GEAR: genomic enrichment analysis of regional DNA copy number changes

We developed an algorithm named GEAR (genomic enrichment analysis of regional DNA copy number changes) for functional interpretation of genome-wide DNA copy number changes identified by array-based comparative genomic hybridization. GEAR selects two types of chromosomal alterations with potential biological relevance, i.e. recurrent and phenotype-specific alterations. Then it performs functional enrichment analysis using a priori selected functional gene sets to identify primary and clinical genomic signatures. The genomic signatures identified by GEAR represent functionally coordinated genomic changes, which can provide clues on the underlying molecular mechanisms related to the phenotypes of interest. GEAR can help the identification of key molecular functions that are activated or repressed in the tumor genomes leading to the improved understanding on the tumor biology. AVAILABILITY: GEAR software is available with online manual in the website, http://www.systemsbiology.co.kr/GEAR/.     

CODEX: a normalization and copy number variation detection method for whole exome sequencing

High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures.     

ITALICS: an algorithm for normalization and DNA copy number calling for Affymetrix SNP arrays

MOTIVATION: Affymetrix SNP arrays can be used to determine the DNA copy number measurement of 11 000-500 000 SNPs along the genome. Their high density facilitates the precise localization of genomic alterations and makes them a powerful tool for studies of cancers and copy number polymorphism. Like other microarray technologies it is influenced by non-relevant sources of variation, requiring correction. Moreover, the amplitude of variation induced by non-relevant effects is similar or greater than the biologically relevant effect (i.e. true copy number), making it difficult to estimate non-relevant effects accurately without including the biologically relevant effect. RESULTS: We addressed this problem by developing ITALICS, a normalization method that estimates both biological and non-relevant effects in an alternate, iterative manner, accurately eliminating irrelevant effects. We compared our normalization method with other existing and available methods, and found that ITALICS outperformed these methods for several in-house datasets and one public dataset. These results were validated biologically by quantitative PCR. AVAILABILITY: The R package ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) has been submitted to Bioconductor.     

Bayesian DNA copy number analysis

Background  

Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents.     

Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization

SUMMARY: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs. AVAILABILITY: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.     

Array-based comparative genomic hybridization and copy number variation in cancer research

Array-based comparative genomic hybridization (aCGH) is a molecular cytogenetic technique used in detecting and mapping DNA copy number alterations. aCGH is able to interrogate the entire genome at a previously unattainable, high resolution and has directly led to the recent appreciation of a novel class of genomic variation: copy number variation (CNV) in mammalian genomes. All forms of DNA variation/polymorphism are important for studying the basis of phenotypic diversity among individuals. CNV research is still at its infancy, requiring careful collation and annotation of accumulating CNV data that will undoubtedly be useful for accurate interpretation of genomic imbalances identified during cancer research.     

Distinct genomic copy number in mitochondria of different mammalian organs

This study shows that mitochondria in liver, kidney, heart, and brain of the mouse have a distinct mitochondrial density. It also demonstrates that the mtDNA copy number per mitochondrion is organ-specific. A reliable method of determining mitochondrial density per organ is by stereological analysis of tissue sections while mtDNA quantitation is by the use of radiolabelled mtDNA probe. This is the first study in which a comprehensive examination of mitochondrial density and quantitation of mitochondrial genomes in mouse organs have been done. In summary the variability is not only in mitochondrial density but also in genomic copy number in mitochondria of various tissues.     

Group normalization for genomic data

Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets.     

Identification of germline genomic copy number variation in familial pancreatic cancer

Adenocarcinoma of the pancreas is a significant cause of cancer mortality, and up to 10?% of cases appear to be familial. Heritable genomic copy number variants (CNVs) can modulate gene expression and predispose to disease. Here, we identify candidate predisposition genes for familial pancreatic cancer (FPC) by analyzing germline losses or gains present in one or more high-risk patients and absent in a large control group. A total of 120 FPC cases and 1,194 controls were genotyped on the Affymetrix 500K array, and 36 cases and 2,357 controls were genotyped on the Affymetrix 6.0 array. Detection of CNVs was performed by multiple computational algorithms and partially validated by quantitative PCR. We found no significant difference in the germline CNV profiles of cases and controls. A total of 93 non-redundant FPC-specific CNVs (53 losses and 40 gains) were identified in 50 cases, each CNV present in a single individual. FPC-specific CNVs overlapped the coding region of 88 RefSeq genes. Several of these genes have been reported to be differentially expressed and/or affected by copy number alterations in pancreatic adenocarcinoma. Further investigation in high-risk subjects may elucidate the role of one or more of these genes in genetic predisposition to pancreatic cancer.     

Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays

Genomic copy number variations (CNVs) are considered as a significant source of genetic diversity and widely involved in gene expression and regulatory mechanism, genetic disorders and disease risk, susceptibility to certain diseases and conditions, and resistance to medical drugs. Many studies have targeted the identification, profiling, analysis, and associations of genetic CNVs. We propose herein two new fuzzy methods, taht is, one based on the fuzzy inference from the pre-processed input, and another based on fuzzy C-means clustering. Our solutions present a higher true positive rate and a lower false negative with no false positive, efficient performance and consumption of least resources.     

Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer

A number of studies of copy number imbalances (CNIs) in breast tumors support associations between individual CNIs and patient outcomes. However, no pattern or signature of CNIs has emerged for clinical use. We determined copy number (CN) gains and losses using high-density molecular inversion probe (MIP) arrays for 971 stage I/II breast tumors and applied a boosting strategy to fit hazards models for CN and recurrence, treating chromosomal segments in a dose-specific fashion (-1 [loss], 0 [no change] and +1 [gain]). The concordance index (C-Index) was used to compare prognostic accuracy between a training (n = 728) and test (n = 243) set and across models. Twelve novel prognostic CNIs were identified: losses at 1p12, 12q13.13, 13q12.3, 22q11, and Xp21, and gains at 2p11.1, 3q13.12, 10p11.21, 10q23.1, 11p15, 14q13.2-q13.3, and 17q21.33. In addition, seven CNIs previously implicated as prognostic markers were selected: losses at 8p22 and 16p11.2 and gains at 10p13, 11q13.5, 12p13, 20q13, and Xq28. For all breast cancers combined, the final full model including 19 CNIs, clinical covariates, and tumor marker-approximated subtypes (estrogen receptor [ER], progesterone receptor, ERBB2 amplification, and Ki67) significantly outperformed a model containing only clinical covariates and tumor subtypes (C-Index _{full model}, train[test]  =  0.72[0.71] ± 0.02 vs. C-Index _{clinical + subtype model}, train[test]  =  0.62[0.62] ± 0.02; p<10⁻⁶). In addition, the full model containing 19 CNIs significantly improved prognostication separately for ER–, HER2+, luminal B, and triple negative tumors over clinical variables alone. In summary, we show that a set of 19 CNIs discriminates risk of recurrence among early-stage breast tumors, independent of ER status. Further, our data suggest the presence of specific CNIs that promote and, in some cases, limit tumor spread.     

Genome-wide copy number analysis of single cells

Copy number variation (CNV) is increasingly recognized as an important contributor to phenotypic variation in health and disease. Most methods for determining CNV rely on admixtures of cells in which information regarding genetic heterogeneity is lost. Here we present a protocol that allows for the genome-wide copy number analysis of single nuclei isolated from mixed populations of cells. Single-nucleus sequencing (SNS), combines flow sorting of single nuclei on the basis of DNA content and whole-genome amplification (WGA); this is followed by next-generation sequencing to quantize genomic intervals in a genome-wide manner. Multiplexing of single cells is discussed. In addition, we outline informatic approaches that correct for biases inherent in the WGA procedure and allow for accurate determination of copy number profiles. All together, the protocol takes ～3 d from flow cytometry to sequence-ready DNA libraries.     

Bayesian estimation of genomic copy number with single nucleotide polymorphism genotyping arrays

Background

The identification of copy number aberration in the human genome is an important area in cancer research. We develop a model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference.

Results

The performance of the algorithm is examined on both simulated and real cancer data, and it is compared with the popular CNAG algorithm for copy number detection.

Conclusions

We demonstrate that our Bayesian mixture model performs at least as well as the hidden Markov model based CNAG algorithm and in certain cases does better. One of the added advantages of our method is the flexibility of modeling normal cell contamination in tumor samples.     

Identification of genomic functional hotspots with copy number alteration in liver cancer

Copy number alterations (CNAs) can be observed in most of cancer patients. Several oncogenes and tumor suppressor genes with CNAs have been identified in different kinds of tumor. However, the systematic survey of CNA-affected functions is still lack. By employing systems biology approaches, instead of examining individual genes, we directly identified the functional hotspots on human genome. A total of 838 hotspots on human genome with 540 enriched Gene Ontology functions were identified. Seventy-six aCGH array data of hepatocellular carcinoma (HCC) tumors were employed in this study. A total of 150 regions which putatively affected by CNAs and the encoded functions were identified. Our results indicate that two immune related hotspots had copy number alterations in most of patients. In addition, our data implied that these immune-related regions might be involved in HCC oncogenesis. Also, we identified 39 hotspots of which copy number status were associated with patient survival. Our data implied that copy number alterations of the regions may contribute in the dysregulation of the encoded functions. These results further demonstrated that our method enables researchers to survey biological functions of CNAs and to construct regulation hypothesis at pathway and functional levels.     

Detecting copy number variation in the human genome using comparative genomic hybridization

Among human beings, it was once estimated that our genomes were 99.9% genetically identical. While this high level of genetic similarity helps to define us as a species, it is our genetic variation that contributes to our phenotypic diversity. As genomic technologies evolve to provide genome-wide analyses at higher resolution, we are beginning to appreciate that the human genome has a lot more variation than was once thought. Array-based comparative genomic hybridization (CGH) is one of these technologies that has recently revealed a newly appreciated type of genetic variation: copy number variation, in which thousands of regions of the human genome are now known to be variable in number between individuals. Some of these copy number variable regions have already been shown to predispose to certain common diseases, and others may ultimately have a significant impact on how each of us reacts to certain foods (e.g., allergic reactions), medications (e.g., pharmacogenomics), microscopic infections (i.e., immunity), and other aspects of our ever-changing environment.     

Probe-free allele-specific copy number detection and analysis of tumors

Cancer development and progression frequently involve nucleotide mutations as well as amplifications and deletions of genomic segments. Quantification of allele-specific copy number is an important step in characterizing tumor genomes for precision medicine. Despite advances in approaches to high-throughput genomic DNA analysis, inexpensive and simple methods for analyzing complex nucleotide and copy number variants are still needed. Real-time polymerase chain reaction (PCR) methods for discovering and genotyping single nucleotide polymorphisms are becoming increasingly important in genetic analysis. In this study, we describe a simple, single-tube, probe-free method that combines SYBR Green I-based quantitative real-time PCR and quantitative melting curve analysis both to detect specific nucleotide variants and to quantify allele-specific copy number variants of tumors. The approach is based on the quantification of the targets of interest and the relative abundance of two alleles in a single tube. The specificity, sensitivity, and utility of the assay were demonstrated in detecting allele-specific copy number changes critical for carcinogenesis and therapeutic intervention. Our approach would be useful for allele-specific copy number analysis or precise genotyping.     

aCGH local copy number aberrations associated with overall copy number genomic instability in colorectal cancer: coordinate involvement of the regions including BCR and ABL

In order to identify small regions of the genome whose specific copy number alteration is associated with high genomic instability in the form of overall genome-wide copy number aberrations, we have analyzed array-based comparative genomic hybridization (aCGH) data from 33 sporadic colorectal carcinomas. Copy number changes of a small number of specific regions were significantly correlated with elevated overall amplifications and deletions scattered throughout the entire genome. One significant region at 9q34 includes the c-ABL gene. Another region spanning 22q11-q13 includes the breakpoint cluster region (BCR) of the Philadelphia chromosome. Coordinate 22q11-q13 alterations were observed in 9 of 11 tumors with the 9q34 alteration. Additional regions on 1q and 14q were associated with overall genome-wide copy number changes, while copy number aberrations on chromosome 7p, 7q, and 13q21.1-q31.3 were found associated with this instability only in tumors from patients with a smoking history. Our analysis demonstrates there are a small number of regions of the genome where gain or loss is commonly associated with a tumor's overall level of copy number aberrations. Our finding BCR and ABL located within two of the instability-associated regions, and the involvement of these two regions occurring coordinately, suggests a system akin to the BCR-ABL translocation of CML may be involved in genomic instability in about one-third of human colorectal carcinomas.     

Detection of DNA copy number changes in human endometriosis by comparative genomic hybridization

Endometriosis is characterized by infertility and pelvic pain in 10-15% of women of reproductive age. The genetic events involved in endometriotic cell expansion remain in large part unknown. To identify genomic changes involved in development of this disease, we examined a panel of 18 selected endometriotic tissues by comparative genomic hybridization (CGH), a molecular cytogenetic method that allows screening of the entire genome for chromosomal gains and/or losses. The study was performed on native, nonamplified DNA extracted from manually dissected endometriotic lesions. Recurrent copy number losses on several chromosomes were detected in 15 of 18 cases. Loss of chromosome 1p and 22q were detected in 50% of the cases. Additional common losses occurred on chromosomes 5p (33%), 6q (27%), 7p(22%), 9q (22%), 16 (22%) as well as on 17q in one case. Gain of DNA sequences were seen at 6q, 7q and 17q in three cases. To validate the CGH data, selective dual-color FISH was performed using probes for the deleted regions on chromosomes 1, 7 and 22 in parallel with the corresponding centromeric probes. Cases showing deletion by CGH all had two signals at 1p36, 7p22.1 and 22q12 in less than 30% of the nuclei in comparison to the double centromeric labels found in more than 85% of the cells. These findings indicate that genes localized to previously undescribed chromosomal regions play a role in development and progression of endometriosis.     

Construction and use of spotted large-insert clone DNA microarrays for the detection of genomic copy number changes

Microarray-based comparative genomic hybridization has become a widespread method for the analysis of DNA copy number changes across the human genome. Initial methods for microarray construction using large-insert clones required the preparation of DNA from large-scale cultures. This rapidly became an expensive and time-consuming process when expanded to the number of clones needed for higher resolution arrays. To overcome this problem, several PCR-based strategies have been developed to enable array construction from small amounts of cloned DNA. Here, we describe the construction of microarrays composed of human-specific large-insert clones (40-200 kb) using a specific degenerate oligonucleotide PCR strategy. In addition, we also describe array hybridization using manual and automated procedures and methods for array analysis. The technology and protocols described in this article can easily be adapted for other species dependent on the availability of clone libraries. According to our protocols, the procedure will take approximately 3 days from labeling the DNA to scanning the hybridized slides.