What Makes SelfDecode WGS Different from Other Providers?
What Makes SelfDecode's Whole Genome Sequencing Different?
SelfDecode's Whole Genome Sequencing (WGS) — also known as Whole Genome Analysis — is built to clinical-grade standards at every step, from sequencing depth and methodology through variant calling, filtering, and AI interpretation. Most consumer genomics providers cut corners along the way; we don't.
For a primer on what Whole Genome Sequencing is and what's included, see What Is Whole Genome Sequencing (WGS)?
How SelfDecode Compares
How we compare on the factors that actually matter:
| Factor | Many Competitors | SelfDecode |
|---|---|---|
| Sequencing type | Exome, arrays, or imputed low-pass | True whole genome |
| Coverage depth | 10x or inconsistent "up to 30x" | True 30x+ |
| Read type | Single-end (some providers) | Paired-end |
| Variant caller | bcftools or unvalidated proprietary tools | DRAGEN |
| Reference genome | GRCh37 (outdated) | GRCh38 (current) |
| Validation | Self-reported or none | GIAB + PrecisionFDA benchmarking |
| Quality filtering | Minimal or absent | Carefully calibrated, validated |
| Data delivery | Bloated, unfiltered, undocumented | Filtered, annotated, documented |
| AI interpretation | Raw data fed to generic AI | Validated data + purpose-built framework |
What "Whole Genome Sequencing" Actually Means
Not all whole genome testing is equal. Across the industry, the term has become a marketing label that covers several very different products:
- Exome sequencing — Covers only ~1–2% of the genome (the protein-coding regions). Misses regulatory regions, structural variants, and non-coding mutations increasingly linked to disease.
- Genotyping arrays — Not sequencing at all. Tests a predefined list of known variants (e.g., 23andMe). Cannot detect rare or novel variants by design.
- Low-pass sequencing with imputation — Sequences at very shallow depth (0.5x–4x) and statistically "fills in" the rest using reference population data. The result looks like whole genome data but is largely inferred. Accuracy varies significantly by ancestry and is least reliable for non-European customers.
SelfDecode performs true Whole Genome Sequencing — the full genome, no shortcuts.
Why 30x Coverage Is the Standard
Clinical-grade whole genome work is performed at 30x mean coverage, meaning each position in the genome is independently read about 30 times. This redundancy is what allows confident, accurate variant calls.
At 10x (commonly marketed as a "budget" option):
- Heterozygous variant accuracy drops meaningfully
- Many clinically important regions receive fewer than 5 reads
- False positive and false negative rates increase significantly
Some providers also advertise "30x" but deliver as little as 10x–30x with no minimum floor, meaning the data isn't suitable for rare variant detection. We use true 30x+ coverage across the full genome.
Paired-End Sequencing
SelfDecode uses paired-end sequencing, reading both ends of each DNA fragment. This is required for:
- Reliable PCR duplicate removal (single-end reads cannot support this)
- Accurate mapping in repetitive genome regions
- Structural variant detection
Single-end sequencing inflates apparent coverage while reducing true information content and introducing systematic calling errors.
Variant Calling: Where Most Pipelines Fail
Turning raw sequencing reads into a usable variant list is the most consequential step in the pipeline — and the most commonly compromised. Common industry issues:
| Problem | Impact |
|---|---|
| Old reference genome (build 37/hg19) | Missed variants, mislocalized calls, incompatibility with modern databases |
| Weak variant callers (e.g., bcftools-only) | No longer accepted for clinical-grade WGS; lower accuracy than current tools |
| Unvalidated proprietary algorithms | No external benchmarking — no way to verify accuracy |
| Population bias | Pipelines optimized on European-ancestry data; higher error rates for other ancestries |
We use DRAGEN, the industry-leading variant caller used in clinical laboratories worldwide, called against GRCh38 (the current standard reference genome). Our pipeline is benchmarked against truth sets from the Genome in a Bottle Consortium and PrecisionFDA challenges. We know our sensitivity and specificity numbers because we've measured them.
Quality Filtering
Raw variant calls always contain errors. Proper filtering removes artifacts; improper filtering either lets them through or removes real variants.
Providers who skip this step deliver bloated, unfiltered files that look comprehensive but contain significant noise. Feeding unfiltered data to any AI system — including an excellent one — produces confident-sounding analysis built on artifacts.
SelfDecode delivers:
- Carefully calibrated quality filters, tested across different ancestral backgrounds and genomic contexts
- Full quality metrics retained (read depth, mapping quality, allele balance, genotype quality scores)
- Clean, compressed, annotated output — not a 4–20 GB unfiltered dump
QA/QC at Every Stage
We run quality checks at every step: sample receipt, library prep, sequencing, alignment, variant calling, filtering, and annotation. Metrics monitored include mean coverage, coverage uniformity, duplicate rate, contamination estimates, sex concordance, and ancestry-informed checks. Samples that don't meet standards are flagged and reprocessed.
AI Interpretation Built on Validated Data
AI analysis is only as good as the data underneath it. An LLM analyzing a variant list full of false positives will produce well-formatted, citation-backed, authoritative-sounding recommendations — built on variants that don't exist.
Our AI operates on data that has already passed through a validated calling and filtering pipeline, annotated with current clinical databases. Our AI framework:
- Enforces evidence standards and flags genuine uncertainty
- Cross-references multiple databases
- Distinguishes established pathogenic variants from speculative associations
- Avoids overstating confidence or building risk assessments on artifacts
The result is a complete, validated chain: sequencing → variant calling → filtering → annotation → interpretation. Each link has been rigorously tested.
Questions?
Email support@selfdecode.com.