Detection of structural variants svs and copy number variations cnvs on ngs data
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Detection of Structural Variants (SVs) and Copy Number Variations (CNVs) on NGS Data PowerPoint PPT Presentation


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Detection of Structural Variants (SVs) and Copy Number Variations (CNVs) on NGS Data. SVs and CNVs. They are often confused… SVs: regions contain insertions and deletions ( indels ) or inversions. CNVs: regions appearing a different number of times in different individuals.

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Detection of Structural Variants (SVs) and Copy Number Variations (CNVs) on NGS Data

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Detection of Structural Variants (SVs) and Copy Number Variations (CNVs) on NGS Data


SVs and CNVs

  • They are often confused…

  • SVs: regions contain insertions and deletions (indels) or inversions.

  • CNVs: regions appearing a different number of times in different individuals.

  • They are closely related phenomena.

  • SVs are operationally defined as genomic events involving >50bp. They include CNVs as well as rearrangements such as inversions and translocations.


Why studying SVs and CNVs

  • It has been acknowledged only very recently that human genomes differ more as a consequence of SVs (including CNVs) than of single-base differences. [first "hypothesis" in 2004-2005 not taken seriously, and evidence only in 2010 with NGS].

  • In particular, many studies observed CNVs and did genotyping with them: they are the easiest SVs to detect.

  • I am not aware of tools for detecting inversions and translocations of DNA durectly on NGS data

  • There are some tools for detecting CNVs: from now on we discuss them only.


Copy Number Variations

  • The challenge is to discover effects of CNVs on human diseases, complex traits (combination of genetic and environmental effects), and evolution.

  • Genotyping of human CNVs is far from being a routine procedure. This is a limit to personalized medicine, for which CNVs detection is a crucial step.

  • No standard method (many and very recent tools, not yet a fair/sharp comparison) exists.


Detecting CNV

  • Since late nineties and until very recently (and still) CNVs were/are detected using aCGH: Array Comparative Hybridization.

  • The array platform are not as rapid and cheap as NGS, and their data cannot be re-used once processed.

  • The aGCH Array has inherent limits on the size and frequency of detectable CNVs.

  • NGS opened a new era in CNV-detection!


CNV calling

  • There are three approaches for CNV calling:

    • Based on read count (RC), or read alignment coverage (Breakdancer, CNVnator, CNV-seq, and tools of [Campbell et al, 2008], [Alkan et al, 2009], [Sudmant et al, 2010], [Yoon et al, 2009]).

    • Based on paired end reads (PEMer, CNVer, VariationHunter, MoDIL, Breakdancer, tools of [Sindi et al, 2009], [Quinlan et al, 2010]).

    • Based on split-read alignments (Pindel, Mosaik, tool of [Mills et al, 2006]).


Read Count Approaches

  • Measuring the amount of reads mapped to a location in the reference genome.

  • Identify CNV regions

  • Estimating Copy Number

  • Some use a sliding window.

  • Problems when coverage is not uniform within a CNV…


Paired End Reads Approaches

  • Mate/Paired End Reads are mapped on the reference genome.


Split Read Approach

  • A read is not mapped in a single locations because of possible structural variation.

  • All un-aligned reads are split and then mapping is sought again.

  • Iterate to find the actual breakpoint.


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