標(biāo)題: Titlebook: Copy Number Variants; Methods and Protocol Derek M. Bickhart Book 2018 Springer Science+Business Media, LLC, part of Springer Nature 2018 P [打印本頁(yè)] 作者: Stubborn 時(shí)間: 2025-3-21 19:40
書(shū)目名稱(chēng)Copy Number Variants影響因子(影響力)
書(shū)目名稱(chēng)Copy Number Variants影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Copy Number Variants網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Copy Number Variants網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Copy Number Variants被引頻次
書(shū)目名稱(chēng)Copy Number Variants被引頻次學(xué)科排名
書(shū)目名稱(chēng)Copy Number Variants年度引用
書(shū)目名稱(chēng)Copy Number Variants年度引用學(xué)科排名
書(shū)目名稱(chēng)Copy Number Variants讀者反饋
書(shū)目名稱(chēng)Copy Number Variants讀者反饋學(xué)科排名
作者: 要素 時(shí)間: 2025-3-21 20:27 作者: 伙伴 時(shí)間: 2025-3-22 00:23 作者: 軌道 時(shí)間: 2025-3-22 05:43
1064-3745 tative and cutting-edge, .Copy Number Variants: Methods and Protocols .aims to provide guidance to Bioinformaticians and Molecular Biologists who are interested in identifying copy number variants (CNV) with a wide variety of experimental media978-1-4939-9359-8978-1-4939-8666-8Series ISSN 1064-3745 Series E-ISSN 1940-6029 作者: 模范 時(shí)間: 2025-3-22 10:52 作者: 連系 時(shí)間: 2025-3-22 16:11
https://doi.org/10.1007/978-1-4302-0262-2a tool that incorporates read-pair and split-read signals to identify high confidence CNV regions in a sequenced sample. By combining two different structural variant (SV) signals in variant calling, RAPTR-SV enables the easy filtration of artifact CNV calls from large datasets.作者: 連系 時(shí)間: 2025-3-22 19:54
Use of RAPTR-SV to Identify SVs from Read Pairing and Split Read Signatures,a tool that incorporates read-pair and split-read signals to identify high confidence CNV regions in a sequenced sample. By combining two different structural variant (SV) signals in variant calling, RAPTR-SV enables the easy filtration of artifact CNV calls from large datasets.作者: 連接 時(shí)間: 2025-3-22 23:59
Book 2018y Number Variants: Methods and Protocols .aims to provide guidance to Bioinformaticians and Molecular Biologists who are interested in identifying copy number variants (CNV) with a wide variety of experimental media作者: SOB 時(shí)間: 2025-3-23 03:15
https://doi.org/10.1007/978-1-4302-0262-2e fragment size and read length, which are limiting factors in read pair and split read analysis. Here we provide a guideline for a user friendly tool for detecting large segmental duplications and deletions that can also predict integer copy numbers for duplicated genes.作者: medium 時(shí)間: 2025-3-23 07:46 作者: 偉大 時(shí)間: 2025-3-23 12:50 作者: ABASH 時(shí)間: 2025-3-23 15:08
https://doi.org/10.1007/978-1-4302-0262-2sts, resources, and analysis time. This chapter provides an overview of the various approaches to CNV detection via NGS data, and examines VS-CNV, a commercial tool developed by Golden Helix, which provides robust CNV calling capabilities for both gene panel and exome data.作者: 利用 時(shí)間: 2025-3-23 20:34 作者: outer-ear 時(shí)間: 2025-3-23 22:38 作者: GEM 時(shí)間: 2025-3-24 05:47
Read Depth Analysis to Identify CNV in Bacteria Using CNOGpro,e resultant read depth at each position in the genome. We here provide instructions on how to analyze this read depth signal with the R package CNOGpro, allowing for estimation of copy numbers with uncertainty for each feature in a genome.作者: Indurate 時(shí)間: 2025-3-24 07:23 作者: 榨取 時(shí)間: 2025-3-24 14:00
Detection of CNVs in NGS Data Using VS-CNV,sts, resources, and analysis time. This chapter provides an overview of the various approaches to CNV detection via NGS data, and examines VS-CNV, a commercial tool developed by Golden Helix, which provides robust CNV calling capabilities for both gene panel and exome data.作者: 弄污 時(shí)間: 2025-3-24 16:11 作者: micronized 時(shí)間: 2025-3-24 19:55
Identification of Copy Number Variants from SNP Arrays Using PennCNV,e 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.作者: exceptional 時(shí)間: 2025-3-25 02:06
Statistical Detection of Genome Differences Based on CNV Segments,body traits based on a CNV segmentation strategy that condenses calls from multiple different sources into a genotype state. Here, we provide a guideline of how to generate CNV segments from known CNV results, and how to detect genome differences based on CNV segments.作者: Collected 時(shí)間: 2025-3-25 04:20 作者: GUMP 時(shí)間: 2025-3-25 08:40
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.作者: 難解 時(shí)間: 2025-3-25 11:55
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.作者: 能量守恒 時(shí)間: 2025-3-25 18:27
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.作者: 魅力 時(shí)間: 2025-3-25 23:06 作者: 提煉 時(shí)間: 2025-3-26 00:38
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.作者: 縮影 時(shí)間: 2025-3-26 05:31 作者: FLORA 時(shí)間: 2025-3-26 10:58
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.作者: Adenocarcinoma 時(shí)間: 2025-3-26 14:32 作者: 杠桿 時(shí)間: 2025-3-26 16:52
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.作者: indicate 時(shí)間: 2025-3-27 00:49
https://doi.org/10.1007/978-1-4302-0262-2 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.作者: 桶去微染 時(shí)間: 2025-3-27 02:05 作者: conception 時(shí)間: 2025-3-27 07:10 作者: MULTI 時(shí)間: 2025-3-27 12:58
Structural Variant Breakpoint Detection with novoBreak,c Mutation Calling Challenge. Here we describe detailed instructions of applying novoBreak (.), an open-source software, for somatic SVs detection. We also briefly introduce how to detect germline SVs using novoBreak pipeline and how to use the Workflow (.) of novoBreak on the Seven Bridges Cancer Genomics Cloud.作者: 愛(ài)社交 時(shí)間: 2025-3-27 14:36 作者: Blood-Clot 時(shí)間: 2025-3-27 18:06 作者: 苦澀 時(shí)間: 2025-3-27 23:51
Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Neon and characterization of SCNA landscape at genome-wide scale are of great importance. Next-generation sequencing and SNP array are current technology of choice for SCNA analysis. They are able to quantify SCNA with high resolution and meanwhile raise great challenges in data analysis. To this end,作者: 南極 時(shí)間: 2025-3-28 04:41
Statistical Detection of Genome Differences Based on CNV Segments, boundaries of CNVs in different individuals that causes changes in frequency. Multiple studies have reported CNV regions associated with diseases or body traits based on a CNV segmentation strategy that condenses calls from multiple different sources into a genotype state. Here, we provide a guidel作者: ACRID 時(shí)間: 2025-3-28 08:54 作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-28 10:28 作者: stroke 時(shí)間: 2025-3-28 17:15 作者: 調(diào)整 時(shí)間: 2025-3-28 20:48 作者: 異端邪說(shuō)2 時(shí)間: 2025-3-28 23:57
Detecting Small Inversions Using SRinversion,other variations, such as point mutations, insertions, and deletions, which can be identified in high sensitivities and specificities based on NGS reads, most of inversions, especially those shorter than 1?kb, remain difficult to detect. Here we introduce a new framework, SRinversion, which was deve作者: cacophony 時(shí)間: 2025-3-29 04:29 作者: 懲罰 時(shí)間: 2025-3-29 07:22
Structural Variant Breakpoint Detection with novoBreak,ancer genomics era, detecting structural variations from short sequencing data is still challenging. We developed a novel algorithm, novoBreak (Chong et al. Nat Methods 14:65–67, 2017), which achieved the highest balanced accuracy (mean of sensitivity and precision) in the ICGC-TCGA DREAM 8.5 Somati作者: obtuse 時(shí)間: 2025-3-29 12:40 作者: 寡頭政治 時(shí)間: 2025-3-29 18:59 作者: 稀釋前 時(shí)間: 2025-3-29 23:06
A Randomized Iterative Approach for SV Discovery with SVelter,ss of genetic variation has had on human health and disease. In spite of the recent advances in sequencing technology and discovery methodology, there are still considerable amount of variants in the genome that are partially or completely misinterpreted. The computational tool introduced in this ch作者: B-cell 時(shí)間: 2025-3-30 02:32
Analysis of Population-Genetic Properties of Copy Number Variations, insertions, deletions and duplications of genomic sequences, is also an informative type of genetic variation. CNVs have been shown to be both common in mammals and important for understanding the relationship between genotype and phenotype. Moreover, population-specific CNVs are candidate regions 作者: 欲望 時(shí)間: 2025-3-30 07:38
Validation of Genomic Structural Variants Through Long Sequencing Technologies,a dearth of approaches to evaluate their results. This is significant, as the accurate identification of SVs is still an outstanding problem whereby no single algorithm has been shown to be able to achieve high sensitivity and specificity across different classes of SVs. The method introduced in thi作者: Guaff豪情痛飲 時(shí)間: 2025-3-30 09:05 作者: 大火 時(shí)間: 2025-3-30 15:11
https://doi.org/10.1007/978-1-4939-8666-8PBHoney; DeAnnCNV; MELT; PINDEL; aCGH作者: 文字 時(shí)間: 2025-3-30 20:27 作者: 無(wú)聊點(diǎn)好 時(shí)間: 2025-3-30 21:32 作者: 單調(diào)性 時(shí)間: 2025-3-31 03:01
https://doi.org/10.1007/978-1-4302-0262-2ants is crucial for understanding cell function, evolution and diseases in living organisms. In this chapter, we describe a detailed protocol that uses ., a split-read algorithm, to discover indels and structural variants in a given genome, from Illumina short-read sequencing data produced from biological samples.作者: MEN 時(shí)間: 2025-3-31 08:05 作者: NUDGE 時(shí)間: 2025-3-31 10:55
