Clinical Exome · ExoSeq Diagnostic Service

Actionable answers for rare genetic disorders.

Clinical Exome Sequencing (ExoSeq) built around ACMG/AMP variant interpretation, physician-facing reporting, and clinical correlation — designed to support referring physicians and genetic counsellors in reaching an actionable diagnosis.

Request Clinical Exome → See the Pipeline
208×
Mean target depth (demo case)
99.84%
Target bases covered ≥ 20×
ACMG
Variant classification standard
Trio
or Proband-only analysis
About the Service

Clinical-grade exome for diagnostic clarity

Unlike research-grade WES, our clinical exome workflow is built around ACMG/AMP variant interpretation guidelines, physician-facing reporting, and clinical correlation.

The test evaluates the protein-coding regions of the genome for pathogenic and likely pathogenic variants relevant to the patient's clinical indication, and is offered as Proband-Only or Trio/Family analysis depending on clinical need.

Every report includes ACMG-classified findings, in-silico pathogenicity predictions, population frequency assessment, and a written interpretive summary correlating genotype with the reported clinical indication.

  • Proband-only or trio/family clinical exome analysis
  • Variant classification per ACMG/AMP guidelines (P, LP, VUS)
  • Annotation against ClinVar, OMIM, gnomAD, 1000 Genomes, CDFD, GME Variome & IndiGen
  • Multi-algorithm in-silico pathogenicity prediction (CADD, PolyPhen-2, SIFT, MutationTaster, PROVEAN)
  • Physician-facing clinical report with key findings & interpretive summary
  • Optional secondary/incidental findings per ACMG SF guidance

ACMG/AMP Classification

Every retained variant is classified as Pathogenic, Likely Pathogenic, or Variant of Uncertain Significance — with supporting evidence reviewed against the patient's clinical indication and family history.

Pathogenic Likely Pathogenic VUS

India-Relevant Annotation

Beyond global databases, variants are assessed against CDFD, GME Variome, and the India-specific IndiGen population database — improving frequency interpretation for the populations you serve.

Learning Path

From first principles to clinical-grade interpretation

New to exomes? Start with Foundations. Running projects or reviewing results? Jump to Practitioner or Expert. Each level builds on the one before it, so you can read this page as a short course or dip in where you need to.

The big idea in plain language: a whole exome reads the parts of your DNA that are used to build proteins, and looks for spelling differences that may explain disease.

LEVEL 1

Genes are recipes, exons are the words

A gene is a recipe for a protein, and the exons are the words that carry it. A change that disrupts a word can abolish the protein — which is how a single spelling change can explain a patient's symptoms.

LEVEL 1

Small region, big payoff

The exome is only about 1–2% of the genome, yet it harbours most known disease-causing variants. One blood draw can therefore screen essentially every known disease gene at once, instead of ordering gene tests one by one.

LEVEL 1

Probes fish out the exons

DNA is cut into fragments, then short probes that match exons are used like hooks to pull those fragments out of the mix. Only the captured fraction is sequenced, which is what makes the depth needed for a clinical call affordable.

LEVEL 1

Reads are puzzle pieces

The sequencer produces millions of short reads. Software maps each one to its place on the human reference genome (hg38), and every difference from the reference is a candidate to be checked against the patient's phenotype.

LEVEL 1

A variant is a difference

Where the patient's sequence differs from the reference, that is a variant — a single letter change (SNV) or a small insertion or deletion (indel). Whether it is carried on one copy (heterozygous) or two (homozygous) decides which inheritance patterns can explain the family history.

LEVEL 1

Annotation adds meaning

A list of coordinates means little. Annotation adds which gene is affected, what changes in the protein, how common the variant is in population databases, and what ClinVar and OMIM say — the evidence a physician-facing report summarises.

For clinicians ordering a test or reading a report: how depth, calling and filtering translate into a result you can act on.

LEVEL 2

Depth is not the whole story

Mean depth tells you the average, but what a report depends on is how many disease-relevant bases reach a usable depth (often judged at 20×). Poor capture uniformity can leave a causative gene effectively untested — the difference between a true negative and a false one.

LEVEL 2

Every call carries a confidence

Each genotype comes with read depth (DP), allele depth (AD) and genotype quality (GQ). Clinical filters typically require adequate depth and GQ, and key variants are checked by eye in the read pileup before they reach a report.

LEVEL 2

Filters you can state

A transparent clinical workflow can be summed up in a sentence: depth at least 20×, genotype quality at least 20, rare in population databases, benign ClinVar entries removed. What remains is reviewed against the indication.

LEVEL 2

Rare variants matter most

Variants common in healthy populations rarely cause rare disease, so population frequency is the first filter. For Indian patients, ancestry-matched databases such as IndiGen matter here: a variant rare globally may be common locally, and vice versa.

LEVEL 2

Family structure is a filter

Sequencing a child with both parents (a trio) shows whether a variant arose de novo or was inherited — and that distinction changes counselling. A de novo variant lowers recurrence risk for future pregnancies; an inherited one defines it, and may reveal a carrier parent.

LEVEL 2

Sanity checks catch problems early

Sample swaps are the error that matters most in a clinical setting. Sex and relatedness checks, contamination estimates and the transition/transversion (Ti/Tv) ratio catch them before any variant reaches a patient report.

For clinical geneticists and computational biologists: classification frameworks, computational evidence, structural limits of the assay, and where the field is moving.

LEVEL 3

ACMG/AMP classification

The 2015 ACMG/AMP guidelines combine 28 evidence criteria into five classes from pathogenic to benign. The classes map directly onto clinical action: pathogenic and likely pathogenic variants can establish a diagnosis, while a VUS generally cannot be acted on alone.

LEVEL 3

Computational evidence

Predictors such as REVEL, CADD, SpliceAI and AlphaMissense contribute supporting evidence — AlphaMissense alone classified about 71 million missense variants (roughly 32% likely pathogenic, 57% likely benign). Under ACMG/AMP they are supporting evidence only; they never override inheritance, frequency or clinical correlation.

LEVEL 3

What an exome cannot see

Exome capture is designed for coding exons, so deep intronic variants, most structural variants and repeat expansions are poorly or not detected. A negative exome is therefore never final: reanalysis, genome sequencing or RNA studies are the follow-up path, chosen with the clinical team.

LEVEL 3

Population resources at scale

gnomAD v4.0 aggregates 807,162 individuals, enabling fine frequency filtering and constraint-based interpretation. For Indian patients, the IndiGen database (1,029 sequenced genomes, ~32% of variants absent from global datasets) reduces false "rare" calls that global frequencies alone would produce.

LEVEL 3

Reanalysis pays off

Knowledge about genes and variants keeps growing: a review of 27 studies reported a median diagnostic yield of about 15% from reanalysis, a median of 22 months after the original test. Set expectations at the time of testing — a negative result today may be resolved on reanalysis later.

LEVEL 3

Secondary findings and consent

Clinical exome programmes may report medically actionable variants unrelated to the indication, from a curated gene list (ACMG SF v3.3, 2025). Whether to receive these secondary findings is an opt-in decision — discuss and document it with the family before testing.

Exome
All protein-coding exons of the genome, about 1–2% of total DNA.
Read depth
How many reads cover a base. Higher depth gives more confident genotypes.
Uniformity
How evenly depth is spread across targets; poor uniformity leaves gaps.
VCF
Standard text file listing variants, genotypes and quality metrics.
Heterozygous
One reference copy and one variant copy of a gene.
De novo
A variant present in the child but absent from both parents.
VUS
Variant of uncertain significance: evidence is insufficient to call it harmful or harmless.
Allele frequency
How often a variant occurs in a reference population such as gnomAD.
The Science

How an exome is captured and read

Whole exome sequencing enriches the protein-coding portion of the genome before sequencing, concentrating reads where disease-causing variants are most often found.

Figure 1 From genome to genotype schematic
1 Genome2 Library3 Hybridise4 Capture5 Sequence exon intron (non-coding) fragments + adapters B biotinylated probe no exon, no probe strept-avidin washedaway variant (3 of 6 reads → het) Exons make up ~1–2%of the genome DNA sheared; adaptersadded to both ends Probes pair withexon-containing fragments Beads pull probes out;the rest is washed away Reads align to hg38;variants are called
Figure 1 | Hybridisation-capture exome sequencing. (1) Exons, the protein-coding segments, form a small fraction of the genome. (2) DNA is fragmented and adapters are ligated for sequencing. (3) Biotinylated probes complementary to exons hybridise to matching fragments. (4) Streptavidin beads pull down probe-bound fragments while off-target DNA is washed away. (5) Enriched fragments are sequenced and aligned; positions where a fraction of reads differ from the reference reveal variants, and the ratio of alternate to reference reads indicates the genotype.
Figure 2 How small is the exome? service stat
Human genome ≈ 3.1 billion basescoding exons ≈ 1–2%
~20,000protein-coding genes
1–2%of the genome sequenced
~21Kcoding variants per sample in our case study
Figure 2 | Scale of the target. Exome capture concentrates sequencing on a sliver of the genome, which is why a high depth is affordable. Green ticks illustrate exon positions and are not to scale.
Figure 3 What kinds of variants are in a typical exome? illustrative
Synonymous 49% Missense 44%
Synonymous (no amino-acid change) Missense (one amino acid changed) In-frame indels, splice region, other (~5%) Predicted loss-of-function (~1–2%)

Most variants in any exome are harmless. The ones that matter for disease are usually the small, rare, protein-altering subset on the right, which is why filtering and annotation are so important.

Figure 3 | Composition of coding variants. Approximate proportions for a typical human exome, shown to convey scale; exact fractions vary by ancestry, capture kit and filters.
Sample Requirements

Specimen & paperwork checklist

Standard specimen requirements are summarized below. Please confirm with our clinical team for patient-specific circumstances.

Parameter Requirement
Sample type Peripheral venous blood (EDTA) or other body fluids; extracted DNA also accepted
Minimum volume / quantity As per clinical team guidance — confirm for paediatric / limited samples
Minimum DNA concentration High-quality genomic DNA preferred; confirm thresholds with clinical team
DNA integrity Samples must pass in-house Sample QC and Library QC
Required paperwork Clinical indication, family history, consent, and referring physician details
Analysis type Proband-only or Trio/Family — specified at order
Shipping / storage Ship on ice / cold pack as appropriate; store blood at 2–8 °C short-term
Rejection criteria Failed Sample QC / Library QC; insufficient quantity or degraded DNA

Exact volume and concentration requirements vary by patient age and sample type. Contact our clinical team at info@nucleomeinfo.com for a tailored submission checklist.

Quantify by fluorometryDye-based quantification reflects double-stranded DNA better than absorbance alone, which also reads RNA and contaminants.
Check purity ratiosClean DNA typically shows A260/280 near 1.8 and A260/230 between about 2.0 and 2.2.
Protect integrityAvoid repeated freeze–thaw cycles and harsh extraction; high-molecular-weight DNA gives better, more even libraries.
Send rich metadataFamily structure, affected status and phenotype terms in the sample sheet drive filtering and interpretation.
Workflow

Validated, ACMG-aligned pipeline

Every clinical exome sample is processed from sample receipt through a physician-facing report.

Figure 4 From sample receipt to signed clinical report infographic
STEP 01

Sample preparation

DNA from EDTA blood or other fluids; only samples passing Sample QC and Library QC proceed.

gDNA
Sample QC
STEP 02

Library & capture

Enzymatic fragmentation and ligation, then capture with the Twist Exome 2.0 kit (38 Mb) plus spike-in probes.

captured library
Library QC
STEP 03

Sequencing

Illumina NovaSeq 6000, 2 × 150 bp paired-end reads.

FASTQ
yield · Q30
STEP 04

Read processing & alignment

Fastp quality filtering, then BWA-MEM alignment to GRCh38.

BAM
alignment rate
STEP 05

Variant calling

GATK v4.x best-practices germline workflow.

VCF
depth · coverage ≥ 20×
STEP 06

Annotation

RefSeq transcripts, ClinVar, OMIM, gnomAD, dbNSFP and population databases including IndiGen.

annotated variants
annotation coverage
STEP 07

Filtration

DP ≥ 20×, GQ ≥ 20, population frequency thresholds; benign ClinVar entries removed.

candidate list
filter audit
STEP 08

ACMG/AMP classification

Pathogenic, likely pathogenic or VUS, reviewed against indication and family history.

classified variants
clinical review
STEP 09

Clinical report

Key findings, interpretive summary and evidence tables, signed and delivered.

PDF report
sign-off
Figure 4 | Gated clinical pipeline. Amber gates mark where metrics are reviewed before a sample proceeds. Samples that fail Sample QC or Library QC are not carried forward.
01

Sample Preparation

DNA extracted from blood (EDTA) or other fluids (Twist kits). Only samples passing Sample QC and Library QC proceed.

02

Library & Capture

Enzymatic fragmentation & ligation (Twist Library Prep); capture with Twist Exome 2.0 Kit (38 Mb) + spike-in probes.

03

Sequencing

Enriched libraries sequenced on Illumina NovaSeq 6000 — 2×150 bp paired-end reads.

04

Read processing & alignment — Fastp quality filtering; BWA-MEM alignment to GRCh38 (hg38).

05

Variant calling — GATK v4.x best-practices germline workflows on GRCh38.

06

Annotation — RefSeq transcripts, ClinVar, OMIM, gnomAD, dbNSFP, and population databases.

07

Variant filtration — DP ≥ 20×, GQ ≥ 20, population AF thresholds; benign ClinVar removed.

08

Clinical classification — ACMG/AMP (P / LP / VUS) reviewed against indication & family history.

09

Reporting — Physician-facing report with key findings, interpretive summary & evidence tables.

Quality Control in Depth

Reading the diagnostic plots like a reviewer

Good exome studies show their quality control. These are the checks that tell you whether reads, coverage and variant calls can be trusted before anyone interprets a single variant.

Figure 5 Per-base sequence quality simulated
Figure 5 | Trimming rescues read ends. Base quality (Phred score) typically falls toward the end of a read. Adapter and quality trimming lifts the mean and narrows the spread. The case study on this page reports 92.25% of bases above Q30.
Figure 6 Cohort sanity check: Ti/Tv ratio simulated
Figure 6 | Transition/transversion ratio. Real variants are enriched for transitions, giving an expected ratio near 2.8 in exomes (about 2.1 genome-wide). Random errors push the ratio toward 0.5, so samples that fall well below the cohort are flagged for contamination or poor library quality.
Figure 7 Interactive coverage simulator model
Mean target depth
Figure 7 | Why mean depth is not enough. The curves show the fraction of target bases covered at least x-fold. A perfectly even library would follow the Poisson curve (green). Real capture is uneven (over-dispersed, orange), so a meaningful share of bases falls below 20× even at high mean depth. Drag the slider to explore. Clinical exome programmes commonly aim for a mean depth around 100× or more with most target bases at 20× or more; the demo case on this page reached 208× with 99.84% of target bases at 20× or more.
Figure 8 Depth and genotype-quality gates simulated
Figure 8 | Why DP and GQ thresholds matter. Each point is a called variant. Calls below 20× depth or with genotype quality below 20 (orange) are held back; green calls pass. Low depth is the main reason genotype quality falls, which is why uniform coverage protects clinically important sites.
Figure 9 Variant evidence in the read pileup simulated
Figure 9 | What a heterozygous call looks like. Reads aligned across a site: about half carry the alternate base (orange), which is the signature of a heterozygous variant. Per-variant read depth, allele depth and quality are listed in the evidence tables of the clinical report.
From Variants to Answers

Finding the needle: filtering and interpretation

A single exome contains tens of thousands of variants. Interpretation is a sequence of filters and evidence weighting that narrows them to a handful of candidates. Nucleome's clinical workflow performs this process and classifies each retained variant per ACMG/AMP, reviewed against the clinical indication and family history.

Figure 10 The filtering funnel illustrative
Coding-region variants calledtypically tens of thousands per exome
~21,000
Protein-alteringmissense, loss-of-function, splice, indels
~10,800
Rare in populationallele frequency below ~1% in gnomAD
~1,100
Predicted damagingconservation, REVEL, CADD, AlphaMissense, SpliceAI
~150
Fits inheritance and phenotypede novo, recessive, X-linked; gene-phenotype match
~8
Candidate diagnostic variant(s)reviewed against ACMG/AMP criteria
1–3
Figure 10 | Each filter removes noise. Bar lengths are on a logarithmic scale. All counts are typical orders of magnitude shown for explanation; real values depend on the filters, indication and family structure.
Figure 11 Inheritance patterns that narrow candidates schematic
De novo (dominant)Autosomal recessiveCompound heterozygousX-linked recessive 0/00/00/1 (new) 0/10/11/1 A/refB/refA and B X/Y refXv/XXv/Y Absent from both parentsTwo copies, one from each carrierTwo different variants in one geneCarrier mother passes to son unaffectedaffectedcarriersquares = male, circles = female
Figure 11 | Family-based filtering. Genotype patterns across a trio sharply reduce the candidate list: de novo variants are absent from both parents; recessive disease needs two altered copies, either homozygous or as two different variants in the same gene (compound heterozygous); X-linked variants pass from carrier mothers to affected sons. Genotypes follow VCF notation (0 = reference, 1 = variant).
Figure 12 ACMG/AMP variant classification guideline framework
Pathogenic≥ 10 points
Likely pathogenic6–9 points
Uncertain (VUS)0–5 points
Likely benign−1 to −6 points
Benign≤ −7 points
Evidence for pathogenicity. Very strong (PVS1, e.g. null variant in a loss-of-function-intolerant gene), strong (PS), moderate (PM, e.g. absent from population databases, hotspot) and supporting (PP, e.g. computational predictions, segregation).
Evidence for benign impact. Stand-alone (BA1, allele frequency too high for the disorder), strong (BS) and supporting (BP, e.g. computational evidence of no impact, synonymous with no splice effect).
Population data

gnomAD v4.0: 807,162 individuals. Variants too common for the disease argue for benign.

Computational predictors

REVEL, CADD, SpliceAI, AlphaMissense. Supporting evidence only; predictors are correlated, not independent.

Clinical and functional data

ClinVar assertions, functional assays, segregation, de novo status and phenotype specificity.

Figure 12 | Combining evidence. The 2015 ACMG/AMP guidelines define 28 criteria that combine into five classes. The point thresholds shown follow the published points-based (Bayesian) reformulation, where supporting, moderate, strong and very strong evidence carry increasing weight. Classification is an expert process that uses phenotype and family information, and in our reports it is reviewed against the patient's clinical indication.
Annotation sourceWhat it addsHow it is used
RefSeqTranscript models and variant consequenceDecides which variants alter a protein
ClinVar & OMIMLaboratory assertions and gene-phenotype linksFlags known pathogenic or benign variants and matches genes to the indication
gnomAD, 1000 GenomesAllele frequencies in large global cohortsRemoves variants too common to cause a rare disorder
CDFD, GME Variome, IndiGenFrequencies in South Asian and Indian populationsImproves frequency interpretation for the populations served
dbNSFPCADD, PolyPhen-2, SIFT, MutationTaster, PROVEAN and other predictorsSupporting computational evidence under ACMG/AMP
Deliverables

Physician-facing clinical report

At the end of testing, referring physicians and patients receive a signed clinical report generated in accordance with ACMG standards.

Clinical Report

Key Findings and Additional Findings tables — gene, variant, zygosity/inheritance, OMIM phenotype, ACMG classification.

Interpretive Summary

Narrative correlation of genotype with reported clinical indication, plus recommendations for confirmatory or follow-up testing.

Variant Evidence

Per-variant read depth, allele depth, quality score, and predicted effect.

Methodology & References

Full description of wet-lab and bioinformatics methodology with literature citations.

QC & Coverage Appendices

Sequencing yield, alignment rate, Q30%, mean depth, target coverage at 1×/5×/20×, and per-gene coding coverage.

Raw Data (on request)

FASTQ / BAM / VCF files for the analyzed sample available upon request.

Case Study

Proband-only clinical exome for a suspected bleeding/metabolic disorder

A proband-only clinical exome was performed for a patient referred for evaluation of a suspected inherited coagulation/metabolic disorder. The exome achieved 100% coding-region coverage across all reported genes.

Sequencing & Coverage Metrics

Proband-only · Twist Exome 2.0 · NovaSeq 6000

Total data generated 34.85 GB
Total reads 114,798,498
Reads aligned 99.98%
Data > Q30 92.25%
Mean target depth 208.49×
Target bases covered ≥ 20× 99.84%

Three likely pathogenic variants were identified (F7, PCCB, NBEAL2) along with four variants of uncertain significance (VWF ×3, F12), each classified per ACMG/AMP guidelines.

Key Findings

F7 Likely Pathogenic LP
PCCB Likely Pathogenic (hom) LP
NBEAL2 Likely Pathogenic LP
VWF VUS ×3 VUS
F12 VUS VUS

The interpretive summary flagged that a single heterozygous LP F7 variant alone does not explain an autosomal recessive bleeding phenotype without a second pathogenic allele, and recommended biochemical confirmation (plasma acylcarnitine profile, urine organic acids) to assess concordance for the homozygous PCCB finding — illustrating how reports pair classification with concrete clinical-correlation guidance.

208×+
Mean target depth
≥ 99.8%
Target bases ≥ 20×
100%
Coding coverage on reported genes
Figure 13 Findings in the demo case, by ACMG/AMP class case study data
3 likely pathogenic 4 uncertain significance
Likely pathogenic: F7, PCCB, NBEAL2 VUS: VWF (3 variants), F12
Classification is only half the story. A heterozygous likely pathogenic F7 variant does not explain a recessive bleeding phenotype without a second allele, so the report recommended biochemical confirmation for the homozygous PCCB finding and correlation with the indication.
Figure 13 | Seven variants, two classes. Counts are taken from the proband-only case study above. No variant was classified pathogenic or benign in this case, and the report pairs each class with clinical-correlation guidance.
The Frontier

What exome sequencing can do, and where it is heading

Published evidence shows that exome sequencing is a first-line diagnostic test for many suspected genetic conditions. Here is how it compares with other approaches, and how new AI tools and larger reference datasets are improving interpretation.

Figure 14 Diagnostic yield in children with suspected genetic disease published data
Genome sequencing
41%
Exome sequencing
36%
Chromosomal microarray
10%
Trios roughly doubled the odds of a diagnosis compared with sequencing the child alone, and the difference between genome and exome yield was not statistically significant.
Figure 14 | Exome vs genome vs microarray. Pooled diagnostic yields from a meta-analysis of 37 studies covering 20,068 children (Clark et al., npj Genomic Medicine, 2018). Newer cohorts and methods may differ.
Figure 15 AI-scored missense variants published data
32% 11% 57%
Likely pathogenic Uncertain Likely benign
71 million possible missense variants were scored by AlphaMissense (Science, 2023). Such scores help prioritise candidates, but are used as supporting rather than stand-alone evidence under ACMG/AMP.
Figure 15 | Proteome-wide missense prediction. Distribution of AlphaMissense classes across all possible human missense variants as reported by its developers. Many variants remain uncertain, which is why functional and clinical evidence still matter.
Table 1 Choosing the right test
ApproachWhat it coversStrengthsLimits
Gene panelA curated list of genes for one conditionHighest depth on few genes, simplest interpretationMisses genes outside the list; needs re-design as knowledge grows
Whole exome (WES)All protein-coding exons (~1–2% of the genome)Hypothesis-free, cost-effective, re-analysable as knowledge growsLimited for deep-intronic, structural and repeat variants; coverage gaps
Whole genome (WGS)Coding and non-coding genomeMore even coverage; detects structural variants and non-coding changesHigher cost and data volume; more variants to interpret
Chromosomal microarrayLarge copy-number changesEstablished for large deletions and duplicationsCannot see single-nucleotide or small indel variants
Table 1 | Test selection. The best choice depends on the clinical question, sample availability and budget. Exome sequencing is often a strong first, hypothesis-free step for suspected genetic disease.
TRIOS

Family-based sequencing

Sequencing parents alongside the child separates inherited from de novo variants and substantially raises diagnostic yield.

REANALYSIS

Re-reading old data

As new gene-disease links appear, re-analysing stored exome data can resolve previously unsolved cases. A review of 27 studies reported a median yield of about 15%.

COPY NUMBER

CNVs from exome depth

Read-depth methods can infer larger deletions and duplications from exome data, within the limits of capture design, and follow ACMG/ClinGen CNV interpretation standards.

STANDARDS

ACMG/AMP is being updated

A draft version 4 of the ACMG/AMP framework is reported to be in pilot, with redesigned evidence codes and continuous point-based scoring. It is not final, so current reports follow the 2015 guidelines.

SPLICING

Predicting splice impact

Deep-learning models such as SpliceAI score whether variants disrupt splicing, and RNA sequencing can confirm suspected splice variants in the right tissue.

POPULATIONS

Ancestry-aware frequencies

Large resources like gnomAD v4.0 improve rare-variant filtering, but allele frequencies should be matched to the ancestry of the individual being tested.

INDIA

IndiGenomes

The IndiGen programme sequenced 1,029 Indian genomes, reporting 55.9 million single-nucleotide variants of which about 32% were not found in global datasets (CSIR-IGIB and CSIR-CCMB, 2020). Population-matched frequencies reduce false "rare" calls.

Scope note. Nucleome's Clinical Exome covers the workflow, ACMG/AMP classification and report deliverables described on this page. The frontier methods here, such as genome sequencing, newer AI predictors and the ACMG/AMP v4 draft, are shown to place the service in its scientific context; contact our clinical team to discuss extended or project-specific needs.
Why Nucleome

Service highlights

Built for referring physicians and genetic counsellors who need actionable, clinically correlated answers.

ACMG/AMP-Compliant

Variant classification and reporting workflow aligned with ACMG/AMP guidelines for sequence variant interpretation.

Broad Annotation

ClinVar, OMIM, gnomAD, 1000 Genomes, CDFD, GME Variome, and the India-specific IndiGen database.

Multi-Algorithm Predictions

CADD, PolyPhen-2, SIFT, MutationTaster, PROVEAN and more — used as supporting ACMG evidence.

High Uniform Coverage

208×+ mean depth with ≥ 99.8% of target bases covered at 20× in our demo case.

Clinical Correlation

Interpretive summaries that explicitly address genotype–phenotype concordance — not just a variant list.

Proband or Trio

Proband-only and trio/family analysis options to support diagnostic yield for each clinical scenario.

Questions

Frequently asked, in plain language

From the basics to the finer points of interpretation.

basicWhat is a clinical exome?
It reads all the protein-coding parts of a person's DNA, the exons, and looks for variants that may explain a medical condition. The exons are only about 1–2% of the genome but contain most known disease-causing variants.
basicProband-only or trio: which should I order?
Proband-only tests the patient alone. A trio also tests both parents, which shows whether a variant arose newly or was inherited and improves the chance of a diagnosis. A published meta-analysis found trios roughly doubled the odds of a diagnosis compared with testing a child alone.
basicWhat will the report contain?
A key findings table, an interpretive summary correlating genotype with the clinical indication, per-variant evidence, methodology with references, and QC and coverage appendices. Raw data are available on request.
intermediateWhat do Pathogenic, Likely Pathogenic and VUS mean?
They are classes from the ACMG/AMP guidelines. Pathogenic and likely pathogenic variants have strong evidence of causing disease. A variant of uncertain significance (VUS) has insufficient or conflicting evidence, so it is reported for transparency and is generally not used on its own for clinical decisions.
intermediateWhy does one heterozygous variant not always explain the condition?
In a recessive disorder both copies of the gene must be affected. A single heterozygous pathogenic variant therefore does not explain the phenotype without a second allele. The case study on this page illustrates exactly this, with biochemical confirmation recommended for a separate homozygous finding.
intermediateWhy are India-specific databases used?
Allele frequency is a key piece of evidence, and global databases under-represent many Indian groups. The IndiGen programme found that about 32% of variants in 1,029 Indian genomes were absent from global datasets, so matched databases help avoid mislabelling population-common variants as rare.
advancedCan a negative exome be final?
Not always. Exome capture targets coding exons, so deep intronic variants, most structural variants and repeat expansions can be missed. Options include reanalysis as knowledge grows, genome sequencing, copy-number analysis or RNA studies, chosen with the clinical team.
advancedHow are in-silico predictors used?
Tools such as CADD, PolyPhen-2, SIFT, MutationTaster and PROVEAN provide supporting computational evidence. Because they are correlated with one another, they count as supporting evidence rather than a verdict, and they never override strong population or clinical data.
advancedHow are secondary findings handled?
They are optional and follow ACMG SF guidance. Whether to report them is a consent decision made with the referring clinical team before testing.
References

Key literature & resources

Selected sources behind the methods and reference values on this page. Figures labelled "simulated", "schematic" or "illustrative" are generated for this page. Case-study figures come from the project described.

  1. Richards S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the ACMG and AMP. Genetics in Medicine 2015;17:405–424.
  2. Tavtigian SV, et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Human Mutation 2020;41:1734–1737.
  3. Zifo. ACMG V4 draft preview: transformative changes in variant classification. 2025 (industry summary of a draft in pilot). Link
  4. Clark MM, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. npj Genomic Medicine 2018;3:16. Link
  5. Evaluating systematic reanalysis of clinical genomic data in rare disease from single center experience and literature review. Molecular Genetics & Genomic Medicine 2020. Link
  6. Chen S, et al. A genomic mutational constraint map using variation in 76,156 human genomes (gnomAD v4). Nature 2024;625:92–100. Release notes: gnomAD v4.0
  7. Jain A, et al. IndiGenomes: a comprehensive resource of genetic variants from over 1000 Indian genomes. Nucleic Acids Research 2021;49:D1225–D1232. News summary: BioSpectrum India
  8. Cheng J, et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 2023;381:eadg7492. Summary
  9. Jaganathan K, et al. Predicting splicing from primary sequence with deep learning (SpliceAI). Cell 2019;176:535–548.
  10. Ioannidis NM, et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. American Journal of Human Genetics 2016;99:877–885.
  11. Rentzsch P, et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Research 2019;47:D886–D894.
  12. Van der Auwera GA, et al. From FastQ data to high-confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Current Protocols in Bioinformatics 2013;43:11.10.
  13. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997, 2013.
  14. Chen S, et al. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018;34:i884–i890.
  15. Liu X, et al. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Medicine 2020;12:103.
  16. Landrum MJ, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research 2018;46:D1062–D1067.
  17. Riggs ER, et al. Technical standards for the interpretation and reporting of constitutional copy-number variants: ACMG and ClinGen. Genetics in Medicine 2020;22:245–257.
  18. ACMG SF v3.3 list for reporting of secondary findings in clinical exome and genome sequencing. Genetics in Medicine 2025. PubMed