Evidence map›Paper›PMID 30602100›Full record

ArticleGenomics & informatics2018

Integration of a Large-Scale Genetic Analysis Workbench Increases the Accessibility of a High-Performance Pathway-Based Analysis Method.

Sungyoung Lee, Taesung Park

Abstract read
In one paragraph

Article in Genomics & informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Sungyoung LeeCenter for Precision Medicine, Seoul National University Hospital, Seoul 03080, Korea.
Taesung ParkDepartment of Statistics, Seoul National University, Seoul 08826, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid increase in genetic dataset volume has demanded extensive adoption of biological knowledge to reduce the computational complexity, and the biological pathway is one well-known source of such knowledge. In this regard, we have introduced a novel statistical method that enables the pathway-based association study of large-scale genetic dataset-namely, PHARAOH. However, researcher-level application of the PHARAOH method has been limited by a lack of generally used file formats and the absence of various quality control options that are essential to practical analysis. In order to overcome these limitations, we introduce our integration of the PHARAOH method into our recently developed all-in-one workbench. The proposed new PHARAOH program not only supports various de facto standard genetic data formats but also provides many quality control measures and filters based on those measures. We expect that our updated PHARAOH provides advanced accessibility of the pathway-level analysis of large-scale genetic datasets to researchers.

Indexed as

genetic analysismultithreadingpathway-level analysisvariant calling format

Identifiers

PMID30602100
PMCPMC6440666

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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.