Evidence map›Paper›PMID 31159868›Full record

ArticleGenome biology2019

Haplotype-aware diplotyping from noisy long reads.

Jana Ebler, Marina Haukness, Trevor Pesout, Tobias Marschall, Benedict Paten

Abstract readValidation Study
In one paragraph

Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Article
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  9. International journal of molecular sciences · 2024
    Article
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  12. Article
  13. Review
  14. Article
  15. Local read haplotagging enables accurate long-read small variant calling.bioRxiv : the preprint server for biology · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Allele detection usingBioinformatics advances · 2023
    Article
  20. 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.

Jana EblerCenter for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, 66123, Germany.
Marina HauknessUC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, 95064, CA, USA.
Trevor PesoutUC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, 95064, CA, USA.
Tobias MarschallCenter for Bioinformatics, Saarland University, Saarland Informatics Campus E2.1, Saarbrücken, 66123, Germany. t.marschall@mpi-inf.mpg.de.ORCID 0000-0002-9376-1030
Benedict PatenUC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, 95064, CA, USA. benedict@soe.ucsc.edu.

Funding

Training SubprojectU54HG007990 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, PATTERSON, DAVID · 2014 to 2018
$14.8M
A Unified Nanopore Platform for Direct Sequencing of Individual Full Length RNA Strands Bearing Modified NucleotidesR01HG010053 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI AKESON, MARK A · 2018 to 2021
$3.8M
Evolution of new regulatory networks via genetic arms races between KRAB zinc finger proteins and retrotransposonsR01HG010329 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI SALAMA, SOFIE REDA · 2019 to 2022
$2.7M
Unbiased analysis of genomic correlates of gene expression in health and diseaseU01HL137183 · NHLBI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H · 2017 to 2019
$1.8M
UC Santa Cruz Training Program In Genomic SciencesT32HG008345 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI BROOKS, ANGELA NORIE, GREEN, RICHARD EDWARD · 2015 to 2020
$1.4M
NHGRI NIH HHS R01 HG010053NHGRI NIH HHS R01 HG010329NHGRI NIH HHS T32 HG008345NHGRI NIH HHS U54 HG007990NHLBI NIH HHS U01 HL137183
6 · The paper itself

Abstract

Current genotyping approaches for single-nucleotide variations rely on short, accurate reads from second-generation sequencing devices. Presently, third-generation sequencing platforms are rapidly becoming more widespread, yet approaches for leveraging their long but error-prone reads for genotyping are lacking. Here, we introduce a novel statistical framework for the joint inference of haplotypes and genotypes from noisy long reads, which we term diplotyping. Our technique takes full advantage of linkage information provided by long reads. We validate hundreds of thousands of candidate variants that have not yet been included in the high-confidence reference set of the Genome-in-a-Bottle effort.

Indexed as

Genotyping TechniquesAlgorithmsGenomicsHaplotypesModels, StatisticalComputational genomicsDiplotypesGenotypingHaplotypesLong readsPhasing

Identifiers

PMID31159868
PMCPMC6547545

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.