Evidence map›Paper›PMID 34488830›Full record

ArticleGenome biology2021

NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Mian Umair Ahsan, Qian Liu, Li Fang, Kai Wang

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.

0numbers the graph read from it
0cells of the map it votes in
61citing 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

61 citing papers in PubMed.

  1. Nanopore Sequencing for Chikungunya Virus: Principles and Application.Methods in molecular biology (Clifton, N.J.) · 2027
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Review
  8. Article
  9. Article
  10. Article
  11. Life science alliance · 2026
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review

1 more citing papers are in PubMed but not listed here.

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

4 authors.

Mian Umair Ahsan *Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Qian Liu *Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Li FangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
Kai WangRaymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. wangk@email.chop.edu.ORCID 0000-0002-5585-982X

Funding

Detection and annotation of structural variants from long-read sequencingR01GM132713 · NIGMS · CHILDREN'S HOSP OF PHILADELPHIA · PI WANG, KAI · 2019 to 2022
$1.9M
NIGMS NIH HHS R01 GM132713
6 · The paper itself

Abstract

Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs using long-range haplotype information, then phases long reads with called SNPs and calls indels with local realignment. Evaluation on 8 human genomes demonstrates that NanoCaller generally achieves better performance than competing approaches. We experimentally validate 41 novel variants in a widely used benchmarking genome, which could not be reliably detected previously. In summary, NanoCaller facilitates the discovery of novel variants in complex genomic regions from long-read sequencing.

Indexed as

High-Throughput Nucleotide SequencingNeural Networks, ComputerAllelesBase SequenceBenchmarkingChromosome MappingGenome, HumanHaplotypesHumansINDEL MutationMajor Histocompatibility ComplexNanoparticlesNanopore SequencingPolymorphism, Single NucleotideDeep learningDifficult-to-map regionsLong-range haplotypeVariant calling

Identifiers

PMID34488830
PMCPMC8419925

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.