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Copy number variation (CNV) is a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals. [1] Copy number variation is a type of structural variation: specifically, it is a type of duplication or deletion event that affects a considerable number of base pairs. [2]
Gains: A copy number gain represents the gain of genetic material. If the gain is of just one additional copy of a segment of DNA, it may be called a duplication (Fig 4). If there is one extra copy of an entire chromosome, it may be called a trisomy. Copy number gains in germline samples may be disease-associated or may be a benign copy number ...
Homogeneously staining regions (HSRs) are chromosomal segments with various lengths and uniform staining intensity after G banding.This type of aberration is also known as Copy Number Gains or Amplification.
Copy number analysis is the process of analyzing data produced by a test for DNA copy number variation in an organism's sample. One application of such analysis is the detection of chromosomal copy number variation that may cause or may increase risks of various critical disorders.
Comparative genomic hybridization (CGH) is a molecular cytogenetic method for analysing copy number variations (CNVs) relative to ploidy level in the DNA of a test sample compared to a reference sample, without the need for culturing cells.
Multiplex ligation-dependent probe amplification (MLPA) is a variation of the multiplex polymerase chain reaction that permits amplification of multiple targets with only a single primer pair. [1] It detects copy number changes at the molecular level, and software programs are used for analysis.
On the other hand, dPCR has a higher precision and has been shown to detect differences of less than 30% in gene expression, distinguish between copy number variations that differ by only 1 copy, and identify alleles that occur at frequencies less than 0.1%. [14] [5]
Other examples of emerging RNA-Seq applications due to the advancement of bioinformatics algorithms are copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. [8] Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays.