Evidence map›Paper›PMID 24626529›Full record

ArticleBriefings in bioinformatics2015

Rich annotation of DNA sequencing variants by leveraging the Ensembl Variant Effect Predictor with plugins.

Michael Yourshaw, S Paige Taylor, Aliz R Rao, Martín G Martín, Stanley F Nelson

Abstract read
In one paragraph

Article in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. Hadoop and PySpark for reproducibility and scalability of genomic sequencing studies.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020
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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

5 authors.

Michael Yourshaw
S Paige Taylor
Aliz R Rao
Martín G Martín
Stanley F Nelson

Funding

Neurogenin3 and Intestinal FailureR01DK083762 · NIDDK · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MARTIN, MARTIN G · 2009 to 2012
$1.6M
NIDDK NIH HHS R01 DK083762
6 · The paper itself

Abstract

High-throughput DNA sequencing has become a mainstay for the discovery of genomic variants that may cause disease or affect phenotype. A next-generation sequencing pipeline typically identifies thousands of variants in each sample. A particular challenge is the annotation of each variant in a way that is useful to downstream consumers of the data, such as clinical sequencing centers or researchers. These users may require that all data storage and analysis remain on secure local servers to protect patient confidentiality or intellectual property, may have unique and changing needs to draw on a variety of annotation data sets and may prefer not to rely on closed-source applications beyond their control. Here we describe scalable methods for using the plugin capability of the Ensembl Variant Effect Predictor to enrich its basic set of variant annotations with additional data on genes, function, conservation, expression, diseases, pathways and protein structure, and describe an extensible framework for easily adding additional custom data sets.

Indexed as

Computational BiologyDatabases, Nucleic AcidGenetic VariationHigh-Throughput Nucleotide SequencingHumansMolecular Sequence AnnotationSequence Analysis, DNASoftwareannotationdatabaseDNA sequencingEnsembl Variant Effect Predictorplugin

Identifiers

PMID24626529
PMCPMC6283364

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.