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Titlebook: Copy Number Variants; Methods and Protocol Derek M. Bickhart Book 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 P

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樓主: Stubborn
21#
發(fā)表于 2025-3-25 04:20:02 | 只看該作者
22#
發(fā)表于 2025-3-25 08:40:08 | 只看該作者
A Randomized Iterative Approach for SV Discovery with SVelter, are still considerable amount of variants in the genome that are partially or completely misinterpreted. The computational tool introduced in this chapter, SVelter, is specifically designed to detect and resolve genomic SVs in all different formats, including the canonical as well as the complex.
23#
發(fā)表于 2025-3-25 11:55:22 | 只看該作者
Analysis of Population-Genetic Properties of Copy Number Variations, in mammals and important for understanding the relationship between genotype and phenotype. Moreover, population-specific CNVs are candidate regions under selection and are potentially responsible for diverse phenotypes.
24#
發(fā)表于 2025-3-25 18:27:10 | 只看該作者
U.S. Web Accessibility Law in Depth we have developed an R package . for SCNA analysis using (1) whole-genome sequencing (WGS), (2) whole-exome sequencing (WES) or (3) whole-genome SNP array data. In this chapter, we provide the features of the package and step-by-step instructions in detail.
25#
發(fā)表于 2025-3-25 23:06:40 | 只看該作者
26#
發(fā)表于 2025-3-26 00:38:41 | 只看該作者
https://doi.org/10.1007/978-1-4302-0262-2 novo events. Here we show how Bionano Genome Mapping creates de novo assemblies from native and intact, megabase-scale DNA molecules and uses those assemblies to detect a wide range of structural variants.
27#
發(fā)表于 2025-3-26 05:31:22 | 只看該作者
28#
發(fā)表于 2025-3-26 10:58:19 | 只看該作者
https://doi.org/10.1007/978-1-4302-0262-2e using PennCNV includes preparation of input files, CNV calling, filtering CNV calls, CNV annotation, and CNV visualization. Here we describe several protocols for CNV calling using PennCNV, together with descriptions on several recent improvements to the software tool.
29#
發(fā)表于 2025-3-26 14:32:00 | 只看該作者
30#
發(fā)表于 2025-3-26 16:52:21 | 只看該作者
https://doi.org/10.1007/978-1-4302-0262-2ds, most of inversions, especially those shorter than 1?kb, remain difficult to detect. Here we introduce a new framework, SRinversion, which was developed specifically for detection of inversions shorter than 1?kb by splitting and realigning poorly mapped or unmapped reads of the NGS data.
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