Evidence map›Paper›PMID 33597536›Full record

ArticleNature communications2021

Aquila enables reference-assisted diploid personal genome assembly and comprehensive variant detection based on linked reads.

Xin Zhou, Lu Zhang, Ziming Weng, David L Dill, Arend Sidow

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. The Bioinformatic Applications of Hi-C and Linked Reads.Genomics, proteomics & bioinformatics · 2024
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

5 authors.

Xin ZhouDepartment of Computer Science, Stanford University, Stanford, CA, USA. maizie.zhou@vanderbilt.edu.ORCID 0000-0003-4015-4787
Lu ZhangDepartment of Pathology, Stanford University, Stanford, CA, USA.
Ziming WengDepartment of Pathology, Stanford University, Stanford, CA, USA.
David L DillDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Arend SidowDepartment of Pathology, Stanford University, Stanford, CA, USA. arend@stanford.edu.ORCID 0000-0002-8287-331X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We introduce Aquila, a new approach to variant discovery in personal genomes, which is critical for uncovering the genetic contributions to health and disease. Aquila uses a reference sequence and linked-read data to generate a high quality diploid genome assembly, from which it then comprehensively detects and phases personal genetic variation. The contigs of the assemblies from our libraries cover >95% of the human reference genome, with over 98% of that in a diploid state. Thus, the assemblies support detection and accurate genotyping of the most prevalent types of human genetic variation, including single nucleotide polymorphisms (SNPs), small insertions and deletions (small indels), and structural variants (SVs), in all but the most difficult regions. All heterozygous variants are phased in blocks that can approach arm-level length. The final output of Aquila is a diploid and phased personal genome sequence, and a phased Variant Call Format (VCF) file that also contains homozygous and a few unphased heterozygous variants. Aquila represents a cost-effective approach that can be applied to cohorts for variation discovery or association studies, or to single individuals with rare phenotypes that could be caused by SVs or compound heterozygosity.

Indexed as

DiploidyPolymorphism, Single NucleotideAnimalsComputational BiologyGenomicsHigh-Throughput Nucleotide SequencingHumansReproducibility of ResultsWhole Genome Sequencing

Identifiers

PMID33597536
PMCPMC7889865

What Socratic holds

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LicenceCC BY
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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 Socratic graph.