CNVDetector: locating copy number variations using array CGH data

Chen, Peng-An; Liu, Hsiao-Fei; Chao, Kun-Mao

doi:10.1093/bioinformatics/btn517

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…To locate copy-number variation (CNV) candidates using the alignment results, we first obtained the depth of each base along the reference genome using SOAPcoverage (http://soap.genomics.org.cn/). We then used CNVDetector (Chen et al 2008), a program developed by BGI, to calculate the mean depth of 100-bp sliding windows along each chromosome and to select the candidate regions of CNV based on the difference of depth between each consecutive window and the overall mean. Events with a high absolute difference in depth ( i.e.…”

Section: Methodsmentioning

confidence: 99%

Whole-Genome Sequences of DA and F344 Rats with Different Susceptibilities to Arthritis, Autoimmunity, Inflammation and Cancer

et al. 2013

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DA (D-blood group of Palm and Agouti, also known as Dark Agouti) and F344 (Fischer) are two inbred rat strains with differences in several phenotypes, including susceptibility to autoimmune disease models and inflammatory responses. While these strains have been extensively studied, little information is available about the DA and F344 genomes, as only the Brown Norway (BN) and spontaneously hypertensive rat strains have been sequenced to date. Here we report the sequencing of the DA and F344 genomes using next-generation Illumina paired-end read technology and the first de novo assembly of a rat genome. DA and F344 were sequenced with an average depth of 32-fold, covered 98.9% of the BN reference genome, and included 97.97% of known rat ESTs. New sequences could be assigned to 59 million positions with previously unknown data in the BN reference genome. Differences between DA, F344, and BN included 19 million positions in novel scaffolds, 4.09 million single nucleotide polymorphisms (SNPs) (including 1.37 million new SNPs), 458,224 short insertions and deletions, and 58,174 structural variants. Genetic differences between DA, F344, and BN, including high-impact SNPs and short insertions and deletions affecting >2500 genes, are likely to account for most of the phenotypic variation between these strains. The new DA and F344 genome sequencing data should facilitate gene discovery efforts in rat models of human disease.

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Section: Methodsmentioning

confidence: 99%

Whole-Genome Sequences of DA and F344 Rats with Different Susceptibilities to Arthritis, Autoimmunity, Inflammation and Cancer

et al. 2013

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“…The method in [45] has been implemented in "Stepgram" (http: //bioinfo.cs.technion.ac.il/-stepgram/). It is also available, with fewer assumptions on noise distribution (but, apparently, without the option for "class discovery" -location of regions over subsets of samples), in [47]. However, these two programs are only distributed as Windows executables, and source code in not available.…”

Section: Interval Scores [45] and Cocoa [46]mentioning

confidence: 99%

Finding Recurrent Copy Number Alteration Regions: A Review of Methods

Rueda¹,

Dı́az-Uriarte

2010

CBIO

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Copy number alterations (CNA) in genomic DNA are linked to a variety of human diseases. Although many methods have been developed to analyze data from a single subject, disease-critical genes are more likely to be found in regions that are common or recurrent among diseased subjects. Unfortunately, finding recurrent CNA regions remains a challenge. We review existing methods for the identification of recurrent CNA regions. Methods differ in their working definition of "recurrent region", the type of input data, the statistical and computational methods used to identify recurrence, and the biological considerations they incorporate (which play a role in the identification of "interesting" regions and in the details of null models used to assess statistical significance). Very few approaches use and/or return probabilities, and code is not easily available for several methods. We emphasize that, when analyzing data from complex diseases with significant among-subject heterogeneity, methods should be able to identify CNAs that affect only a subset of subjects. We suggest that finding recurrent CNAs would benefit from clearly specifying the types of pattern to be detected and the intended usage of the regions found (CNA association with disease, CNA effects on gene expression, clustering of subjects). We finish with suggestions for further methodological research.

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“…PennCNV (5) is a popular option for SNP array analysis, implementing a hidden Markov model algorithm. A number of other CNV calling options are available, including QuantiSNP (6), CNVCALL (7), CNVDetector (8), CGHCall (9) and CNV-Seq (10), all of which are publicly available tools and highly enabling to researchers.…”

Section: Introductionmentioning

confidence: 99%

ParseCNV integrative copy number variation association software with quality tracking

Glessner

Jin

Hákonarson

2013

Nucleic Acids Research

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A number of copy number variation (CNV) calling algorithms exist; however, comprehensive software tools for CNV association studies are lacking. We describe ParseCNV, unique software that takes CNV calls and creates probe-based statistics for CNV occurrence in both case–control design and in family based studies addressing both de novo and inheritance events, which are then summarized based on CNV regions (CNVRs). CNVRs are defined in a dynamic manner to allow for a complex CNV overlap while maintaining precise association region. Using this approach, we avoid failure to converge and non-monotonic curve fitting weaknesses of programs, such as CNVtools and CNVassoc, and although Plink is easy to use, it only provides combined CNV state probe-based statistics, not state-specific CNVRs. Existing CNV association methods do not provide any quality tracking information to filter confident associations, a key issue which is fully addressed by ParseCNV. In addition, uncertainty in CNV calls underlying CNV associations is evaluated to verify significant results, including CNV overlap profiles, genomic context, number of probes supporting the CNV and single-probe intensities. When optimal quality control parameters are followed using ParseCNV, 90% of CNVs validate by polymerase chain reaction, an often problematic stage because of inadequate significant association review. ParseCNV is freely available at http://parsecnv.sourceforge.net.

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CNVDetector: locating copy number variations using array CGH data

Cited by 10 publications

References 10 publications

Whole-Genome Sequences of DA and F344 Rats with Different Susceptibilities to Arthritis, Autoimmunity, Inflammation and Cancer

Whole-Genome Sequences of DA and F344 Rats with Different Susceptibilities to Arthritis, Autoimmunity, Inflammation and Cancer

Finding Recurrent Copy Number Alteration Regions: A Review of Methods

ParseCNV integrative copy number variation association software with quality tracking

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