Evidence map›Paper›PMID 32249310›Full record

ReviewBriefings in bioinformatics2021

GenoPheno: cataloging large-scale phenotypic and next-generation sequencing data within human datasets.

Alba Gutiérrez-Sacristán, Carlos De Niz, Cartik Kothari, Sek Won Kong, Kenneth D Mandl, Paul Avillach

Abstract readReview
In one paragraph

Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. A high-throughput phenotyping algorithm is portable from adult to pediatric populations.Journal of the American Medical Informatics Association : JAMIA · 2021
    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

6 authors.

Alba Gutiérrez-SacristánDepartment of Biomedical Informatics, Harvard Medical School.
Carlos De NizDepartment of Biomedical Informatics, Harvard Medical School.
Cartik KothariDepartment of Biomedical Informatics, Harvard Medical School.
Sek Won KongDepartment of Biomedical Informatics, Harvard Medical School; Computational Health Informatics Program, Boston Children's Hospital.
Kenneth D MandlDepartment of Biomedical Informatics, Harvard Medical School; Computational Health Informatics Program, Boston Children's Hospital.
Paul AvillachDepartment of Biomedical Informatics, Harvard Medical School; Computational Health Informatics Program, Boston Children's Hospital.

Funding

TrainingU54HG007963 · NHGRI · HARVARD MEDICAL SCHOOL · PI KOHANE, ISAAC S. · 2014 to 2018
$18.3M
Instrumenting the Delivery System for a Genomics Research Information CommonsU01TR002623 · NCATS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2019 to 2024
$8.6M
NCATS NIH HHS U01 TR002623NHGRI NIH HHS U54 HG007963
6 · The paper itself

Abstract

Precision medicine promises to revolutionize treatment, shifting therapeutic approaches from the classical one-size-fits-all to those more tailored to the patient's individual genomic profile, lifestyle and environmental exposures. Yet, to advance precision medicine's main objective-ensuring the optimum diagnosis, treatment and prognosis for each individual-investigators need access to large-scale clinical and genomic data repositories. Despite the vast proliferation of these datasets, locating and obtaining access to many remains a challenge. We sought to provide an overview of available patient-level datasets that contain both genotypic data, obtained by next-generation sequencing, and phenotypic data-and to create a dynamic, online catalog for consultation, contribution and revision by the research community. Datasets included in this review conform to six specific inclusion parameters that are: (i) contain data from more than 500 human subjects; (ii) contain both genotypic and phenotypic data from the same subjects; (iii) include whole genome sequencing or whole exome sequencing data; (iv) include at least 100 recorded phenotypic variables per subject; (v) accessible through a website or collaboration with investigators and (vi) make access information available in English. Using these criteria, we identified 30 datasets, reviewed them and provided results in the release version of a catalog, which is publicly available through a dynamic Web application and on GitHub. Users can review as well as contribute new datasets for inclusion (Web: https://avillachlab.shinyapps.io/genophenocatalog/; GitHub: https://github.com/hms-dbmi/GenoPheno-CatalogShiny).

Indexed as

Databases, GeneticPhenotypeGenetic Predisposition to DiseaseHigh-Throughput Nucleotide SequencingHumansPrecision MedicineWhole Genome SequencingbiobankscatalogLarge-scale datasetsnext-generation sequencing dataphenotypic dataprecision medicine

Identifiers

PMID32249310
PMCPMC7820848

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.