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XHMM / eXome-Hidden Markov Model
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Extracts copy-number signal from noisy read depth by leveraging the large-scale nature of sequencing projects to discern patterns of read-depth biases. XHMM is a statistical toolset that normalizes sequencing coverage in large-scale exome sequencing. It uses this information to discover Copy-Number Variants (CNVs) while providing quality metrics that indicate how strongly the data support a particular CNV.
A read count based tool that exploits all the reads produced by whole-exome sequencing (WES) experiments to detect copy Number Variants (CNVs) with a genome-wide resolution. EXCAVATOR2 enhances the identification of genomic CNVs (overlapping or non-overlapping exons) from WES data by integrating the analysis of In-targets and Off-targets reads. It extends the RC approach to the whole genome sequence and exploits the shifting level model (SLM) algorithm to segment the two combined profiles. Thereafter, the FastCall algorithm allows to classify each segmented region into five possible states (two-copy deletion, one-copy deletion, normal, one-copy duplication and multiple-copy amplification).
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A software tool for copy number detection that uses both the targeted reads and the nonspecifically captured off-target reads to infer copy number evenly across the genome. This combination achieves both exon-level resolution in targeted regions and sufficient resolution in the larger intronic and intergenic regions to identify copy number changes. In particular, we successfully inferred copy number at equivalent to 100-kilobase resolution genome-wide from a platform targeting as few as 293 genes. After normalizing read counts to a pooled reference, we evaluated and corrected for three sources of bias that explain most of the extraneous variability in the sequencing read depth: GC content, target footprint size and spacing, and repetitive sequences. We compared the performance of CNVkit to copy number changes identified by array comparative genomic hybridization. We packaged the components of CNVkit so that it is straightforward to use and provides visualizations, detailed reporting of significant features, and export options for integration into existing analysis pipelines.
CANOES / Copy number variants with an Arbitrary Number Of Exome Samples
An algorithm for the detection of rare copy number variants from exome sequencing data. CANOES uses the negative binomial distribution. The method was applied to a family-based exome sequencing dataset and show how it compares to XHMM using Copy number variants (CNVs) called by PennCNV from genotyping microarrays as the comparator. CANOES can be used in conjunction with XHMM to filter for high-quality CNV calls. CANOES makes significantly more deletion calls than XHMM and significantly fewer duplication calls.
DeAnnCNV / Detection and Annotation of Copy Number Variations from WES data
An online tool designed for precise detection and annotation of copy number variations (CNVs) from whole-exome sequencing (WES) data. Upon submitting the file generated from WES data by an in-house tool that can be downloaded from this server, DeAnnCNV can detect CNVs in each sample and extract the shared CNVs among multiple samples. DeAnnCNV also provides additional useful supporting information for the detected CNVs and associated genes to help users to find the potential candidates for further experimental study.
CLAMMS / Copy number estimation using Lattice-Aligned Mixture Models
An algorithm for calling copy number variants (CNVs) from exome sequencing read depths. CLAMMS is suitable for calling CNVs across the whole allele frequency spectrum, not just rare CNVs. Previous tools require that each sample be compared to a reference panel of samples that are assumed to be diploid in any given region. This assumption does not hold in copy number polymorphic regions (where non-diploid alleles are not rare), leading to improper genotypes. CLAMMS can scale to datasets of tens or hundreds of thousands of samples. Apart from one short processing step (which takes ~30 seconds for 30,000 samples), each sample can be processed in parallel. Unlike previous tools, which have RAM requirements that scale linearly in the number of samples, each CLAMMS process uses a constant amount of RAM regardless of the number of samples.
CKAT / CNV Kernel Association Test
A copy number variant (CNV) kernel association test. CKAT is a real dataset able to examine the association between CNV and disease as autism spectrum disorders (ASD) which demonstrates the potential usefulness of this method. This association is assessed by comparing the kernel-based similarity with the similarity in the trait using a score test for variance components in a random effect model. The kernel implemented in CKAT is elaborately designed so that it can capture special features of CNVs, such as multidimensionality (type and size) and heterogeneity effects.
A tool for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and simple to execute workflows that can scale to thousands of samples and can be easily incorporated into existing variant calling pipelines.
Anaconda / AN Automated pipeline for somatic COpy Number variation Detection and Annotation
Provides a pipeline which combines several copy number variation (CNV)-calling methods and annotates CNVs resulted from whole-exome sequencing (WES) data analysis. Anaconda performs its workflow in five steps: (i) configuring the running environment; (ii) detecting somatic CNVs by assigned tools; (ii) extracting the intersection of detected CNVs; (iv) retrieving and annotate genes located within called CNVs; (v) generating a HTML-based report including all the analyzed results.
An R package for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data. In terms of both accuracy and time, VEGAWES provided better results on the synthetic data and tumor samples demonstrating its potential in robust detection of aberrant regions in the genome.
A package for the detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. exomeCopy implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. Simulations show high sensitivity for detecting heterozygous and homozygous CNVs, outperforming normalization and state-of-the-art segmentation methods.
Allows users of copy number variation (CNV) detection tools to assess and tune performance. Ximmer is composed of three parts: (1) a simulation method, (2) an analysis pipeline, and (3) a graphical report. The software first simulates synthetic single copy deletions in existing whole exome sequencing (WES) data and then automates the detection of deletions with up to 5 CNV detection methods. It was tested using four datasets representing different Illumina sequencing platforms, exome captures, read configurations and sequencing depths.
iCNV / integrated Copy Number Variation caller
Allows copy number variation (CNV) detection. iCNV can be applied in whole exome sequencing (WES) only, whole genome sequencing (WGS) only, single-nucleotide polymorphism (SNP) array only, or any combination of SNP and sequencing data. It uses high throughput sequencing data, allowing for integration of SNP-array data. This tool utilizes B-allele frequency information from sequencing data, which is valuable for CNV detection and exact copy number inference.
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