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

Autor: Peter Venhuizen, Arndt von Haeseler, Milica Krunic, Bettina Kaserer, Leonhard Müllauer
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Journal of Personalized Medicine, Vol 9, Iss 1, p 10 (2019)
Journal of Personalized Medicine
Volume 9
Issue 1
ISSN: 2075-4426
Popis: 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.
Databáze: OpenAIRE