Evidence map›Paper›PMID 39391725›Full record

ArticleiScience2024

Building a pangenome alignment index via recursive prefix-free parsing.

Eddie Ferro, Marco Oliva, Travis Gagie, Christina Boucher

Abstract read
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Prefix-free parsing for merging big BWTs.International Symposium on String Processing and Information Retrieval : SPIRE ... : proceedings. SPIRE (Symposium) · 2026
    Article
  4. Article
  5. Partitioned Multi-MUM finding for scalable pangenomics.bioRxiv : the preprint server for biology · 2025
    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

4 authors.

Eddie FerroDepartment of Computer and Information Science and Engineering, Herbert-Wertheim College of Engineering, University of Florida, Gainesville, FL 32607, USA.
Marco OlivaDepartment of Computer and Information Science and Engineering, Herbert-Wertheim College of Engineering, University of Florida, Gainesville, FL 32607, USA.
Travis GagieFaculty of Computer Science, Dalhousie University, Halifax, NS, Canada.
Christina BoucherDepartment of Computer and Information Science and Engineering, Herbert-Wertheim College of Engineering, University of Florida, Gainesville, FL 32607, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pangenomics alignment offers a solution to reduce bias in biomedical research. Traditionally, short-read aligners like Bowtie and BWA indexed a single reference genome to find approximate alignments. These methods, limited by linear-memory requirements, can only index a few genomes. Emerging pangenome aligners, such as VG, Giraffe, and Moni, address this by indexing more genomes. VG and Giraffe use a variation graph, while Moni indexes sequences accounting for repetition using prefix-free parsing to build a dictionary and parse. The main challenge is the parse's size, which becomes significantly larger than the dictionary. To scale Moni, we propose removing the parse from the construction of the run-length encoded BWT (RLBWT), suffix array, and Longest Common Prefix (LCP) by applying prefix-free parsing recursively. This approach improves construction time and memory requirements, enabling efficient construction of RLBWT, suffix array, and LCP for large pangenomes, such as those from the Human Pangenome Reference Consortium.

Indexed as

Biocomputational methodBioinformaticsClassification of bioinformatical subjectGenomic analysis

Identifiers

PMID39391725
PMCPMC11465122

What OpenQuestion holds

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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.