Exploring our family’s genomes with Claude Science

Published October 3, 2026

A couple of years ago, I obtained our whole-genome sequencing data from GeneDx—for my wife, our son and myself. Their analysis, ordered by UCSF, had already identified the likely explanation for our son’s rare neurodevelopmental condition: a de novo variant in SPTAN1. I wanted to understand more about it, and whether that understanding could eventually help us pursue a treatment.

I had the files and the clinical background to ask questions. What I lacked was a workable way to explore the data. Claude Science helped bridge that gap, answer several questions I had about our data, and ultimately assemble material for discussions with our neurogeneticist and the n-Lorem Foundation about a potential future antisense oligonucleotide (ASO) therapy.

Getting to grips with the data

Before Claude Science, I tried grappling with open-source command-line tools such as bcftools and subsequently hired two bioinformaticians from top schools through Upwork. They found it challenging to reproduce GeneDx’s variant-analysis workflow, and I still had unanswered questions and uncertainty about how complete the analysis was. After watching the Claude Science launch webinar, I gave it a try.

Our data included VCF files, which list called genetic variants, and three 25 GB CRAM files, which retain the underlying sequencing reads. A variant call tells you what the analysis detected; the reads help you check the evidence.

With all three family members’ data—a trio—we could compare our son’s variants with ours, then examine candidates that appeared absent from both parents. Claude helped establish a workflow I could follow, question and refine.

Questions to help answer

The questions ranged from checking known findings to exploring possible routes to treatment:

1. Could we confirm the SPTAN1 variant in the sequencing data and identify any other potentially notable de novo variants in our son that GeneDx might not have called or reported?
2. Did our son have significantly more de novo single-nucleotide variants (SNVs) than expected? Around 60–70 per person is a commonly cited benchmark from short-read studies, although the expected count depends on parental ages and how the data are analysed.
3. Could we infer whether the SPTAN1 mutation arose in the egg or sperm—and distinguish what the data could tell us about its parental origin from when it arose?
4. Did any of us carry variants classified in ClinVar as pathogenic or likely pathogenic that warranted a closer look?
5. Did I have a variant associated with Gilbert’s syndrome that might explain my isolated, mildly raised bilirubin on routine blood tests over the years?
6. Could we corroborate the expected absence of reportable ACMG secondary findings, which clinical sequencing labs are recommended to assess when patients consent? And did our WGS results agree with our Natera Horizon prenatal carrier screening results where the tests overlapped?
7. Finally, and perhaps most importantly, what could our WGS data tell us about whether our son’s variant might be amenable to gene editing or an ASO approach?

With our neurogeneticist’s guidance, that final question became more focused: could we build a case for an ASO that selectively reduced RNA from the variant-bearing copy, for n-Lorem to consider?

Working through the questions

1. Could we confirm the SPTAN1 variant in the data and find other notable variants?

The analysis recovered the known SPTAN1 c.6958A>G variant: a change from A to G at position 6958 of the coding sequence, using the report’s reference transcript, NM_001130438. Its predicted protein change is p.(Arg2320Gly), or R2320G—arginine replaced by glycine at position 2320. The diagnostic credit belongs to GeneDx.

It also identified a second candidate de novo variant that changes a protein’s amino-acid sequence, in SNRNP35. The report recorded 17 variant-supporting reads out of 41 in our son, with none among 31 maternal or 32 paternal reads. Checking the underlying CRAMs provided stronger evidence than simply finding no matching variant in the parental VCFs.

Getting there involved a correction. An earlier Claude analysis had incorrectly concluded that all his SNRNP35 variants were inherited. A follow-up report traced this to comparing across the gene rather than checking the individual position. Re-examining that position supported a de novo finding.

The clinical meaning was less clear. The report described the amino-acid substitution as conservative, with computational predictions of benign or tolerated effects, and judged its likely significance to be low. Those predictions did not establish that it was harmless, nor did the analysis show that GeneDx had missed a disease-causing variant. It gave us another finding to discuss with our neurogeneticist, with independent confirmation still needed.

2. Did our son have more de novo variants than expected?

The dedicated de novo analysis reported 84 high-confidence candidates: 72 single-nucleotide variants and 12 small insertions or deletions (indels). For an SNV-only comparison, the count is 72. The age model used in the graph below includes both SNVs and small indels, so it uses the combined total of 84.

Getting to that count required more than comparing VCF files. The initial comparison produced over 34,000 candidates that appeared unique to our son. Checking the parental CRAMs and applying coverage and allele-balance filters narrowed that list to 84. A variant missing from a parent’s VCF was not necessarily absent from their sequencing reads.

Claude’s report interpreted the resulting count as consistent with the expected range after accounting for parental age—I was 40 at conception—rather than evidence of an unusually high mutation burden. It cited Jónsson et al. (Nature, 2017), a study of 1,548 trios that counted both SNVs and indels. Adding the study’s published paternal and maternal age equations gives an expected total of about 83 at paternal age 40 and maternal age 34.

That remained a research interpretation: the calls and the age-adjusted comparison were not independently validated, and the report identified the indels as the less certain part of the count. A typical overall count would also say little about the effect of an individual variant such as SPTAN1.

For us, the takeaway was reassuring: based on this analysis, our son appeared to have roughly the number of de novo mutations expected for our ages at conception.

3. Could we infer whether the mutation arose in the egg or sperm?

The first step was to ask whether the variant was in our son’s maternal or paternal copy of SPTAN1. We usually inherit one copy of each autosomal region from each parent.

This is called phasing. Sequencing fragments that span both a variant and a nearby informative marker can link them to the same chromosome copy, or haplotype.

Our saved analysis reported twelve informative fragments and provisionally placed the variant on the paternally inherited chromosome. That would not mean I carried and passed on the same mutation. Nor would it establish that the mutation arose in a sperm rather than after fertilization: identifying the chromosome’s parental origin does not, by itself, establish the mutation’s timing.

But a review for this article caught an inconsistency: the report’s diagram and explanation assigned opposite versions of a nearby marker to the variant-bearing chromosome. The underlying reads and parental assignments need to be reconciled before relying on that conclusion. A polished report can still contain a consequential error.

4. Did any of us have notable ClinVar matches?

I also wanted to look beyond our son’s de novo variants and examine all three genomes for variants classified in ClinVar as pathogenic or likely pathogenic that might warrant further review.

5. Could a variant explain my raised bilirubin?

For my own genome, I had a specific question: whether a variant associated with Gilbert’s syndrome might explain the isolated, mildly raised bilirubin that had appeared on routine blood tests over the years.

6. Did the data agree with our clinical screening?

I wanted to check for reportable ACMG secondary findings and compare the overlapping results with our Natera Horizon prenatal carrier screening. These were opportunities to check the analysis against our existing clinical reports.

Could that knowledge help us pursue an ASO?

With our neurogeneticist’s guidance, the work shifted towards a therapeutic question: could selectively reducing the RNA from the variant-bearing copy of SPTAN1 help?

ASOs are short synthetic strands that bind RNA. The approach we explored would aim to lower a particular RNA transcript and, consequently, its protein product. It would leave the DNA unchanged.

The rationale depends on how the variant causes disease. If an altered protein interferes with normal function—a dominant-negative effect—selectively reducing it might help. If the problem is too little functional protein—haploinsufficiency—further reduction could make things worse. Studies of some SPTAN1 variants have shown abnormal spectrin aggregation, but that does not establish the mechanism of our son’s variant.

Claude helped explore that rationale and possible ways to distinguish the two copies. Our neurogeneticist pushed the analysis further, including suggesting a BLAST check for sequence similarity elsewhere in the genome. The follow-up report described megaBLAST and BLAT searches and explored additional markers that might belong to the same haplotype.

These were useful questions to investigate, but genomic similarity searches cannot establish an ASO’s specificity. A real ASO acts on RNA, and its behaviour depends on its sequence and chemistry. Experimental work has shown that imperfect matches can still produce unwanted effects. The unresolved phasing also matters if a proposed target depends on identifying the variant-bearing copy correctly.

Building a case for n-Lorem

The practical output was a more focused report for clinical discussion and n-Lorem’s consideration: the proposed mechanism, possible routes to selective targeting, and questions requiring laboratory work.

n-Lorem develops personalized experimental ASO medicines through a process involving a research physician, scientific review, drug discovery, safety assessment and regulatory oversight. My understanding from our discussions is that we have progressed beyond an initial feasibility step. Further analysis and laboratory work lie ahead; any eventual drug development remains conditional. I’m keeping the case-specific design details private.

As a clinician and a parent, I could bring context and questions. Claude made the computational exploration more accessible, while our neurogeneticist helped sharpen the questions and judge the next steps. Establishing the mechanism, showing benefit and developing a safe treatment remain substantial work.

What changed most was my ability to participate. Files I had struggled to interrogate became a basis for asking more precise questions about our son’s condition—and what we might be able to do next.