Evidence map›Paper›PMID 36646895›Full record

ArticleNature methods2023

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs.

Jonas A Sibbesen, Jordan M Eizenga, Adam M Novak, Jouni Sirén, Xian Chang, Erik Garrison, Benedict Paten

Abstract read
PubMed Publisher
In one paragraph

Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing 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

49 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Lossless pangenome indexing using tag arrays.Algorithms for molecular biology : AMB · 2026
    Article
  10. Article
  11. Article
  12. Review
  13. Accessing crop genetic diversity via pangenomics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Jonas A Sibbesen *UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA.ORCID http://orcid.org/0000-0002-5528-0236
Jordan M Eizenga *UC Santa Cruz Genomics Institute, Santa Cruz, CA, USA.
Adam M NovakUC Santa Cruz Genomics Institute, Santa Cruz, CA, USA.
Jouni SirénUC Santa Cruz Genomics Institute, Santa Cruz, CA, USA.ORCID http://orcid.org/0000-0001-5828-4139
Xian ChangUC Santa Cruz Genomics Institute, Santa Cruz, CA, USA.
Erik GarrisonUniversity of Tennessee Health Science Center, Memphis, TN, USA.
Benedict PatenUC Santa Cruz Genomics Institute, Santa Cruz, CA, USA. bpaten@ucsc.edu.ORCID http://orcid.org/0000-0001-8863-3539

Funding

The WashU-UCSC-EBI Human Genome Reference Center."U41HG010972 · NHGRI · WASHINGTON UNIVERSITY · PI Ting Wang · 2019 to 2026
$24.9M
Dockstore: The Community Platform for Reproducible Biomedical Workflows and ApplicationsU24HG011853 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI Jonathan Lawson, Benedict Paten · 2021 to 2026
$5.7M
Comprehensive, Flexible and FAIR Tools for the HuBMAP HIVEOT2OD026682 · OD · CARNEGIE-MELLON UNIVERSITY · PI PATEN, BENEDICT, RUFFALO, MATTHEW · 2018 to 2021
$3.4M
The construction and utility of reference pan-genome graphsU01HG010961 · NHGRI · DANA-FARBER CANCER INST · PI LI, HENG, PATEN, BENEDICT · 2020 to 2023
$3.2M
Enabling Comparative PangenomicsR01HG010485 · NHGRI · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI HAUSSLER, DAVID H, MARTIN, FERGAL JAMES · 2020 to 2023
$2.6M
NIH HHS OT2 OD026682U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) R01HG010485U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) U01HG010961U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) U24HG011853U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) U41HG010972
6 · The paper itself

Abstract

Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our toolchain, which consists of additions to the VG toolkit and a standalone tool, RPVG, can construct spliced pangenome graphs, map RNA sequencing data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. We show that this workflow improves accuracy over state-of-the-art RNA sequencing mapping methods, and that it can efficiently quantify haplotype-specific transcript expression without needing to characterize the haplotypes of a sample beforehand.

Indexed as

Computational BiologyGene Expression ProfilingHaplotypesMetagenomicsTranscriptome

Identifiers

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.