The Genomic Background of CNV Waves in Whole Genome Sequencing
CNVs encompass everything from small deletions to aneuploidies and are linked with multiple diseases. Unfortunately, detecting them remains a challenging endeavor both using DNA microarrays and next-generation sequencing experiments.
Multiple methods have been proposed to identify and link CNVs with disease-relevant phenotypes, including creating prior knowledge networks (PKNs) from text mining and gene expression data.
1. High-throughput
Genome-wide differences among individuals may result from SNVs (single nucleotide variants), translocations, inversions or copy number variants (CNVs; gain or loss of DNA molecules), mutations, translocations or inversions that lead to copy number variants; CNVs can range from small insertion/deletion mutations up to full aneuploidies in DNA molecules – they may even serve as biomarkers in diagnostic tests! Knowing more about CNVs associated with complex diseases as well as understanding their genomic background remains an area of focus research for many.
Current genome sequencing approaches can detect rare CNVs with an acceptable false discovery rate (FDR), however to reduce false positives it is necessary to create more effective algorithms capable of distinguishing true from false positive CNV calls. Such algorithms should take into account factors like surrounding genes and expression levels when making this determination, as well as functional impact of CNVs; such impact assessment could include protein level changes or changes in Kinase Phosphorylation Status status or engineering them into model organisms so their consequences may be assessed more directly.
Not only can genomic CNV wave whole genome sequencing technology detect CNVs, but it can also be utilized for somatic tumor profiling and clinical applications such as identification of potential drug targets. To do this, specialized sample preparation kits and sequencing platforms with proven performance for advanced genomic applications must be utilized.
Illumina Sequencing Platform offers long-read sequencing capabilities and customizable data analysis tools that are suitable for a range of applications, with reliable reagent and instrument designs, advanced software that processes complex samples efficiently, as well as tools like the BaseSpace Variant Interpreter that help researchers interpret genetic data to identify variants or biological pathways which might compromise patient health.
Genomic CNV Wave Whole Genome Sequencing VEP software provides another helpful resource, prioritizing and filtering CNVs based on several criteria, such as their effect on specific phenotypes, location within known genes, overlap with SNP-GWAS signals and presence or absence of transcription factor binding sites in affected regions as well as whether or not they reside in high information areas of regulatory networks.
2. Fast turnaround
Next-generation sequencing (NGS), an increasingly powerful technology, has dramatically transformed both genetic research and clinical care. NGS allows multiplexed genomic analysis with advanced bioinformatics data curation tools as well as various other omics studies such as transcriptomics, epigenomics and metagenomics – providing high throughput sequencing data valuable for disease variant identification as well as rapid clinical diagnostics.
NGS platform facilitates both whole genome sequencing (WGS) and whole exome sequencing (WES), providing access to genetic variants resulting in SNVs, CNVs, insertions, and deletions within protein-coding genes. This approach can detect SNVs, CNVs, insertions, and deletions associated with genetic diseases as well as cancer. WES involves enrichment of exonic regions using hybrid capture or targeted amplification techniques followed by high throughput sequencing; an alternative option to WGS that still allows identification of numerous disease-causing genetic variations.
Low-pass WGS provides greater genome-wide coverage, more evenly distributed reads and higher resolution for CNV detection than CMA alone. Furthermore, its better accuracy in estimating exact CNV boundaries helps avoid hybridization saturation while longer read lengths and paired-end sequencing can further increase resolution and sensitivity when it comes to CNV identification.
Noting the accuracy of NGS-based CNV analysis depends on many variables, including the sequencing platform used, amount of DNA needed for sequencing, read length, and sequencing mode (paired-end vs single-end). Furthermore, interpreting and reporting CNVs generated from NGS data is no simple task; an incorrect description could cause other healthcare providers to perform family validation for the same variant or misinterpret its clinical significance resulting in inappropriate guidance for treatment of patients.
Genomic CNV Wave Whole Genome Sequencing was recently used for prenatal diagnosis testing on infants in intensive care units with suspected genetic diseases. This method offers much faster turnaround than conventional NGS methods and can even be done within the hospital itself, with results sent directly to their referring physician in days. This new technology may allow families to receive diagnoses faster while also cutting costs at medical facilities.
3. Customizable
CNV discovery in large genome sequencing projects can be difficult due to the noise characteristics of raw sequencing data. Due to factors like extraction techniques, sample storage conditions and immune system status at blood draw time causing different levels of background noise which result in genomic waves in chromosome coverage which over or underestimate CNVs.
Next-Generation Sequencing (NGS) technology offers several advantages over microarrays in terms of high sensitivity and precise breakpoint location, but its use remains restricted due to factors like sample heterogeneity, genomic distance between target sequence and probes used for hybridization, and GC content bias that could influence its signal.
To address these challenges, cnvHiTSeq employs multiple postprocessing and filtering steps. The first is to remove overlapping signals between probes that are close in genomic distance (such as deletion and duplication) while isolated signals likely due to chromosome rearrangements are downweighted due to likely spurious associations.
An additional post-processing step involves discarding CNVs based on their length or confidence score to prevent false positives, which may arise due to misinterpretation of noise in raw sequence data, insufficient sample coverage or longer genomic distances that could indicate sampling errors or chromosome rearrangement. This step is especially important for the detection of larger and higher confidence CNVs.
Another essential step is reducing the chimera rate, or the number of reads spanning two separate genomic regions due to mismatches between original sample DNA and reference genome. This is often encountered when performing WGA due to variability in nucleotide composition between samples; CNV analysis is particularly susceptible as its reads tend to favor regions with lower GC content.
To address this problem, cnvHiTSeq utilizes a three-state model consisting of red (0), gray (1) and green (2) states to assign weights according to an idealized diploid reference genome’s expected allele frequency – this enables it to detect CNVs which would otherwise go undetected by other methods with more rigid state definitions.
4. Affordable
CNVs are more prevalent than single-nucleotide polymorphisms (SNPs) in human genome, and their effects are usually more profound [1, 2]. Yet accurately detecting both large and small CNVs from low-coverage WGS data remains challenging. Traditional methods involve mapping sequence reads back to a reference genome and looking for discrepancies such as missing or overlapped fragments; some methods also take into account distance between adjacent bins or orientation of sequences; however these approaches can produce false positive results due to bias or sequencing errors [3, 3, 4, 5].
Digital-WGS greatly overcomes these limitations, with one recent study showing it could identify single-cell CNVs with 52.4-kb resolution at an ADO rate of just 5%. This high resolution is made possible through addressable control of droplets during all steps of sequencing reactions, providing sufficient release of genomic DNA from chromosomes, uniform amplification, and eliminating allelic dropout.
Another advantage of digital-WGS technology is its affordability; even with high throughput sequencing capabilities, the cost is considerably less than with other whole genome sequencing technologies such as centrifugal emulsion amplification (eMDA).
The method involves using DNA extracted from individual metaphase chromosomes extracted using laser microdissection, microfluidics or flow cytometry as input into a population haplotype construction algorithm for use in discovering CNVs and genotyping them. As different sample types produce different effects upon DNA inputs from population haplotype construction algorithms, probability distributions specific to each data type can be used to model them before creating a CNV calling pipeline that takes into account all sources of information and thus makes this suitable for both large and small CNVs.
