Evidence map›Paper›PMID 31649718›Full record

SynthesisFrontiers in genetics2019

Predicting Functional Effects of Synonymous Variants: A Systematic Review and Perspectives.

Zishuo Zeng, Yana Bromberg

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing 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

56 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
  6. Article
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  8. Article
  9. Article
  10. Review
  11. Article
  12. Variant Effect Prediction in the Age of Machine Learning.Cold Spring Harbor perspectives in biology · 2024
    Review
  13. A second hotspot for pathogenic exon-skipping variants in CDC45.European journal of human genetics : EJHG · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. Review
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

2 authors.

Zishuo ZengInstitute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ, United States.
Yana BrombergDepartment of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in high-throughput experimentation have put the exploration of genome sequences at the forefront of precision medicine. In an effort to interpret the sequencing data, numerous computational methods have been developed for evaluating the effects of genome variants. Interestingly, despite the fact that every person has as many synonymous (sSNV) as non-synonymous single nucleotide variants, our ability to predict their effects is limited. The paucity of experimentally tested sSNV effects appears to be the limiting factor in development of such methods. Here, we summarize the details and evaluate the performance of nine existing computational methods capable of predicting sSNV effects. We used a set of

Indexed as

effect predictorsmachine learningsynonymous variantsvariant frequencyvariant functional effect

Identifiers

PMID31649718
PMCPMC6791167

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.