Evidence map›Paper›PMID 33624749›Full record

ArticleBioinformatics (Oxford, England)2021

d-PBWT: dynamic positional Burrows-Wheeler transform.

Ahsan Sanaullah, Degui Zhi, Shaojie Zhang

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. An Efficient Dynamic Data Structure for Haplotype Matching and Compression on Biobank-Scale Data.Journal of computational biology : a journal of computational molecular cell biology · 2026
    Article
  2. Article
  3. Article
  4. DynamicbioRxiv : the preprint server for biology · 2025
    Article
  5. Haplotype Matching with GBWT for Pangenome Graphs.bioRxiv : the preprint server for biology · 2025
    Article
  6. An Efficient Data Structure and Algorithm for Long-Match Query in Run-Length Compressed BWT.Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Haplotype Threading Using the Positional Burrows-Wheeler Transform.Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2022
    Article
  15. 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

3 authors.

Ahsan SanaullahDepartment of Computer Science, University of Central Florida, Orlando, FL 32816, USA.
Degui ZhiCenter for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Shaojie ZhangDepartment of Computer Science, University of Central Florida, Orlando, FL 32816, USA.ORCID 0000-0002-4051-5549

Funding

Scalable methods for identity by descentR01HG010086 · NHGRI · YALE UNIVERSITY · PI Shaojie Zhang, Degui Zhi · 2018 to 2026
$4.9M
NHGRI NIH HHS R01 HG010086US National Institutes of Health R01HG010086
6 · The paper itself

Abstract

motivationDurbin's positional Burrows-Wheeler transform (PBWT) is a scalable data structure for haplotype matching. It has been successfully applied to identical by descent (IBD) segment identification and genotype imputation. Once the PBWT of a haplotype panel is constructed, it supports efficient retrieval of all shared long segments among all individuals (long matches) and efficient query between an external haplotype and the panel. However, the standard PBWT is an array-based static data structure and does not support dynamic updates of the panel.

resultsHere, we generalize the static PBWT to a dynamic data structure, d-PBWT, where the reverse prefix sorting at each position is stored with linked lists. We also developed efficient algorithms for insertion and deletion of individual haplotypes. In addition, we verified that d-PBWT can support all algorithms of PBWT. In doing so, we systematically investigated variations of set maximal match and long match query algorithms: while they all have average case time complexity independent of database size, they have different worst case complexities and dependencies on additional data structures. AVAILABILITYAND IMPLEMENTATION: The benchmarking code is available at genome.ucf.edu/d-PBWT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifiers

PMID33624749
PMCPMC12158185

What OpenQuestion holds

Textmetadata
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.