Evidence map›Paper›PMID 37234922›Full record

ReviewFrontiers in molecular biosciences2023

Resources and tools for rare disease variant interpretation.

Luana Licata, Allegra Via, Paola Turina, Giulia Babbi, Silvia Benevenuta, Claudio Carta, Rita Casadio, Andrea Cicconardi, Angelo Facchiano, Piero Fariselli and 12 more

Abstract readReview
In one paragraph

Review in Frontiers in molecular biosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

22 authors.

Luana LicataDepartment of Biology, University of Rome Tor Vergata, Roma, Italy.
Allegra ViaDepartment of Biochemical Sciences "A. Rossi Fanelli", University of Rome "La Sapienza", Roma, Italy.
Paola TurinaDepartment of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Giulia BabbiDepartment of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Silvia BenevenutaDepartment of Medical Sciences, University of Torino, Torino, Italy.
Claudio CartaNational Centre for Rare Diseases, Istituto Superiore di Sanità, Roma, Italy.
Rita CasadioDepartment of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Andrea CicconardiDepartment of Physics, University of Genova, Genova, Italy.
Angelo FacchianoNational Research Council, Institute of Food Science, Avellino, Italy.
Piero FariselliDepartment of Medical Sciences, University of Torino, Torino, Italy.
Deborah GiordanoNational Research Council, Institute of Food Science, Avellino, Italy.
Federica IsidoriMedical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Anna MarabottiDepartment of Chemistry and Biology "A. Zambelli", University of Salerno, Fisciano, SA, Italy.
Pier Luigi MartelliDepartment of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Stefano PascarellaDepartment of Biochemical Sciences "A. Rossi Fanelli", University of Rome "La Sapienza", Roma, Italy.
Michele PinelliDepartment of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy.
Tommaso PippucciMedical Genetics Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.
Roberta RussoDepartment of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Napoli, Italy.
Castrense SavojardoDepartment of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Bernardina ScafuriDepartment of Chemistry and Biology "A. Zambelli", University of Salerno, Fisciano, SA, Italy.
Lucrezia ValerianiCenter for Technology and Innovation, Trieste, Italy.
Emidio CapriottiDepartment of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Collectively, rare genetic disorders affect a substantial portion of the world's population. In most cases, those affected face difficulties in receiving a clinical diagnosis and genetic characterization. The understanding of the molecular mechanisms of these diseases and the development of therapeutic treatments for patients are also challenging. However, the application of recent advancements in genome sequencing/analysis technologies and computer-aided tools for predicting phenotype-genotype associations can bring significant benefits to this field. In this review, we highlight the most relevant online resources and computational tools for genome interpretation that can enhance the diagnosis, clinical management, and development of treatments for rare disorders. Our focus is on resources for interpreting single nucleotide variants. Additionally, we present use cases for interpreting genetic variants in clinical settings and review the limitations of these results and prediction tools. Finally, we have compiled a curated set of core resources and tools for analyzing rare disease genomes. Such resources and tools can be utilized to develop standardized protocols that will enhance the accuracy and effectiveness of rare disease diagnosis.

Indexed as

genetic disordergenome interpretationgenotype-phenotype associationmachine learningprecision medicinerare diseasesingle nucleotide variant (SNV)

Identifiers

PMID37234922
PMCPMC10206239

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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.