Streamline CNV - A Handy Nextflow Pipeline for Chromosomal Copy Number Variation Analysis
Background Aneuploidy, characterized by imbalanced chromosomal copy number variation (CNV), occurs in both normal somatic tissues and a wide range of diseases. Low pass whole genome sequencing (LP-WGS) to detect CNVs in cells and tissues has been widely used in all biological research. Existing informatics tools for detecting CNV often require computational expertise, often posing challenges for wet-lab biologists. To address this need, we developed Streamline CNV (SLCNV) - a user-friendly and flexible copy number analysis pipeline that allows researchers to perform many copy number-related analyses steps with a single, straightforward command. Results Streamline CNV is based on Nextflow and is packaged in a Docker image that can be executed using Singularity, making it portable and reproducible across different user environments. The pipeline automatically processes input data, performs sequencing quality control to filter problematic samples, outputs mapping statistics, CNV calling, and generates outputs including intuitive CNV plots, segmentation files (with statistics), annotation of genes on altered chromosomes, evaluation of proportion of chromosomal regions that are non-euploid. Based on user preferences, it can cluster samples solely based on CNV patterns, map the genomic location and density of genes of interest along chromosomes, and visually highlight regions with evident CNVs. In addition, it allows users to select and order chromosomes for display in plots, define sample label color palettes, and choose whether to include sample labels in the clustering visualizations. It also allows users to specify the amount of computing resources to allocate to the pipeline, such as CPU, memory, and runtime. Streamline CNV can be customizable and upgradable. It works with any species and genome assembly and efficiently supports both single-cell and bulk sequencing data with improved performance coding. Jobs can be distributed across cores of a basic Unix workstation, multiple nodes in a high-performance computing cluster using the Slurm scheduler, or cloud environments, as needed. Conclusions We demonstrated the tool using an in-house sequencing dataset of human and mouse cell lines. Our results showed that the Streamline CNV pipeline effectively detected copy number patterns and generated informative summaries. It annotated the findings by identifying affected genes within the copy-number segments and at the break-end regions. It also reported the proportions of altered chromosomal regions impacted by copy number changes for each sample. The clustering plot demonstrated the relationships between samples based on their copy number profiles. Gene density plots aid in interpreting how genes from distinct pathways are associated with CNV pattern alterations. This tool enables researchers, regardless of bioinformatics expertise, to perform comprehensive CNV analyses across a wide range of scenarios. It also facilitates the interpretation of the copy number results and makes it possible for transforming raw CNV data into further exploration of meaningful biological insights.