Rare copy number variant analysis in case-control studies using snp array data: a scalable and automated data analysis pipeline.

IF 2.9 3区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

BMC Bioinformatics Pub Date : 2024-11-15 DOI:10.1186/s12859-024-05979-0

Haydee Artaza, Ksenia Lavrichenko, Anette S B Wolff, Ellen C Røyrvik, Marc Vaudel, Stefan Johansson

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引用次数: 0

Abstract

Background: Rare copy number variants (CNVs) significantly influence the human genome and may contribute to disease susceptibility. High-throughput SNP genotyping platforms provide data that can be used for CNV detection, but it requires the complex pipelining of bioinformatic tools. Here, we propose a flexible bioinformatic pipeline for rare CNV analysis from human SNP array data.

Results: The pipeline consists of two major sub-pipelines: (1) Calling and quality control (QC) analysis, and (2) Rare CNV analysis. It is implemented in Snakemake following a rule-based structure that enables automation and scalability while maintaining flexibility.

Conclusions: Our pipeline automates the detection and analysis of rare CNVs. It implements a rigorous CNV quality control, assesses the frequencies of these rare CNVs in patients versus controls, and evaluates the impact of CNVs on specific genes or pathways. We hence aim to provide an efficient yet flexible bioinformatic framework to investigate rare CNVs in biomedical research.

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利用 snp 阵列数据进行病例对照研究中的罕见拷贝数变异分析：可扩展的自动数据分析管道。

背景：罕见拷贝数变异（CNV）对人类基因组有重大影响，并可能导致疾病易感性。高通量 SNP 基因分型平台提供的数据可用于 CNV 检测，但这需要复杂的生物信息工具流水线。在此，我们提出了一种灵活的生物信息学流水线，用于从人类 SNP 阵列数据中分析罕见 CNV：该流水线由两个主要子流水线组成：（1）调用和质量控制（QC）分析；（2）罕见 CNV 分析。它是在 Snakemake 中按照基于规则的结构实现的，在保持灵活性的同时实现了自动化和可扩展性：我们的管道可自动检测和分析罕见 CNV。它实施了严格的 CNV 质量控制，评估了这些罕见 CNV 在患者和对照中的频率，并评估了 CNV 对特定基因或通路的影响。因此，我们的目标是提供一个高效而灵活的生物信息框架，用于研究生物医学研究中的罕见 CNV。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Bioinformatics 生物-生化研究方法

CiteScore

5.70

自引率

3.30%

发文量

506

审稿时长

4.3 months

期刊介绍： BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.