Evidence map›Paper›PMID 37095564›Full record

ArticleGenome biology2023

Transformation of alignment files improves performance of variant callers for long-read RNA sequencing data.

Vladimir B C de Souza, Ben T Jordan, Elizabeth Tseng, Elizabeth A Nelson, Karen K Hirschi, Gloria Sheynkman, Mark D Robinson

Abstract read
In one paragraph

Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Long-read RNA sequencing: A transformative technology for exploring transcriptome complexity in human diseases.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
  11. Article
  12. Article
  13. 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

7 authors.

Vladimir B C de SouzaDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057, Zurich, Switzerland.
Ben T JordanDepartment of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA.
Elizabeth TsengPacBio, San Francisco, USA.
Elizabeth A NelsonDepartment of Cell Biology and Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.
Karen K HirschiDepartment of Cell Biology and Cardiovascular Research Center, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.
Gloria SheynkmanDepartment of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, USA. gs9yr@virginia.edu.
Mark D RobinsonDepartment of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057, Zurich, Switzerland. mark.robinson@mls.uzh.ch.ORCID 0000-0002-3048-5518

Funding

Uncovering the functional diversification mechanisms of transcription factor isoforms involved in stem cell differentiationR35GM142647 · NIGMS · UNIVERSITY OF VIRGINIA · PI SHEYNKMAN, GLORIA · 2021 to 2025
$2.1M
NIGMS NIH HHS R35 GM142647
6 · The paper itself

Abstract

Long-read RNA sequencing (lrRNA-seq) produces detailed information about full-length transcripts, including novel and sample-specific isoforms. Furthermore, there is an opportunity to call variants directly from lrRNA-seq data. However, most state-of-the-art variant callers have been developed for genomic DNA. Here, there are two objectives: first, we perform a mini-benchmark on GATK, DeepVariant, Clair3, and NanoCaller primarily on PacBio Iso-Seq, data, but also on Nanopore and Illumina RNA-seq data; second, we propose a pipeline to process spliced-alignment files, making them suitable for variant calling with DNA-based callers. With such manipulations, high calling performance can be achieved using DeepVariant on Iso-seq data.

Indexed as

High-Throughput Nucleotide SequencingRNAExome SequencingRNA-SeqSequence Analysis, RNARNA

Identifiers

PMID37095564
PMCPMC10123983

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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.