Evidence map›Paper›PMID 38350858›Full record

ArticleBMC bioinformatics2024

123VCF: an intuitive and efficient tool for filtering VCF files.

Milad Eidi, Samaneh Abdolalizadeh, Soheila Moeini, Masoud Garshasbi, Javad Zahiri

Abstract read
In one paragraph

Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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.

Milad EidiDepartment of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Samaneh AbdolalizadehDepartment of Genetics and Molecular Medicine, School of Medicine, Zanjan University of Medical Sciences (ZUMS), Zanjan, Iran.
Soheila MoeiniDépartement de Biochimie et Médecine Moléculaire, Université de Montréal, Montreal, QC, Canada.
Masoud GarshasbiDepartment of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. masoud.garshasbi@modares.ac.ir.
Javad ZahiriDepartment of Neuroscience, University of California San Diego, San Diego, CA, USA. Jzahiri@health.ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe advent of Next-Generation Sequencing (NGS) has catalyzed a paradigm shift in medical genetics, enabling the identification of disease-associated variants. However, the vast quantum of data produced by NGS necessitates a robust and dependable mechanism for filtering irrelevant variants. Annotation-based variant filtering, a pivotal step in this process, demands a profound understanding of the case-specific conditions and the relevant annotation instruments. To tackle this complex task, we sought to design an accessible, efficient and more importantly easy to understand variant filtering tool.

resultsOur efforts culminated in the creation of 123VCF, a tool capable of processing both compressed and uncompressed Variant Calling Format (VCF) files. Built on a Java framework, the tool employs a disk-streaming real-time filtering algorithm, allowing it to manage sizable variant files on conventional desktop computers. 123VCF filters input variants in accordance with a predefined filter sequence applied to the input variants. Users are provided the flexibility to define various filtering parameters, such as quality, coverage depth, and variant frequency within the populations. Additionally, 123VCF accommodates user-defined filters tailored to specific case requirements, affording users enhanced control over the filtering process. We evaluated the performance of 123VCF by analyzing different types of variant files and comparing its runtimes to the most similar algorithms like BCFtools filter and GATK VariantFiltration. The results indicated that 123VCF performs relatively well. The tool's intuitive interface and potential for reproducibility make it a valuable asset for both researchers and clinicians.

conclusionThe 123VCF filtering tool provides an effective, dependable approach for filtering variants in both research and clinical settings. As an open-source tool available at https://project123vcf.sourceforge.io , it is accessible to the global scientific and clinical community, paving the way for the discovery of disease-causing variants and facilitating the advancement of personalized medicine.

Indexed as

AlgorithmsSoftwareHigh-Throughput Nucleotide SequencingReproducibility of ResultsExome sequencingGenome sequencingNext generation sequencingVariant analysisVariant filteringVCFVCF filtering

Identifiers

PMID38350858
PMCPMC10865685

What OpenQuestion holds

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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.