside design | Lowest per-sample sequencing cost; panel breadth drives sensitivity |
Pepkio verifies FASTQ integrity (MD5 checksums), read length, paired-end structure, and read groups (@RG tags) required by GATK [5]. Capture kit bait, target, and calling intervals are recorded; bait, target, and calling files are not interchangeable [1]. Sample metadata is logged in sample_manifest.csv; sub-threshold yield is flagged before alignment.
Tools used: md5sum; custom validation scripts
Output: sample_manifest.csv with sample IDs, capture kit metadata, interval file paths, read counts, and QC flags
FastQC assesses per-base quality, adapter content, and duplication; fastp trims adapters and low-quality ends when needed. Libraries with low Q30 yield or extreme adapter contamination are flagged before alignment.
Tools used: FastQC 0.12.1; fastp 0.23.4
Output: fastqc/ reports; fastp.json / fastp.html trim statistics; trimmed FASTQs when trimming is applied
Paired-end reads from Illumina, MGI DNBSEQ, or other scoped platforms are aligned to GRCh38 (or agreed build) with BWA-MEM2 using Picard/GATK-compatible settings [4, 10], producing coordinate-sorted BAM. Mapping rate and insert-size distribution are compared against expected ranges for capture libraries.
Tools used: BWA-MEM2 2.2.1; samtools 1.21
Output: {sample}.sorted.bam; alignment summary metrics
PCR and optical duplicates are marked with Picard MarkDuplicates so downstream callers do not double-count clustered molecules [5]. Elevated duplicate rates trigger review of library prep metadata.
Tools used: Picard 3.2.0 MarkDuplicates; samtools 1.21 index
Output: {sample}.dedup.bam and .bai; duplicate metrics table
GATK BaseRecalibrator and ApplyBQSR adjust per-base qualities using known polymorphism sites from the GATK resource bundle [5]. Recalibration reports are inspected for covariate drift across cycles and read groups.
Tools used: GATK 4.6.0.0 BaseRecalibrator; GATK ApplyBQSR
Output: {sample}.recal.bam; recalibration report PDF
Picard CollectHsMetrics and mosdepth report mean target coverage, PCT_TARGET_BASES_20X, PCT_TARGET_BASES_100X, and per-target depth distributions using kit-specific bait and target intervals [2]. On-target breadth, not genome-wide mean depth, determines callability; samples below agreed thresholds are flagged before calling. At 20× stringency, Dillon et al. [7] estimated the likelihood of missing a clinically relevant variant in a phenotype gene list was maximally 8%.
Tools used: mosdepth 0.3.3; Picard 3.2.0 CollectHsMetrics
Output: coverage_summary.csv; hs_metrics.txt; mosdepth.targets.dist.txt; on-target coverage histogram and CDF plots
GATK HaplotypeCaller runs in -ERC GVCF mode with -L capture calling intervals, emitting gVCF blocks restricted to exome targets [4, 5]. DeepVariant 1.8.0 is available as an alternative single-sample caller when scoped at kickoff [11].
Tools used: GATK 4.6.0.0 HaplotypeCaller
Output: {sample}.g.vcf.gz and .tbi; per-sample variant count summary
For multi-sample projects, gVCFs are imported into GenomicsDB and joint-genotyped with GenotypeGVCFs over capture intervals, rescuing variants weakly supported in individual samples [5]. Single-sample projects skip this step.
Tools used: GATK 4.6.0.0 GenomicsDBImport; GATK GenotypeGVCFs
Output: {cohort}.joint.vcf.gz; GenomicsDB workspace; sample count and site-level summary
When cohort size supports it, GATK VariantRecalibrator applies VQSR [4]; smaller cohorts use documented GATK hard filters. Dataset-specific genotype-quality filters may supplement VQSR when scoped [12].
Tools used: GATK 4.6.0.0 VariantRecalibrator / ApplyVQSR or VariantFiltration; bcftools 1.21
Output: {cohort}.filtered.vcf.gz; {cohort}.pass-only.vcf.gz; VQSR tranche plots or hard-filter summary
Ensembl VEP annotates consequences, gene symbols, and population or clinical fields when reference databases are configured [13]. Known pseudogene-prone loci (for example SMN1, CYP21A2, PKD1, STRC) are flagged for manual review when variants are reported [1, 14]. MultiQC aggregates QC metrics; final scripts, README, Methods draft, and HTML QC report are packaged per agreed retention policy.
Tools used: Ensembl VEP 112; bcftools 1.21; MultiQC 1.25
Output: variant_annotation_master.tsv; MultiQC report; final deliverable bundle with scripts and Methods draft
For germline SNV/indel discovery on GRCh38, Pepkio recommends ≥50-100× on-target depth for research cohorts and ~75-100× mean on-target depth for lab-QC-aligned projects [2]. VQSR typically requires at least one WGS or ~30 exomes [4]. Thresholds are confirmed at kickoff.
Yes, with caveats. Low Q30 yield or mean on-target depth below ~20× reduce sensitivity for heterozygous indels and rare variants [3]. Sub-threshold samples are flagged; re-sequencing or partial analysis on priority gene lists is discussed at kickoff.
Illumina NovaSeq X, 6000, NextSeq 2000, and HiSeq FASTQs use the standard BWA-MEM2 + GATK workflow. MGI DNBSEQ-T7, G400, and G99 FASTQs are processed when scoped at kickoff with adapter/QC validation in the report. Agilent SureSelect, Twist Exome 2.0, IDT xGen Exome, and KAPA HyperExome require kit-specific interval files. Element AVITI and Ultima UG100 when scoped at kickoff.
Single-sample germline projects typically complete in 2-4 weeks; multi-sample cohorts with joint genotyping and VQSR typically require 4-8 weeks. Timelines are confirmed at kickoff.
Version-pinned pipelines aligned with functional-equivalence principles reduce batch-driven call differences [15]. Pepkio stratifies sequencing center, flowcell, and capture batch in QC reports. Joint genotyping via gVCF aggregation can improve sensitivity within capture targets [5]. Batch-specific correction beyond standard QC is scoped at kickoff.
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 alignment QC, on-target coverage assessment, and before final delivery. A PhD-level scientific contact leads the project.
Methods clarification, QC thresholds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Methods and Supplementary drafts included; substantial new requests scoped separately.
No. Pepkio does not require co-authorship unless explicitly discussed. Acknowledgment of bioinformatics support is standard practice.
Yes. Bait, target, and calling .interval_list files must match your capture kit version [1]. Mismatched intervals produce incorrect coverage and calls. Kit metadata is documented in the Methods draft.
Default calling is restricted to capture intervals. Off-target or genome-wide calling is a separately scoped milestone; depth and sensitivity outside targets are documented in the QC report.
Tumor-normal somatic exome and custom workflows are scoped at kickoff, including client BAMs, custom gene lists, pedigree filtering, plink exports, or CNV integration. Somatic projects typically require ≥200× tumor on-target depth and distinct callers.
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