Evidence map›Paper›PMID 41342159›Full record

ArticleKidney research and clinical practice2026

The importance and clinical utility of reanalysis of exome and genome sequencing data.

Dongseok Moon, Go Hun Seo

Abstract read
In one paragraph

Article in Kidney research and clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Dongseok MoonMedical Genetics Division, 3billion Inc., Seoul, Republic of Korea.
Go Hun SeoMedical Genetics Division, 3billion Inc., Seoul, Republic of Korea. ghseo@3billion.io.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exome sequencing (ES) and genome sequencing (GS) are essential for diagnosing rare genetic disorders, yet a significant number of patients remain without a definitive diagnosis after the initial analysis. Reanalysis of existing ES and GS data has emerged as a clinically indispensable practice, offering the potential to solve previously unresolved cases by leveraging rapid advances in genomic knowledge and technology. This review comprehensively addresses the growing importance, clinical utility, methodologies, and challenges of ES and GS data reanalysis. The increase in diagnostic yield from reanalysis is driven by several key factors: the continuous discovery of new gene-disease associations, ongoing updates to clinical and population genetic databases like ClinVar and gnomAD, the refinement of bioinformatic pipelines, and the application of advanced analytical techniques. Reanalysis has been shown to provide an additional diagnostic yield ranging from 3% to 15% across various disease cohorts, including neurodevelopmental, renal, and cardiovascular disorders. A significant portion of these new diagnoses stems from the reclassification of variants of uncertain significance, which often leads to direct and meaningful changes in clinical management, including targeted surveillance and tailored therapies. The reanalysis of ES and GS data is no longer a supplementary activity but a fundamental component of modern genomic medicine, transforming genomic testing from a one-time event into a continuous diagnostic process. To fully realize its potential, the development of standardized guidelines is crucial to address financial, logistical, and ethical barriers and to facilitate the equitable integration of reanalysis into routine clinical care.

Indexed as

Exome sequencingGenetic variationPrecision medicineRare diseasesWhole genome sequencing

Identifiers

PMID41342159
PMCPMC13347085

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.