Efficient population-scale variant analysis and prioritization with VAPr.

Amanda Birmingham, Adam M Mark, Carlo Mazzaferro, Guorong Xu, Kathleen M Fisch

Abstract read

In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Biological Interpretation of Complex Genomic Data.Methods in molecular biology (Clifton, N.J.) · 2019
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

5 authors.

Amanda BirminghamCenter for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

Adam M MarkCenter for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

Carlo MazzaferroCenter for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

Guorong XuCenter for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

Kathleen M FischCenter for Computational Biology and Bioinformatics, Department of Medicine, University of California, San Diego, La Jolla, CA, USA.

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024

$88.3M

SYNTHETIC SYSTEMSP50GM085764 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HASTY, JEFF M, IDEKER, TREY · 2010 to 2018

$23.3M

NCATS NIH HHS UL1 TR001442NIGMS NIH HHS P50 GM085764

6 · The paper itself

Abstract

Summary: With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package Variant Analysis and Prioritization (VAPr). VAPr leverages existing annotation tools ANNOVAR and MyVariant.info with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies. Availability and implementation: VAPr is developed in Python and is available for free use and extension under the MIT License. An install package is available on PyPi at https://pypi.python.org/pypi/VAPr, while source code and extensive documentation are on GitHub at https://github.com/ucsd-ccbb/VAPr.

Indexed as

Computational BiologyExomeGenomicsMetagenomicsSoftware

Identifiers

PMID29659724

DOI10.1093/bioinformatics/bty192

PMCPMC6084604

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.