Evidence map›Paper›PMID 40713517›Full record

ArticleBMC bioinformatics2025

Accurate human genome analysis with element avidity sequencing.

Andrew Carroll, Alexey Kolesnikov, Daniel E Cook, Lucas Brambrink, Kelly N Wiseman, Sophie M Billings, Semyon Kruglyak, Bryan R Lajoie, Junhua Zhao, Shawn E Levy and 4 more

Abstract read
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Pangenome-aware DeepVariant.bioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Highly accurate assembly polishing with DeepPolisher.bioRxiv : the preprint server for biology · 2024
    Article
  15. Article
  16. Article
  17. Utility Analyses of AVITI Sequencing Chemistry.bioRxiv : the preprint server for biology · 2024
    Article
  18. Personalized Pangenome References.bioRxiv : the preprint server for biology · 2023
    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

14 authors.

Andrew CarrollGoogle LLC, Mountain View, CA, USA. awcarroll@google.com.
Alexey KolesnikovGoogle LLC, Mountain View, CA, USA.
Daniel E CookGoogle LLC, Mountain View, CA, USA.
Lucas BrambrinkGoogle LLC, Mountain View, CA, USA.
Kelly N WisemanElement Biosciences, San Diego, CA, USA.
Sophie M BillingsElement Biosciences, San Diego, CA, USA.
Semyon KruglyakElement Biosciences, San Diego, CA, USA.
Bryan R LajoieElement Biosciences, San Diego, CA, USA.
Junhua ZhaoElement Biosciences, San Diego, CA, USA.
Shawn E LevyElement Biosciences, San Diego, CA, USA.
Cory Y McLeanGoogle LLC, Mountain View, CA, USA.
Kishwar ShafinGoogle LLC, Mountain View, CA, USA.
Maria NattestadGoogle LLC, Mountain View, CA, USA.
Pi-Chuan ChangGoogle LLC, Mountain View, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNew sequencing technologies provide options for the scientific community to design studies and build clinical workflows. These options expand user choice, and can enable more accurate, scalable, or affordable workflows depending on the fit between scientist needs and platform capability. However, it is essential to understand the performance of these new technologies for different tasks, especially for capabilities that were not possible or tractable in prior technologies. We investigate the new sequencing technology avidity from Element Biosciences. to help the scientific community understand the performance of the options to generate sequencing data.

resultsWe show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20-30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element's ability to generate paired end sequencing with longer insert sizes than typical short-read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving more accurate genome analyses at all coverages.

conclusionsNew options for sequencing technologies can analyze genomes comparably or better than prior standard methods.

Indexed as

Genome, HumanGenomicsHigh-Throughput Nucleotide SequencingSequence Analysis, DNAWhole Genome SequencingHumansBioinformaticsGenomicsSequencingVariant callingWGS

Identifiers

PMID40713517
PMCPMC12291380

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.