Allele-specific copy number detection | Whole-genome sequencing data analysis
Estimation of allele-specific copy number (ASCN), which quantifies the number of copies of each allele at each variant loci rather than the total number of chromosome copies, is an important step in the characterization of tumor genomes and the inference of their clonal history. Sequencing produces reads containing both alleles at heterozygous variant loci, and thus, like genotyping arrays, allows the disambiguation of ASCNs. Compared to genotyping arrays, next-generation sequencing can provide finer resolution in estimating ASCNs because each person has his/her own unique heterozygous variant loci that are not included in regular genotyping arrays.
Serves for dissection of genome-wide allele-specific copy number in tumors. ASCAT infers ASCAT (accurate genome-wide allele-specific copy number) profiles from single nucleotide polymorphism (SNP) array data to evaluate and adjust both tumor cell aneuploidy and non-aberrant cell admixture. The ASCAT profiles generated can be useful for interpretation of cancer genome sequencing data and for identification of changes varying in size from point mutations to complex rearrangements.
A bioinformatic tool for analyzing and visualizing allele-specific copy numbers and loss-of-heterozygosity in cancer genomes. The data input is in the format of whole-genome sequencing data which enables characterization of genomic alterations ranging in size from point mutations to entire chromosomes. High quality results are obtained even if samples have low coverage, ~4x, low tumor cell content or are aneuploid.
A tool that calls the amplified alleles, and thus amplified haplotype, in copy number aberration regions in next-generation sequencing tumor data. The amplified haplotype may reveal gene variants. We assess the performance of HATS using simulated amplified regions generated from varying copy number and coverage levels, followed by amplicons in real data. We demonstrate that HATS infers the amplified alleles more accurately than does the naive approach, especially at low to intermediate coverage levels and in cases (including high coverage) possessing stromal contamination or allelic bias.
Analyzes allele-specific copy number in next generation sequencing (NGS) data. FACETS simplifies systematic identification of clonal and subclonal copy number events through a cellular fraction feature in the model. It can be used for GC-normalization, sequencing bias adjustment and/or segmentation analysis. This tool is useful to simplify large-scale application providing comprehensive output, and integrated visualization.
Normalizes allele-specific copy number estimates (ASCNs) from any technology and preprocessing method, without requiring matched normal. CalMaTe is a platform-independent multi-array method that controls single nucleotide polymorphism (SNP)-specific systematic variation by modeling the crosstalk between alleles in bi-allelic SNPs. The software was applied to the TCGA-ovarian cancer dataset. An add-on to the Aroma Project framework is also included in the package.
Enables copy number profiling and downstream analyses in disease genetic studies. MARATHON is a pipeline that gathers statistical software: CODEX and CODEX2 perform read depth normalization for total copy number profiling, iCNV receives read depth normalized by CODEX/CODEX2, FALCON and FALCON-X perform allele-specific copy number (ASCN) analysis and Canopy receives input from FALCON/FALCON-X to perform tumor phylogeny reconstruction. The pipeline adapts to different study designs and research goals.
Allows allele-specific somatic copy number alternation (SCNA) analysis accounting for the hypersegmentation. hsegHMM is a hidden Markov modeling (HMM) approach that simultaneously conducts the segmentation and genotype mixture modeling required to identify SCNAs across chromosomes. The software estimates genotype status as well as copy number at each locus, incorporating the complexities of tumor samples as well as hypersegmentation. It can improve the accuracy of detecting genotype status at each locus in next-generation sequencing (NGS)-based platforms.
Allows users to find somatic allele-specific copy number changes in whole exome sequencing. FALCON-X consists of a model to estimate the allele-specific copy number at these heterozygous positions. It uses a modified Bayes information criterion to determine the number of signals. Given the allele-specific coverage and site biases at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.
Leverages adjacent normal tissue from tumor biopsies. LumosVar has seven steps: (i) a set of unmatched control samples is analyzed for position quality scores, (ii) read counts and quality metrics are extracted from the tumor bams, (iii) quality scores are calculated for each candidate variant position, (iv) segmentation is performed, (v) allele specific copy number state for each segment is found, (vi) each candidate variant position is classified as somatic, germline heterozygous, or homozygous, and (vii) model parameters are optimized.
Allows users to detect copy number aberrations (CAN) on cancer whole genome sequencing (WGS) data. ACEseq provides an automated platform without prior information requirement. The software can perform a wide range of features such as quality check or structure variants (SV) breakpoint inclusion. It authorizes to improve segmentation and to obtain quantitative metrics.
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