Evidence map›Paper›PMID 31297895›Full record

ArticleHuman mutation2019

Future directions for high-throughput splicing assays in precision medicine.

Christy L Rhine, Christopher Neil, David T Glidden, Kamil J Cygan, Alger M Fredericks, Jing Wang, Nephi A Walton, William G Fairbrother

Abstract readComparative Study
In one paragraph

Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Assessing the Functional Significance of Novel and Rare Variants of theInternational journal of molecular sciences · 2025
    Article
  3. Article
  4. Sequence-Independent RNA Sensing in Living Mammalian Cells.bioRxiv : the preprint server for biology · 2025
    Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Cancers · 2021
    Article
  14. Article
  15. 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

8 authors.

Christy L RhineMolecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island.
Christopher NeilMolecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island.
David T GliddenCenter for Computational Molecular Biology, Brown University, Providence, Rhode Island.ORCID 0000-0002-2742-9531
Kamil J CyganMolecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island.
Alger M FredericksMolecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island.
Jing WangMolecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island.
Nephi A WaltonGenomic Medicine Institute, Geisinger, Danville, Pennsylvania.
William G FairbrotherMolecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island.

Funding

Discovering Splicing Defects in Human GenesR01GM127472 · NIGMS · BROWN UNIVERSITY · PI William G Fairbrother · 2018 to 2026
$5.9M
A genomic approach to studying the life cycle of intron lariatsR01GM105681 · NIGMS · BROWN UNIVERSITY · PI FAIRBROTHER, WILLIAM G · 2014 to 2022
$3.7M
Component 2 - Resource ProjectU41HG007346 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2015 to 2017
$1.7M
A Discovery Tool for Variations that Affect SplicingR01GM095612 · NIGMS · BROWN UNIVERSITY · PI FAIRBROTHER, WILLIAM G · 2010 to 2014
$1.5M
Critical Assessment of Genome Interpretation ConferenceR13HG006650 · NHGRI · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2011 to 2018
$165k
NHGRI NIH HHS R13 HG006650NHGRI NIH HHS U41 HG007346NIGMS NIH HHS R01 GM095612NIGMS NIH HHS R01 GM105681NIGMS NIH HHS R01 GM127472
6 · The paper itself

Abstract

Classification of variants of unknown significance is a challenging technical problem in clinical genetics. As up to one-third of disease-causing mutations are thought to affect pre-mRNA splicing, it is important to accurately classify splicing mutations in patient sequencing data. Several consortia and healthcare systems have conducted large-scale patient sequencing studies, which discover novel variants faster than they can be classified. Here, we compare the advantages and limitations of several high-throughput splicing assays aimed at mitigating this bottleneck, and describe a data set of ~5,000 variants that we analyzed using our Massively Parallel Splicing Assay (MaPSy). The Critical Assessment of Genome Interpretation group (CAGI) organized a challenge, in which participants submitted machine learning models to predict the splicing effects of variants in this data set. We discuss the winning submission of the challenge (MMSplice) which outperformed existing software. Finally, we highlight methods to overcome the limitations of MaPSy and similar assays, such as tissue-specific splicing, the effect of surrounding sequence context, classifying intronic variants, synthesizing large exons, and amplifying complex libraries of minigene species. Further development of these assays will greatly benefit the field of clinical genetics, which lack high-throughput methods for variant interpretation.

Indexed as

MutationRNA SplicingComputational BiologyHigh-Throughput Nucleotide SequencingHumansMachine LearningPrecision MedicineRNA PrecursorsSequence Analysis, RNASoftwareRNA Precursorsassaydiseasehigh-throughputprecision medicinesplicingvariant

Identifiers

PMID31297895
PMCPMC6744296

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.