• Corpus ID: 16875610

Screening and merging algorithm for the detection of copy-number alterations

  title={Screening and merging algorithm for the detection of copy-number alterations},
  author={Murilo S. Pinheiro and Benilton S. Carvalho and Alu'isio S. Pinheiro},
  journal={arXiv: Applications},
We call change-point problem (CPP) the identification of changes in the probabilistic behavior of a sequence of observations. Solving the CPP involves detecting the number and position of such changes. In genetics the study of how and what characteristics of a individual's genetic content might contribute to the occurrence and evolution of cancer has fundamental importance in the diagnosis and treatment of such diseases and can be formulated in the framework of chage-point analysis. In this… 


This study proposes the Screening and Ranking algorithm (SaRa) which can detect CNVs fast and accurately with complexity down to O(n), and characterize theoretical properties and present numerical analysis for the algorithm.
QuantiSNP: an Objective Bayes Hidden-Markov Model to detect and accurately map copy number variation using SNP genotyping data
QuantiSNP is a novel algorithm for high-resolution CNV/aneuploidy detection with application to clinical genetics, cancer and disease association studies and it is believed that the OB-HMM framework has widespread applicability in genomic research.
PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data.
PennCNV, a hidden Markov model (HMM) based approach, is presented for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data, demonstrating the feasibility of whole-genome fine-mapping ofCNVs via high- density SNP genotypesing.
OncoSNP-SEQ: a statistical approach for the identification of somatic copy number alterations from next-generation sequencing of cancer genomes
OncoSNP-SEQ is a statistical model-based approach for inferring copy number profiles directly from high-coverage whole genome sequencing data that is able to account for unknown tumour purity and ploidy.
Change-point Problem and Regression: An Annotated Bibliography
The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as “the change-point problem” or, in the Eastern literature, as
Optimal detection of changepoints with a linear computational cost
This work considers the problem of detecting multiple changepoints in large data sets and introduces a new method for finding the minimum of such cost functions and hence the optimal number and location of changepoints that has a computational cost which is linear in the number of observations.
Wild binary segmentation for multiple change-point detection
This work proposes a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data, and proposes two stopping criteria for WBS: one based on thresholding and the other based on what is termed the `strengthened Schwarz information criterion'.
Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer.
It is shown that the majority of monoallelic expression in the transcriptomes of triple-negative breast cancer can be explained by genomic regions of LOH and an upper bound for monoalle Alic expression is established that may be explaining by other tumor-specific modifications such as epigenetics or mutations.
Inferring Loss-of-Heterozygosity from Unpaired Tumors Using High-Density Oligonucleotide SNP Arrays
A hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome is described.