rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
about
GROM-RD: resolving genomic biases to improve read depth detection of copy number variantsComputational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectivesVisualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and futureDetection of Genomic Structural Variants from Next-Generation Sequencing DataA survey of copy-number variation detection tools based on high-throughput sequencing data.Unraveling genomic variation from next generation sequencing data.Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data.PSE-HMM: genome-wide CNV detection from NGS data using an HMM with Position-Specific Emission probabilities.Copy number variation detection using next generation sequencing read counts.cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rateCorrecting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data.Common copy number variation detection from multiple sequenced samplesRobust Detection and Identification of Sparse Segments in Ultra-High Dimensional Data Analysis.Comparative studies of copy number variation detection methods for next-generation sequencing technologiesCNV-TV: a robust method to discover copy number variation from short sequencing reads.Detecting structural variations in the human genome using next generation sequencing.Detection of somatic copy number alterations in cancer using targeted exome capture sequencing.Mediation of Drosophila autosomal dosage effects and compensation by network interactions.Detection and interpretation of genomic structural variation in health and disease.WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.Clinical Applications of Next-Generation Sequencing in Cancer Diagnosis.PSCC: sensitive and reliable population-scale copy number variation detection method based on low coverage sequencingDetailed Secondary Structure Models of Invertebrate 7SK RNAs.Detecting differential copy number variation between groups of samples.Beta approximation of ratio distribution and its application to next generation sequencing read counts.Beta approximation of ratio distribution and its application to next generation sequencing read counts.MSeq-CNV: accurate detection of Copy Number Variation from Sequencing of Multiple samples.
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rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
description
2010 nî lūn-bûn
@nan
2010 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@ast
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@en
type
label
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@ast
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@en
prefLabel
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@ast
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@en
P2093
P2860
P356
P1433
P1476
rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.
@en
P2093
Lovelace J Luquette
Peter J Park
Tae-Min Kim
P2860
P2888
P356
10.1186/1471-2105-11-432
P577
2010-08-18T00:00:00Z
P5875
P6179
1047796464