Vcfanno: fast, flexible annotation of genetic variants

Pedersen, Brent S.; Layer, Ryan M.; Quinlan, Aaron R.

doi:10.1186/s13059-016-0973-5

Cited by 154 publications

(106 citation statements)

References 23 publications

(12 reference statements)

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“…Based on our positive experiences, we recommend authors of other bionformatics tools for processing SAM/BAM data, and potentially also other sequencing data formats, to also consider Go as an implementation language. Previous bioinformatics tools that are implemented in Go include bíogo [45], Fastcov [46], SeqKit [47], and Vcfanno [48], among others.…”

Section: Discussionmentioning

confidence: 99%

A comparison of three programming languages for a full-fledged next-generation sequencing tool

Costanza¹,

Herzeel²,

Verachtert³

2019

Preprint

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Background elPrep is an established multi-threaded framework for preparing SAM and BAM files in sequencing pipelines. To achieve good performance, its software architecture makes only a single pass through a SAM/BAM file for multiple preparation steps, and keeps sequencing data as much as possible in main memory. Similar to other SAM/BAM tools, management of heap memory is a complex task in elPrep, and it became a serious productivity bottleneck in its original implementation language during recent further development of elPrep. We therefore investigated three alternative programming languages: Go and Java using a concurrent, parallel garbage collector on the one hand, and C++17 using reference counting on the other hand for handling large amounts of heap objects. We reimplemented elPrep in all three languages and benchmarked their runtime performance and memory use.Results The Go implementation performs best, yielding the best balance between runtime performance and memory use. While the Java benchmarks report a somewhat faster runtime than the Go benchmarks, the memory use of the Java runs is significantly higher. The C++17 benchmarks run signif- * Equal contributor

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Section: Discussionmentioning

confidence: 99%

A comparison of three programming languages for a full-fledged next-generation sequencing tool

Costanza¹,

Herzeel²,

Verachtert³

2019

Preprint

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“…The programmed script for the database displays the variants. The annotation was performed using relatively accepted public programs including TransVar for the coordinate conversion [18], vcfanno for the table annotation [8], Variant Effect Predictor to convert the g.HGVS format to the VCF format [16], CrossMap for the lift-over between hg10 and GRCh38 [17], and InterVar for the semi-automatic variant scoring [5].…”

Section: Methodsmentioning

confidence: 99%

Japanese pathogenic variant database: DPV

Suzuki

Kurosawa

Fukuda

et al. 2018

TRD

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Abstract. Databases of pathogenic variants form the basis for clinical genomic diagnosis using next-generation sequencers. ClinVar and the Human Gene Mutation Database (HGMD) are two major databases for pathogenic variants. However, ethnic diversity in the distributions of pathogenic variants in these databases has been observed; thus, geographic region-specific variant databases are required. We established a Japanese pathogenic variant database in 2016 and began registering pathogenic variants from published articles written by Japanese researchers. The criteria for registration are as follows: 1) the variant has been validated using Sanger sequencing, 2) the minor allele frequency of the variant is lower than 0.03 in the normal Japanese population, and 3) the clinical features of the patient have been critically evaluated by multiple clinicians and geneticists. All registered variants can be downloaded as an aggregated single VCF file to use in laboratories. We are presently curating variants from more than 2000 articles and estimate the registration of >10,000 pathogenic variants derived from the Japanese population. Although the number of registered variants remains relatively small, the clinical genetics community in Japan has begun to work on this initiative in a concerted manner.

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“…These customized annotation databases are private and only accessible to the uploader. Tools used on VCF-Server for annotation are ANNOVAR (Yang & Wang, 2015), SnpSift (Cingolani, Patel et al, 2012), and vcfanno (Pedersen, Layer, & Quinlan, 2016).…”

Section: Variant Annotation With Commonly Used Databasesmentioning

confidence: 99%

VCF‐Server: A web‐based visualization tool for high‐throughput variant data mining and management

Jiang

Zhao

et al. 2019

Molec Gen & Gen Med

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Background Next‐generation sequencing (NGS) has been widely used in both clinics and research. It has become the most powerful tool for diagnosing genetic disorders and investigating disease etiology through the discovery of genetic variants. Variants identified by NGS are stored in variant call format (VCF) files. However, querying and filtering VCF files are extremely difficult for researchers without programming skills. Furthermore, as the mutation data are increasing exponentially, there is an urgent need to develop tools to manage these variant data in a centralized way. Methods The VCF‐Server was developed as a web‐based visualization tool to support the interactive analysis of genetic variant data. It allows researchers and medical geneticists to manage, annotate, filter, query, and export variants in a fast and effective way. Results In this study, we developed the VCF‐Server, a powerful and easily accessible tool for researchers and medical geneticists to perform variant analysis. Users can query VCFs, annotate, and filter variants without knowing programming code. Once the VCF file is uploaded, VCF‐Server allows users to annotate the VCF with commonly used databases or user‐defined variant annotations (including variant blacklist and whitelist). Variant information in the VCF is shown visually via the interactive graphical interface. Users can filter the variants with flexible filtering rules, and the prioritized variants can be exported locally for further analysis. As VCF‐Server adopts a web file system, files in the VCF‐Server can be stored and managed in a centralized way. Moreover, VCF‐Server allows direct web‐based analysis (accessible through either desktop computers or mobile devices) as well as local deployment. Conclusions With an easy‐to‐use graphical interface, VCF‐Server allows researchers with little bioinformatics background to explore and mine mutation data, which may broaden the application of NGS technology in clinics and research. The tool is freely available for use at https://www.diseasegps.org/VCF-Server?lan = eng .

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Vcfanno: fast, flexible annotation of genetic variants

Cited by 154 publications

References 23 publications

A comparison of three programming languages for a full-fledged next-generation sequencing tool

A comparison of three programming languages for a full-fledged next-generation sequencing tool

Japanese pathogenic variant database: DPV

VCF‐Server: A web‐based visualization tool for high‐throughput variant data mining and management

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