Evidence map›Paper›PMID 35055388›Full record

ArticleJournal of personalized medicine2022

Alistair Ward, Matt Velinder, Tonya Di Sera, Aditya Ekawade, Sabrina Malone Jenkins, Barry Moore, Rong Mao, Pinar Bayrak-Toydemir, Gabor Marth

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2022. 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. Article
  2. Review
  3. 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

9 authors.

Alistair WardDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Matt VelinderDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Tonya Di SeraDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Aditya EkawadeDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Sabrina Malone JenkinsDepartment of Pediatrics, University of Utah Scbool of Medicine, Salt Lake City, UT 84112, USA.
Barry MooreDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Rong MaoARUP Laboratories, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Pinar Bayrak-ToydemirARUP Laboratories, Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.
Gabor MarthDepartment of Human Genetics, University of Utah School of Medicine, Salt Lake City, UT 84112, USA.

Funding

Enhancing clinical diagnostic analysis with a robust de novo mutation detection toolR01HG012286 · NHGRI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Gabor T Marth, MARTIN TRISTANI-FIROUZI · 2022 to 2026
$4.7M
IOBIO: Web-based, interactive tools for real-time analysis in genomic big dataR01HG009000 · NHGRI · UNIVERSITY OF UTAH · PI MARTH, GABOR T · 2017 to 2020
$2.9M
Web tools for physician-driven diagnostic interpretation of genomic patient dataR01HG009712 · NHGRI · UNIVERSITY OF UTAH · PI MARTH, GABOR T, TRISTANI-FIROUZI, MARTIN · 2017 to 2020
$2.8M
IOBIO based, distributed genomic data access, management, visualization, and analysisR44HG009096 · NHGRI · FRAMESHIFT LABS, INC. · PI MARTH, GABOR T, MILLER, CHASE · 2018 to 2019
$2.0M
Empowering real-time, web-based genomic big data analysis at commercial scale.R41HG009096 · NHGRI · FRAMESHIFT LABS, INC. · PI MARTH, GABOR T, MILLER, CHASE · 2016 to 2017
$275k
Visually-driven disease variant analysis empowering real-time clinical research.R41HG009629 · NHGRI · FRAMESHIFT LABS, INC. · PI MARTH, GABOR T, MILLER, CHASE · 2017 to 2017
$256k
Novel iobio analysis supporting genome scale data quality control.R43HG009868 · NHGRI · FRAMESHIFT LABS, INC. · PI MARTH, GABOR T, MILLER, CHASE · 2017 to 2017
$250k
NHGRI NIH HHS 5R01HG009712NHGRI NIH HHS R01 HG009000NHGRI NIH HHS R01 HG009712NHGRI NIH HHS R01 HG012286NHGRI NIH HHS R41 HG009096NHGRI NIH HHS R41 HG009629NHGRI NIH HHS R43 HG009868NHGRI NIH HHS R44 HG009096
6 · The paper itself

Abstract

The primary goal of precision genomics is the identification of causative genetic variants in targeted or whole-genome sequencing data. The ultimate clinical hope is that these findings lead to an efficacious change in treatment for the patient. In current clinical practice, these findings are typically returned by expert analysts as static, text-based reports. Ideally, these reports summarize the quality of the data obtained, integrate known gene-phenotype associations, follow allele segregation and affected status within the sequenced samples, and weigh computational evidence of pathogenicity. These findings are used to prioritize the variant(s) most likely to cause the given patient's phenotypes. In most diagnostic settings, a team of experts contribute to these reports, including bioinformaticians, clinicians, and genetic counselors, among others. However, these experts often do not have the necessary tools to review genomic findings, test genetic hypotheses, or query specific gene and variant information. Additionally, team members often rely on different tools and methods based on their given expertise, resulting in further difficulties in communicating and discussing genomic findings. Here, we present

Indexed as

clinicalcollaborationdiagnosticsgeneticsgenomicsNICUrapid sequencingreanalysissoftwareundiagnosed diseasevisualization

Identifiers

PMID35055388
PMCPMC8780189

What OpenQuestion 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 OpenQuestion graph.