Evidence map›Paper›PMID 40097267›Full record

ArticleBioinformatics (Oxford, England)2025

Generating multiple alignments on a pangenomic scale.

Jannik Olbrich, Thomas Büchler, Enno Ohlebusch

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. 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. Article
  2. Col-BWT: Pangenomic Seed Chaining with Maximal Matches Improves Read Classification.Journal of computational biology : a journal of computational molecular cell biology · 2026
    Article
  3. Prefix-free parsing for merging big BWTs.International Symposium on String Processing and Information Retrieval : SPIRE ... : proceedings. SPIRE (Symposium) · 2026
    Article
  4. Partitioned Multi-MUM finding for scalable pangenomics.bioRxiv : the preprint server for biology · 2025
    Article
  5. Faster run-length compressed suffix arrays.Oasics : openaccess series in informatics · 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

3 authors.

Jannik OlbrichInstitute of Theoretical Computer Science, Ulm University, Ulm, 89069, Germany.ORCID 0000-0003-3291-7342
Thomas BüchlerInstitute of Theoretical Computer Science, Ulm University, Ulm, 89069, Germany.ORCID 0000-0002-9273-5439
Enno OhlebuschInstitute of Theoretical Computer Science, Ulm University, Ulm, 89069, Germany.

Funding

Deutsche Forschungsgemeinschaft
6 · The paper itself

Abstract

motivationSince novel long read sequencing technologies allow for de novo assembly of many individuals of a species, high-quality assemblies are becoming widely available. For example, the recently published draft human pangenome reference was based on assemblies composed of contigs. There is an urgent need for a software-tool that is able to generate a multiple alignment of genomes of the same species because current multiple sequence alignment programs cannot deal with such a volume of data.

resultsWe show that the combination of a well-known anchor-based method with the technique of prefix-free parsing yields an approach that is able to generate multiple alignments on a pangenomic scale, provided that large-scale structural variants are rare. Furthermore, experiments with real world data show that our software tool PANgenomic Anchor-based Multiple Alignment significantly outperforms current state-of-the art programs. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://gitlab.com/qwerzuiop/panama, archived at swh:1:dir:e90c9f664995acca9063245cabdd97549cf39694.

Indexed as

GenomicsSequence AlignmentSoftwareAlgorithmsHumansSequence Analysis, DNA

Identifiers

PMID40097267
PMCPMC11928754

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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 OpenQuestion graph.