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Identifies copy number variations (CNVs) from raw copy number. COKGEN consists in a configurable platform for CNV identification that allows users to: (1) adjust the parameters of our default formulation to tune the behavior of the method to the target application; and (2) specify their own target objective functions and tune parameters to emphasize relative importance of different objective criteria. The software has been tested on Affymetrix 6.0 array data from 270 HapMap individuals.
A free software tool for copy number variation (CNV) detection from SNP genotyping arrays. PennCNV can handle signal intensity data from Illumina and Affymetrix arrays. With appropriate preparation of file format, it can also handle other types of SNP arrays and oligonucleotide arrays. PennCNV implements a hidden Markov model (HMM) that integrates multiple sources of information to infer CNV calls for individual genotyped samples. It differs from segmentation-based algorithm in that it considered SNP allelic ratio distribution as well as other factors, in addition to signal intensity alone. In addition, PennCNV can optionally utilize family information to generate family-based CNV calls by several different algorithms. Furthermore, PennCNV can generate CNV calls given a specific set of candidate CNV regions, through a validation-calling algorithm.
A package for array-based CNV (Copy Number Variation) analysis which is designed to control the FDR (False Discovery Rate) while ensuring high sensitivity. For controlling the FDR, we propose a probabilistic latent variable model, cn.FARMS, which is optimized by a Bayesian maximum a posteriori approach. cn.FARMS controls the FDR through the information gain of the posterior over the prior. The prior represents the null hypothesis of copy number 2 for all samples from which the posterior can only deviate by strong and consistent signals in the data. In experiments, cn.FARMS outperformed its competitors both with respect to FDR and sensitivity, i.e. has fewer false positives while detecting more true CNVs. The reduced FDR increases the discovery power of studies and avoids that researchers are misguided by spurious correlations between CNVs and diseases.
GLAD / Gain and Loss Analysis of DNA
A package for the automatic detection of breakpoints from array CGH profile, and the assignment of a status to each chromosomal region. The breakpoint detection step of GLAD is based on the Adaptive Weights Smoothing (AWS) procedure and provides highly convincing results: our algorithm detects 97, 100 and 94% of breakpoints in simulated data, karyotyping results and manually analyzed profiles, respectively. The percentage of correctly assigned statuses ranges from 98.9 to 99.8% for simulated data and is 100% for karyotyping results.
A fast and accurate algorithm for assigning single nucleotide polymorphism (SNP) genotypes to microarray data from the Illumina BeadArray technology. The algorithm can assign genotypes to hybridization data from thousands of individuals simultaneously and pools information across multiple individuals to improve the calling. The method can accommodate variations in hybridization intensities which result in dramatic shifts of the position of the genotype clouds by identifying the optimal coordinates to initialize the algorithm. By incorporating the process of perturbation analysis, we can obtain a quality metric measuring the stability of the assigned genotype calls.
A genotype-calling algorithm for Illumina arrays that uses both SNP-wise and sample-wise calling to more accurately ascertain genotypes at rare, low-frequency and common variants, even when genotype intensity clouds are shifted from their expected positions. optiCall works by first taking a random subset of intensity measures, both within and across samples. The subset is used to define regions of high probability for the three genotype classes. Genotypes are then called on a per SNP basis, with all samples overlaid onto the probability regions, which are incorporated as a data-derived.prior during clustering. In this way common variants are seen as three clouds in a per SNP view, and rare variants are called based on the intensity region in which they fall.
A web-based tool that applies a number of popular algorithms to a single array CGH profile entered by the user. CGHweb generates a heatmap panel of the segmented profiles for each method as well as a consensus profile. The clickable heatmap can be moved along the chromosome and zoomed in or out. It also displays the time that each algorithm took and provides numerical values of the segmented profiles for download. The web interface calls algorithms written in the statistical language R.
Implements a multilevel model adjusting for batch effects and providing allele-specific estimates of copy number. The CRLMM algorithm estimates genotypes through a hierarchical model for the log ratios of A:B intensities that accounts for the dependency on intensity strength, batch effects, and the uncertainty of parameters estimated from the training step. For each platform design supported by CRLMM, we provide one annotation package that contains parameters estimated from the training data for every SNP-genotype combination.
Contains classes and methods useful for storing, visualizing and analyzing high density SNP data. Originally developed from the SNPscan web-tool, SNPchip utilizes S4 classes and extends other open source R tools available at Bioconductor. We extended the classes defined in Biobase to accommodate SNP chip data and have added methods useful for producing visual and descriptive summaries. In particular, the plotting methods are useful for identifying regions of probable chromosomal anomalies, with the capability of producing both broad (genome-wide) and focused (chromosome-specific) views of copy number and genotype data.
A quality score for copy number variants (CNVs) detected by PennCNV, prior to performing CNV-based association studies. Our contribution can be viewed as a post-processing step of PennCNV calls, whereby various CNV metrics are combined to estimate the probability of a called CNV to be a likely consensus call. This probability could then be used as copy number dosage for trait associations. We chose to improve CNV detection in a way that is directly applicable for large meta-analytic GWAS, where analysts preferably want to run only a single and fast CNV calling pipeline and provide association summary statistics from various platforms.
An R package for processing of Affymetrix CytoScan HD, CytoScan 750k and SNP 6.0 microarray raw intensities (CEL files). Rawcopy uses data from a large number of reference samples to produce log ratio for total copy number analysis and B-allele frequency for allele-specific copy number and heterozygosity analysis. Rawcopy achieves higher signal-to-noise ratio than commonly used free and proprietary alternatives, leading to improved identification of copy number alterations. In addition, Rawcopy visualises each microarray sample for assessment of technical quality, patient identity and genome-wide absolute copy number states.
SiDCoN / Simulated DNA Copy Number
Allows interpretation of complex regions of change. SiDCoN can be useful for training researchers to accurately score whole-genome profiles in the presence of significant stromal contamination. It can serve to the estimation of the level of stromal contamination within a tumour sample. This tool offers a way to users to assess a wide variety of SNP-aCGH data interpretations. It can be employed to estimate the stromal contamination rate within tumour biopsies.
An online platform to streamline the processing and downstream interpretation of microarray data in a clinical context, tailored towards but not limited to the Illumina BeadArray platform. Provided analysis tools include CNV analysis, parent of origin and uniparental disomy detection. Interpretation tools include data visualisation, gene prioritization, automated PubMed searching, linking data to several genome browsers and annotation of CNVs based on several public databases. Finally a module is provided for uniform reporting of results.
NBC / Neighborhood Breakpoint Conservation
Detects recurrent breakpoints in copy number data. NBC computes the probability that a breakpoint occurs between each pair of adjacent probes over all possible segmentations of a single copy number profile. It combines these probabilities across multiple profiles to find recurrent breakpoints. This tool is useful for profiles obtained from next-generation DNA sequencing data. It aims to uncover and categorize recurrent germline and somatic rearrangements.
Detects copy number variation (CNV) regions and provides a visual inspection function to reduce false positive CNV calls based on comparative genomic hybridization arrays (aCGH) and next generation sequencing (NGS) data. Genovar consists of three major components: (i) visualizes aCGH data and sequence alignment of chromosomal regions; (ii) provides a read-depth plot, and (iii) summary information of each read when a certain read is selected in the panel. The program also provides comprehensive information to help in the elimination of spurious signals by visual inspection, making Genovar a valuable tool for reducing false positive CNV results.
SubPatCNV / Subspace Pattern-ming of Copy Number Variations
A data mining tool for discovery of CNV regions that exhibit in subsets of samples larger than a support threshold. SubPatCNV is suitable for analysis of arrayCGH data of a population or a patient cohort such as HapMap data or TCGA data to answer specific questions like "Which are all the chromosomal fragments showing nearly identical deletions or insertions in more than 30% of the individuals in the HapMap population or TCGA tumor samples?".
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