Evidence map›Paper›PMID 27528417›Full record

ReviewNature reviews. Genetics2016

Towards precision medicine.

Euan A Ashley

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 467 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
467citing papers in PubMed, 5 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

467 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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407 more citing papers are in PubMed but not listed here.

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

1 author.

Euan A AshleyCenter for Inherited Cardiovascular Disease, Falk Cardiovascular Research Building, Stanford Medicine, 870 Quarry Road, Stanford, California 94305, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is great potential for genome sequencing to enhance patient care through improved diagnostic sensitivity and more precise therapeutic targeting. To maximize this potential, genomics strategies that have been developed for genetic discovery - including DNA-sequencing technologies and analysis algorithms - need to be adapted to fit clinical needs. This will require the optimization of alignment algorithms, attention to quality-coverage metrics, tailored solutions for paralogous or low-complexity areas of the genome, and the adoption of consensus standards for variant calling and interpretation. Global sharing of this more accurate genotypic and phenotypic data will accelerate the determination of causality for novel genes or variants. Thus, a deeper understanding of disease will be realized that will allow its targeting with much greater therapeutic precision.

Indexed as

AlgorithmsGenomicsGenotypeHumansPrecision MedicineSequence Analysis, DNASoftware

Identifiers

PMID27528417

What OpenQuestion holds

Textmetadata
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.