Evidence map›Paper›PMID 30717290›Full record

ArticleJournal of personalized medicine2019

VARIFI-Web-Based Automatic Variant Identification, Filtering and Annotation of Amplicon Sequencing Data.

Milica Krunic, Peter Venhuizen, Leonhard Müllauer, Bettina Kaserer, Arndt von Haeseler

Abstract read
In one paragraph

Article in Journal of personalized medicine, 2019. 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.

  1. 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.

Milica KrunicCenter for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, 1030 Vienna, Austria. milica.krunic@univie.ac.at.
Peter VenhuizenDepartment of Applied Genetics und Cell Biology, University of Natural Resources and Life Sciences, Muthgasse 18, 1190 Vienna, Austria. peter.venhuizen@boku.ac.at.
Leonhard MüllauerInstitute of Pathology, Medical University Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria. leonhard.muellauer@meduniwien.ac.at.
Bettina KasererInstitute of Pathology, Medical University Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria. bettina.kaserer@meduniwien.ac.at.
Arndt von HaeselerCenter for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, Dr. Bohrgasse 9, 1030 Vienna, Austria. arndt.von.haeseler@unvie.ac.at.

Funding

The Austrian Science Fund W1207-B09
6 · The paper itself

Abstract

Fast and affordable benchtop sequencers are becoming more important in improving personalized medical treatment. Still, distinguishing genetic variants between healthy and diseased individuals from sequencing errors remains a challenge. Here we present VARIFI, a pipeline for finding reliable genetic variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (indels)). We optimized parameters in VARIFI by analyzing more than 170 amplicon-sequenced cancer samples produced on the Personal Genome Machine (PGM). In contrast to existing pipelines, VARIFI combines different analysis methods and, based on their concordance, assigns a confidence score to each identified variant. Furthermore, VARIFI applies variant filters for biases associated with the sequencing technologies (e.g., incorrectly identified homopolymer-associated indels with Ion Torrent). VARIFI automatically extracts variant information from publicly available databases and incorporates methods for variant effect prediction. VARIFI requires little computational experience and no in-house compute power since the analyses are conducted on our server. VARIFI is a web-based tool available at varifi.cibiv.univie.ac.at.

Indexed as

amplicon sequencingcancerpersonalized medicinepipelinevariant finding

Identifiers

PMID30717290
PMCPMC6463100

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.