Dataset Information

Copy Number Variation and Allele Ratio Analysis in Candida albicans Using Whole Genome Sequencing Data.

ABSTRACT: Whole genome sequencing of human fungal pathogens has revolutionized the speed and accuracy in which sequence variants that cause antifungal resistance can be identified. Genome rearrangements resulting in copy number variation (CNV) are a significant source of acquired antifungal drug resistance across diverse fungal species. Some CNVs are transient in nature, while other CNVs are stable and well tolerated even in the absence of antifungal drugs. By visualizing whole genome sequencing read depth as a function of genomic location, CNVs and CNV breakpoints (genomic positions where the copy number changes occur relative to the rest of the genome) are rapidly identified. A similar analysis can be used to visualize allele ratio changes that occur across the genomes of heterozygous fungal species, both in the presence and absence of CNVs. This protocol walks through the bioinformatic analysis of CNVs and allele ratios utilizing free, open-source visualization tools. We provide code to use with an example dataset (matched antifungal drug-sensitive and drug-resistant Candida albicans isolates) and notes on how to expand this protocol to other fungal genomes.

SUBMITTER: Todd RT

PROVIDER: S-EPMC10676714 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Copy Number Variation and Allele Ratio Analysis in Candida albicans Using Whole Genome Sequencing Data.

Todd Robert T RT Selmecki Anna A

Methods in molecular biology (Clifton, N.J.) 20230101

Whole genome sequencing of human fungal pathogens has revolutionized the speed and accuracy in which sequence variants that cause antifungal resistance can be identified. Genome rearrangements resulting in copy number variation (CNV) are a significant source of acquired antifungal drug resistance across diverse fungal species. Some CNVs are transient in nature, while other CNVs are stable and well tolerated even in the absence of antifungal drugs. By visualizing whole genome sequencing read dept ...[more]

PMID: 37024698

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Project description:BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Dataset Information

Copy Number Variation and Allele Ratio Analysis in Candida albicans Using Whole Genome Sequencing Data.

Publications

Copy Number Variation and Allele Ratio Analysis in Candida albicans Using Whole Genome Sequencing Data.

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