Evidence map›Paper›PMID 42316246›Full record

ArticleAlgorithms for molecular biology : AMB2026

Lossless pangenome indexing using tag arrays.

Parsa Eskandar, Benedict Paten, Jouni Sirén

Abstract read
In one paragraph

Article in Algorithms for molecular biology : AMB, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Lossless pangenome indexing using tag arrays.Algorithms for molecular biology : AMB · 2026
    Article
  2. Article
  3. Col-BWT: Pangenomic Seed Chaining with Maximal Matches Improves Read Classification.Journal of computational biology : a journal of computational molecular cell biology · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Parsa EskandarUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Benedict PatenUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Jouni SirénUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA. jlsiren@ucsc.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pangenome graphs represent the genomic variation by encoding multiple haplotypes within a unified graph structure. However, efficient and lossless indexing of such structures remains challenging due to the scale and complexity of pangenomic data. We present a practical and scalable indexing framework based on tag arrays, which annotate positions in the Burrows-Wheeler transform (BWT) with graph coordinates. Our method extends the FM-index with a run-length compressed tag structure that enables efficient retrieval of all unique graph locations where a query pattern appears. We introduce a novel construction algorithm that combines unique k-mers, graph-based extensions, and haplotype traversal to compute the tag array in a memory-efficient manner. To support large genomes, we process each chromosome independently and then merge the results into a unified index using properties of the multi-string BWT and r-index. Our evaluation on the HPRC graphs demonstrates that the tag array structure compresses effectively, scales well with added haplotypes, and preserves accurate mapping information across diverse regions of the genome. This indexing method enables lossless and haplotype-aware querying in complex pangenomes and offers a practical indexing layer to develop scalable aligners and downstream graph-based analysis tools. The index additionally supports efficient one-to-all coordinate translation, enabling any interval on a haplotype to be mapped to its corresponding intervals across all other haplotypes in the graph.

Indexed as

Burrows–Wheeler transformPangenome indexingTag arrays

Identifiers

PMID42316246
PMCPMC13520286

What OpenQuestion holds

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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.