Evidence map›Paper›PMID 41963335›Full record

ArticleNature communications2026

Assembling unmapped reads reveals hidden variation in South Asian genomes.

Arun Das, Arjun Biddanda, Rajiv C McCoy, Michael C Schatz

Abstract read
In one paragraph

Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Arun DasDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA. arun.das@som.umaryland.edu.ORCID http://orcid.org/0000-0002-0332-1956
Arjun BiddandaDepartment of Biology, Johns Hopkins University, Baltimore, MD, USA.
Rajiv C McCoyDepartment of Biology, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0003-0615-146X
Michael C SchatzDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-4118-4446

Funding

Implementing the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)U24HG010263 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI Enis Afgan, VINCENT JAMES CAREY · 2018 to 2026
$23.8M
Function and fitness consequences of human genetic variationR35GM133747 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Rajiv Champion McCoy · 2019 to 2026
$3.3M
Tooling for accurately studying the epigenome along the human pangenome referenceU01HG013744 · NHGRI · UNIVERSITY OF WASHINGTON · PI STERGACHIS, ANDREW BEN · 2024 to 2024
$1.4M
Optimized workflows for structural variant analysis of the Kids First genomes using short and long readsR03CA272952 · NCI · JOHNS HOPKINS UNIVERSITY · PI SCHATZ, MICHAEL · 2022 to 2023
$312k
NIGMS NIH HHS R35 GM133747U.S. Department of Health & Human Services | National Institutes of Health (NIH) R03CA272952U.S. Department of Health & Human Services | National Institutes of Health (NIH) R35GM133747U.S. Department of Health & Human Services | National Institutes of Health (NIH) U01HG013744U.S. Department of Health & Human Services | National Institutes of Health (NIH) U24HG010263
6 · The paper itself

Abstract

Conventional genome mapping-based approaches systematically overlook genetic variation, particularly in regions that substantially differ from the reference. To explore this hidden variation, here we examine unmapped and poorly mapped reads from the genomes of 640 human individuals from South Asian populations in the 1000 Genomes Project and the Simons Genome Diversity Project. We assemble tens of megabases of non-redundant sequence in tens of thousands of large contigs, a significant portion of which is present in both South Asian and other populations. We demonstrate that much of this sequence is not discovered by traditional variant discovery approaches even when using complete genomes and pangenomes. Across 20,000 placed contigs, we find 8215 intersections with 106 protein coding genes and over 15,000 placements within 1 kbp of a known GWAS hit. We use long-read data from a subset of samples to validate the majority of their assembled sequences, align RNA-seq data to identify hundreds of unplaced contigs with transcriptional potential, and query existing nucleotide databases to infer the origins of the remaining unplaced sequences. Our results highlight the limitations of even the most complete reference genomes and provide a model for understanding the distribution of hidden variation in any human population.

Indexed as

Genetic VariationGenome, HumanAsia, SouthernChromosome MappingContig MappingHumansPolymorphism, Single NucleotideSequence Analysis, DNASouth Asian People

Identifiers

PMID41963335
PMCPMC13246962

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.