Assembly of microarrays for genome-wide measurement of DNA copy number

Extensive sequence duplication in Arabidopsis revealed by pseudo-heterozygosity

BACKGROUND

It is apparent that genomes harbor much structural variation that is largely undetected for technical reasons. Such variation can cause artifacts when short-read sequencing data are mapped to a reference genome. Spurious SNPs may result from mapping of reads to unrecognized duplicated regions. Calling SNP using the raw reads of the 1001 Arabidopsis Genomes Project we identified 3.3 million (44%) heterozygous SNPs. Given that Arabidopsis thaliana (A. thaliana) is highly selfing, and that extensively heterozygous individuals have been removed, we hypothesize that these SNPs reflected cryptic copy number variation.

RESULTS

The heterozygosity we observe consists of particular SNPs being heterozygous across individuals in a manner that strongly suggests it reflects shared segregating duplications rather than random tracts of residual heterozygosity due to occasional outcrossing. Focusing on such pseudo-heterozygosity in annotated genes, we use genome-wide association to map the position of the duplicates. We identify 2500 putatively duplicated genes and validate them using de novo genome assemblies from six lines. Specific examples included an annotated gene and nearby transposon that transpose together. We also demonstrate that cryptic structural variation produces highly inaccurate estimates of DNA methylation polymorphism.

CONCLUSIONS

Our study confirms that most heterozygous SNP calls in A. thaliana are artifacts and suggest that great caution is needed when analyzing SNP data from short-read sequencing. The finding that 10% of annotated genes exhibit copy-number variation, and the realization that neither gene- nor transposon-annotation necessarily tells us what is actually mobile in the genome suggests that future analyses based on independently assembled genomes will be very informative.

Perspectives on the Application of Cytogenomic Approaches in Chronic Lymphocytic Leukaemia

Chronic lymphocytic leukaemia (CLL) is a haematological malignancy characterised by the accumulation of monoclonal mature B lymphocytes (positive for CD5+ and CD23+) in peripheral blood, bone marrow, and lymph nodes. Although CLL is reported to be rare in Asian countries compared to Western countries, the disease course is more aggressive in Asian countries than in their Western counterparts. It has been postulated that this is due to genetic variants between populations. Various cytogenomic methods, either of the traditional type (conventional cytogenetics or fluorescence in situ hybridisation (FISH)) or using more advanced technology such as DNA microarrays, next generation sequencing (NGS), or genome wide association studies (GWAS), were used to detect chromosomal aberrations in CLL. Up until now, conventional cytogenetic analysis remained the gold standard in diagnosing chromosomal abnormality in haematological malignancy including CLL, even though it is tedious and time-consuming. In concordance with technological advancement, DNA microarrays are gaining popularity among clinicians as they are faster and better able to accurately diagnose the presence of chromosomal abnormalities. However, every technology has challenges to overcome. In this review, CLL and its genetic abnormalities will be discussed, as well as the application of microarray technology as a diagnostic platform.

Efficient change-points detection for genomic sequences via cumulative segmented regression

MOTIVATION

Knowing the number and the exact locations of multiple change points in genomic sequences serves several biological needs. The cumulative-segmented algorithm (cumSeg) has been recently proposed as a computationally efficient approach for multiple change-points detection, which is based on a simple transformation of data and provides results quite robust to model mis-specifications. However, the errors are also accumulated in the transformed model so that heteroscedasticity and serial correlation will show up, and thus the variations of the estimated change points will be quite different, while the locations of the change points should be of the same importance in the original genomic sequences.

RESULTS

In this study, we develop two new change-points detection procedures in the framework of cumulative segmented regression. Simulations reveal that the proposed methods not only improve the efficiency of each change point estimator substantially but also provide the estimators with similar variations for all the change points. By applying these proposed algorithms to Coriel and SNP genotyping data, we illustrate their performance on detecting copy number variations.

AVAILABILITY AND IMPLEMENTATION

The proposed algorithms are implemented in R program and the codes are provided in the online supplementary material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detection of copy number variants in African goats using whole genome sequence data

Background

Copy number variations (CNV) are a significant source of variation in the genome and are therefore essential to the understanding of genetic characterization. The aim of this study was to develop a fine-scaled copy number variation map for African goats. We used sequence data from multiple breeds and from multiple African countries.

Results

A total of 253,553 CNV (244,876 deletions and 8677 duplications) were identified, corresponding to an overall average of 1393 CNV per animal. The mean CNV length was 3.3 kb, with a median of 1.3 kb. There was substantial differentiation between the populations for some CNV, suggestive of the effect of population-specific selective pressures. A total of 6231 global CNV regions (CNVR) were found across all animals, representing 59.2 Mb (2.4%) of the goat genome. About 1.6% of the CNVR were present in all 34 breeds and 28.7% were present in all 5 geographical areas across Africa, where animals had been sampled. The CNVR had genes that were highly enriched in important biological functions, molecular functions, and cellular components including retrograde endocannabinoid signaling, glutamatergic synapse and circadian entrainment.

Conclusions

This study presents the first fine CNV map of African goat based on WGS data and adds to the growing body of knowledge on the genetic characterization of goats.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07703-1.

Post-selection inference for changepoint detection algorithms with application to copy number variation data

Changepoint detection methods are used in many areas of science and engineering, for example, in the analysis of copy number variation data to detect abnormalities in copy numbers along the genome. Despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or the presence) of given changepoints post-selection are lacking. Post-selection inference offers a framework to fill this gap, but the most straightforward application of these methods results in low-powered hypothesis tests and leaves open several important questions about practical usability. In this work, we carefully tailor post-selection inference methods toward changepoint detection, focusing on copy number variation data. To accomplish this, we study commonly used changepoint algorithms: binary segmentation, as well as two of its most popular variants, wild and circular, and the fused lasso. We implement some of the latest developments in post-selection inference theory, mainly auxiliary randomization. This improves the power, which requires implementations of Markov chain Monte Carlo algorithms (importance sampling and hit-and-run sampling) to carry out our tests. We also provide recommendations for improving practical useability, detailed simulations, and example analyses on array comparative genomic hybridization as well as sequencing data.

RIGD: A Database for Intronless Genes in the Rosaceae

Most eukaryotic genes are interrupted by one or more introns, and only prokaryotic genomes are composed of mainly single-exon genes without introns. Due to the absence of introns, intronless genes in eukaryotes have become important materials for comparative genomics and evolutionary biology. There is currently no cohesive database that collects intronless genes in plants into a single database, although many databases on exons and introns exist. In this study, we constructed the Rosaceae Intronless Genes Database (RIGD), a user-friendly web interface to explore and collect information on intronless genes from different plants. Six Rosaceae species, Pyrus bretschneideri, Pyrus communis, Malus domestica, Prunus persica, Prunus mume, and Fragaria vesca, are included in the current release of the RIGD. Sequence data and gene annotation were collected from different databases and integrated. The main purpose of this study is to provide gene sequence data. In addition, attribute analysis, functional annotations, subcellular localization prediction, and GO analysis are reported. The RIGD allows users to browse, search, and download data with ease. Blast and comparative analyses are also provided through this online database, which is available at http://www.rigdb.cn/.

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.

Copy number variations and constitutional chromothripsis (Review)

Both copy number variations (CNVs) and chromothripsis are phenomena that involve complex genomic rearrangements. Chromothripsis results in CNVs and other structural changes. CNVs are frequently observed in the human genome. Studies on CNVs have been increasing exponentially; the Database of Genomic Variants shows an increase in the number of data published on structural variations added to the database in the last 15 years. CNVs may be a result of replicative and non-replicative mechanisms, and are hypothesized to serve important roles in human health and disease. Chromothripsis is a phenomena of chromosomal rearrangement following chromosomal breaks at multiple locations and involves impaired DNA repair. In 2011, Stephens et al coined the term chromothripsis for this type of fragmenting event. Several proposed mechanisms have been suggested to underlie chromothripsis, such as p53 inactivation, micronuclei formation, abortive apoptosis and telomere fusions in telomere crisis. Chromothripsis gives rise to normal or abnormal phenotypes. In this review, constitutional chromothripsis, which may coexist with multiple de novo CNVs are described and discussed. This reviews aims to summarize recent advances in our understanding of CNVs and chromothripsis, and describe the effects of these phenomena on human health and birth defects.

Tumor Interferon Signaling Is Regulated by a lncRNA INCR1 Transcribed from the PD-L1 Locus

Tumor interferon (IFN) signaling promotes PD-L1 expression to suppress T cell-mediated immunosurveillance. We identify the IFN-stimulated non-coding RNA 1 (INCR1) as a long noncoding RNA (lncRNA) transcribed from the PD-L1 locus and show that INCR1 controls IFNγ signaling in multiple tumor types. Silencing INCR1 decreases the expression of PD-L1, JAK2, and several other IFNγ-stimulated genes. INCR1 knockdown sensitizes tumor cells to cytotoxic T cell-mediated killing, improving CAR T cell therapy. We discover that PD-L1 and JAK2 transcripts are negatively regulated by binding to HNRNPH1, a nuclear ribonucleoprotein. The primary transcript of INCR1 binds HNRNPH1 to block its inhibitory effects on the neighboring genes PD-L1 and JAK2, enabling their expression. These findings introduce a mechanism of tumor IFNγ signaling regulation mediated by the lncRNA INCR1 and suggest a therapeutic target for cancer immunotherapy.

Dual-Enhanced Raman Scattering-Based Characterization of Stem Cell Differentiation Using Graphene-Plasmonic Hybrid Nanoarray

Surface-enhanced Raman scattering (SERS) has demonstrated great potential to analyze a variety of bio/chemical molecular interactions within cells in a highly sensitive and selective manner. Despite significant advancements, it remains a critical challenge to ensure high sensitivity and selectivity, while achieving uniform signal enhancement and high reproducibility for quantitative detection of targeted biomarkers within a complex stem cell microenvironment. Herein, we demonstrate an innovative sensing platform, using graphene-coated homogeneous plasmonic metal (Au) nanoarrays, which synergize both electromagnetic mechanism (EM)- and chemical mechanism (CM)-based enhancement. Through the homogeneous plasmonic nanostructures, generated by laser interference lithography (LIL), highly reproducible enhancement of Raman signals could be obtained via a strong and uniform EM. Additionally, the graphene-functionalized surface simultaneously amplifies the Raman signals by an optimized CM, which aligns the energy level of the graphene oxide with the target molecule by tuning its oxidation levels, consequently increasing the sensitivity and accuracy of our sensing system. Using the dual-enhanced Raman scattering from both EM from the homogeneous plasmonic Au nanoarray and CM from the graphene surface, our graphene-Au hybrid nanoarray was successfully utilized to detect as well as quantify a specific biomarker (TuJ1) gene expression levels to characterize neuronal differentiation of human neural stem cells (hNSCs). Collectively, we believe our unique graphene-plasmonic hybrid nanoarray can be extended to a wide range of applications in the development of simple, rapid, and accurate sensing platforms for screening various bio/chemical molecules.

Chromosomal Microarray Analysis Using Array Comparative Genomic Hybridization on DNA from Amniotic Fluid and Chorionic Villus Sampling

Chromosomal Microarray analysis offers an objective high resolution view of copy number changes in the genome that contribute to genomic disorders in various clinical setting such as postnatal, prenatal, and oncology. Here, we describe a fast and reliable method of using chromosomal microarray analysis in detection of genomic imbalances that may be associated with congenital malformations in a prenatal setting. Results can be obtained in 4-5 days using direct amniotic fluid (AF) or chorionic villus samples (CVS).

Overexpression of Claspin and Timeless protects cancer cells from replication stress in a checkpoint-independent manner

Oncogene-induced replication stress (RS) promotes cancer development but also impedes tumor growth by activating anti-cancer barriers. To determine how cancer cells adapt to RS, we have monitored the expression of different components of the ATR-CHK1 pathway in primary tumor samples. We show that unlike upstream components of the pathway, the checkpoint mediators Claspin and Timeless are overexpressed in a coordinated manner. Remarkably, reducing the levels of Claspin and Timeless in HCT116 cells to pretumoral levels impeded fork progression without affecting checkpoint signaling. These data indicate that high level of Claspin and Timeless increase RS tolerance by protecting replication forks in cancer cells. Moreover, we report that primary fibroblasts adapt to oncogene-induced RS by spontaneously overexpressing Claspin and Timeless, independently of ATR signaling. Altogether, these data indicate that enhanced levels of Claspin and Timeless represent a gain of function that protects cancer cells from of oncogene-induced RS in a checkpoint-independent manner.

Computational detection and experimental validation of segmental duplications and associated copy number variations in water buffalo ( Bubalus bubalis )

Duplicated sequences are an important source of gene evolution and structural variation within mammalian genomes. Using a read depth approach based on next-generation sequencing, we performed a genome-wide analysis of segmental duplications (SDs) and associated copy number variations (CNVs) in the water buffalo (Bubalus bubalis). By aligning short reads of Olimpia (the reference water buffalo) to the UMD3.1 cattle genome, we identified 1,038 segmental duplications comprising 44.6 Mb (equivalent to ~1.73% of the cattle genome) of the autosomal and X chromosomal sequence in the buffalo genome. We experimentally validated 70.3% (71/101) of these duplications using fluorescent in situ hybridization. We also detected a total of 1,344 CNV regions across 14 additional water buffaloes, amounting to 59.8 Mb of variable sequence or the equivalent of 2.2% of the cattle genome. The CNV regions overlap 1,245 genes that are significantly enriched for specific biological functions including immune response, oxygen transport, sensory system and signal transduction. Additionally, we performed array Comparative Genomic Hybridization (aCGH) experiments using the 14 water buffaloes as test samples and Olimpia as the reference. Using a linear regression model, a high Pearson correlation (r = 0.781) was observed between the log₂ ratios between copy number estimates and the log₂ ratios of aCGH probes. We further designed Quantitative PCR assays to confirm CNV regions within or near annotated genes and found 74.2% agreement with our CNV predictions. These results confirm sub-chromosome-scale structural rearrangements present in the cattle and water buffalo. The information on genome variation that will be of value for evolutionary and phenotypic studies, and may be useful for selective breeding of both species.

Prenatal diagnosis by chromosomal microarray analysis

Chromosomal microarray analysis (CMA) is performed either by array comparative genomic hybridization or by using a single nucleotide polymorphism array. In the prenatal setting, CMA is on par with traditional karyotyping for detection of major chromosomal imbalances such as aneuploidy and unbalanced rearrangements. CMA offers additional diagnostic benefits by revealing sub-microscopic imbalances or copy number variations that are too small to be seen on a standard G-banded chromosome preparation. These submicroscopic imbalances are also referred to as microdeletions and microduplications, particularly when they include specific genomic regions that are associated with clinical sequelae. Not all microdeletions/duplications are associated with adverse clinical phenotypes and in many cases, their presence is benign. In other cases, they are associated with a spectrum of clinical phenotypes that may range from benign to severe, while in some situations, the clinical significance may simply be unknown. These scenarios present a challenge for prenatal diagnosis, and genetic counseling prior to prenatal CMA greatly facilitates delivery of complex results. In prenatal diagnostic samples with a normal karyotype, chromosomal microarray will diagnose a clinically significant subchromosomal deletion or duplication in approximately 1% of structurally normal pregnancies and 6% with a structural anomaly. Pre-test counseling is also necessary to distinguish the primary differences between the benefits, limitations and diagnostic scope of CMA versus the powerful but limited screening nature of non-invasive prenatal diagnosis using cell-free fetal DNA.

Establishment of a novel patient-derived Ewing's sarcoma cell line, NCC-ES1-C1

Ewing's sarcoma is an aggressive mesenchymal tumor characterized by the presence of a unique EWSR1-FLI1 translocation. Ewing's sarcoma primarily occurs in the bone and soft tissues. Cell lines enable researchers to investigate the molecular backgrounds of disease and the significance of genetic alterations in relevant cellular contexts. Here, we report the establishment and characterization of a novel Ewing's sarcoma cell line following primary Ewing's sarcoma tumor tissue culture. The established cell line was authenticated by DNA microsatellite short tandem repeat analysis, characterized by in vitro assays, and named NCC-ES1-C1. The NCC-ES1-C1 cell line grew well for 15 mo and was subcultured more than 50 times during this period. Characterization of the cells revealed that they were not adherent and showed floating features. In conclusion, we successfully established a novel Ewing's sarcoma cell line, NCC-ES1-C1, from primary tumor tissue. The cell line has the characteristic EWSR1-FLI1 gene fusion and exhibits aggressive growth in vitro. Thus, the NCC-ES1-C1 cell line will be a useful tool for investigating the mechanisms of disease and the biological role of the EWSR1-FLI1 fusion gene.

Heavy-Tailed Noise Suppression and Derivative Wavelet Scalogram for Detecting DNA Copy Number Aberrations

Most existing array comparative genomic hybridization (array CGH) data processing methods and evaluation models assumed that the probability density function (pdf) of noise in array CGH data is a Gaussian distribution. However, in practice, such noise distribution is peaky and heavy-tailed. Therefore, a Gaussian pdf is not adequate to approximate the noise in array CGH data and hence introduces wrong detections of chromosomal aberrations and leads misunderstanding on disease pathogenesis. A more accurate and sufficient model of noise in array CGH data is necessary and beneficial to the detection of DNA copy number variations. We analyze the real array CGH data from different platforms and show that the distribution of noise in array CGH data is fitted very well by generalized Gaussian distribution (GGD). Based on our new noise model, we propose a novel array CGH processing method combining the advantages of both the smoothing and segmentation approaches. The new method uses generalized Gaussian bivariate shrinkage function and one-directional derivative wavelet scalogram in generalized Gaussian noise. In the smoothing step, with the new generalized Gaussian noise model, we derive the heavy-tailed noise suppression algorithm in stationary wavelet domain. In the segmentation step, the 1D Gaussian derivative wavelet scalogram is employed to detect break points. Both real and simulated array CGH data with different noises (such as Gaussian noise, GGD noise, and real noise) are used in our experiments. We demonstrate that our new method outperforms other state-of-the-art methods, in terms of both root mean squared errors and receiver operating characteristic curves.

Copy number variant analysis using genome-wide mate-pair sequencing

Copy number variation (CNV) is a common form of structural variation detected in human genomes, occurring as both constitutional and somatic events. Cytogenetic techniques like chromosomal microarray (CMA) are widely used in analyzing CNVs. However, CMA techniques cannot resolve the full nature of these structural variations (i.e. the orientation and location of associated breakpoint junctions) and must be combined with other cytogenetic techniques, such as karyotyping or FISH, to do so. This makes the development of a next-generation sequencing (NGS) approach capable of resolving both CNVs and breakpoint junctions desirable. Mate-pair sequencing (MPseq) is a NGS technology designed to find large structural rearrangements across the entire genome. Here we present an algorithm capable of performing copy number analysis from mate-pair sequencing data. The algorithm uses a step-wise procedure involving normalization, segmentation, and classification of the sequencing data. The segmentation technique combines both read depth and discordant mate-pair reads to increase the sensitivity and resolution of CNV calls. The method is particularly suited to MPseq, which is designed to detect breakpoint junctions at high resolution. This allows for the classification step to accurately calculate copy number levels at the relatively low read depth of MPseq. Here we compare results for a series of hematological cancer samples that were tested with CMA and MPseq. We demonstrate comparable sensitivity to the state-of-the-art CMA technology, with the benefit of improved breakpoint resolution. The algorithm provides a powerful analytical tool for the analysis of MPseq results in cancer.

Establishment and characterization of novel patient-derived osteosarcoma xenograft and cell line

Osteosarcoma is an aggressive mesenchymal malignancy of the bone. Patient-derived models are essential tools for elucidating the molecular mechanisms associated with poor prognosis and the development of novel anticancer drugs. This study described the establishment of a patient-derived cancer model of osteosarcoma. Primary osteosarcoma tumor tissues were obtained from an osteosarcoma patient and inoculated in the skin of immunodeficient mice, followed by transplantation to other mice upon growth. Cells were maintained in monolayer cultures, and the capability of spheroid formation was assessed by seeding the cells on culture dishes. The invasion ability of cells was monitored by Matrigel assay, and genomic and proteomic backgrounds were examined by mass spectrometry. A cell line was established from patient-derived tumors and showed similar histology to that of the primary tumor tissue. Additionally, these cells formed spheroids on low-attachment tissue-culture dishes and exhibited invasive capabilities, and we confirmed that the genomic backgrounds were similar between patient-derived xenograft tumors and the cell line. Furthermore, the proteome of the patient-derived tumors and the cells exhibited similar, but not identical, patterns to that of the original tumor tissue. Our results indicated that this patient-derived xenograft model and cell line would be useful resources for osteosarcoma research.

Novel applications of array comparative genomic hybridization in molecular diagnostics

INTRODUCTION

In 2004, the implementation of array comparative genomic hybridization (array comparative genome hybridization [CGH]) into clinical practice marked a new milestone for genetic diagnosis. Array CGH and single-nucleotide polymorphism (SNP) arrays enable genome-wide detection of copy number changes in a high resolution, and therefore microarray has been recognized as the first-tier test for patients with intellectual disability or multiple congenital anomalies, and has also been applied prenatally for detection of clinically relevant copy number variations in the fetus. Area covered: In this review, the authors summarize the evolution of array CGH technology from their diagnostic laboratory, highlighting exonic SNP arrays developed in the past decade which detect small intragenic copy number changes as well as large DNA segments for the region of heterozygosity. The applications of array CGH to human diseases with different modes of inheritance with the emphasis on autosomal recessive disorders are discussed. Expert commentary: An exonic array is a powerful and most efficient clinical tool in detecting genome wide small copy number variants in both dominant and recessive disorders. However, whole-genome sequencing may become the single integrated platform for detection of copy number changes, single-nucleotide changes as well as balanced chromosomal rearrangements in the near future.

A nonparametric Bayesian basket trial design

Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer. Therefore, finding the subpopulation of patients who can most benefit from an aberration-specific targeted therapy across multiple cancer types is important. We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are the use of a flexible nonparametric Bayesian survival regression based on a random covariate-dependent partition of patients, and decisions based on a flexible utility function that reflects the requirement of the clinicians appropriately and realistically, and the adaptive allocation of patients to their superior treatments. Through extensive simulation studies, the new method is demonstrated to achieve desirable operating characteristics and compares favorably against the alternatives.

iSeg: an efficient algorithm for segmentation of genomic and epigenomic data

Background

Identification of functional elements of a genome often requires dividing a sequence of measurements along a genome into segments where adjacent segments have different properties, such as different mean values. Despite dozens of algorithms developed to address this problem in genomics research, methods with improved accuracy and speed are still needed to effectively tackle both existing and emerging genomic and epigenomic segmentation problems.

Results

We designed an efficient algorithm, called iSeg, for segmentation of genomic and epigenomic profiles. iSeg first utilizes dynamic programming to identify candidate segments and test for significance. It then uses a novel data structure based on two coupled balanced binary trees to detect overlapping significant segments and update them simultaneously during searching and refinement stages. Refinement and merging of significant segments are performed at the end to generate the final set of segments. By using an objective function based on the p-values of the segments, the algorithm can serve as a general computational framework to be combined with different assumptions on the distributions of the data. As a general segmentation method, it can segment different types of genomic and epigenomic data, such as DNA copy number variation, nucleosome occupancy, nuclease sensitivity, and differential nuclease sensitivity data. Using simple t-tests to compute p-values across multiple datasets of different types, we evaluate iSeg using both simulated and experimental datasets and show that it performs satisfactorily when compared with some other popular methods, which often employ more sophisticated statistical models. Implemented in C++, iSeg is also very computationally efficient, well suited for large numbers of input profiles and data with very long sequences.

Conclusions

We have developed an efficient general-purpose segmentation tool and showed that it had comparable or more accurate results than many of the most popular segment-calling algorithms used in contemporary genomic data analysis. iSeg is capable of analyzing datasets that have both positive and negative values. Tunable parameters allow users to readily adjust the statistical stringency to best match the biological nature of individual datasets, including widely or sparsely mapped genomic datasets or those with non-normal distributions.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2140-3) contains supplementary material, which is available to authorized users.

High resolution global chromosomal aberrations from spontaneous miscarriages revealed by low coverage whole genome sequencing

OBJECTIVE

Chromosome aberrations are generally considered as one of the most substantial causative factors contributing to spontaneous miscarriages. Cytogenetic analyses like G-banded karyotype and chromosomal microarray analyses are often performed to further investigate the chromosome status of a miscarried fetus.

STUDY DESIGN

Here, we describe a novel method, AnnoCNV, to detect DNA copy number variations (CNVs) using low coverage whole genome sequencing (WGS). We investigated the overall frequency of chromosomal abnormalities in 149 miscarriage specimens using AnnoCNV.

RESULTS

Among 149 fetal miscarriage samples, more than two fifths of them (42.95%, 64) carried at least one chromosomal abnormality, and a subset (40) was identified as autosomal trisomy which account for 26.84% of all samples. We have also developed a robust algorithm in AnnoCNV, which is able to differentiate specifically karyotype 69,XXY from sex chromosomal aneuploidy 45,X, and to identify 45,X/46,XX mosaicism. Lastly, across the whole genome AnnoCNV identifies CNVs, which are associated with both reported symptoms and unknown clinical conditions.

CONCLUSION

This cost-effective strategy reveals genome wide discovery of chromosome aberrations at higher resolution, which are consistent with parallel investigation conducted by SNP based assay.

Quantitative, in situ analysis of mRNAs and proteins with subcellular resolution

We describe here a method, termed immunoFISH, for simultaneous in situ analysis of the composition and distribution of proteins and individual RNA transcripts in single cells. Individual RNA molecules are labeled by hybridization and target proteins are concurrently stained using immunofluorescence. Multicolor fluorescence images are acquired and analyzed to determine the abundance, composition, and distribution of hybridized probes and immunofluorescence. We assessed the ability of immunoFISH to simultaneous quantify protein and transcript levels and distribution in cultured HER2 positive breast cancer cells and human breast tumor samples. We demonstrated the utility of this assay in several applications including demonstration of the existence of a layer of normal myoepithelial KRT14 expressing cells that separate HER2+ cancer cells from the stromal and immune microenvironment in HER2+ invasive breast cancer. Our studies show that immunoFISH provides quantitative information about the spatial heterogeneity in transcriptional and proteomic features that exist between and within cells.

aCGHViewer: A Generic Visualization Tool for aCGH Data

Array-Comparative Genomic Hybridization (aCGH) is a powerful high throughput technology for detecting chromosomal copy number aberrations (CNAs) in cancer, aiming at identifying related critical genes from the affected genomic regions. However, advancing from a dataset with thousands of tabular lines to a few candidate genes can be an onerous and time-consuming process. To expedite the aCGH data analysis process, we have developed a user-friendly aCGH data viewer (aCGHViewer) as a conduit between the aCGH data tables and a genome browser. The data from a given aCGH analysis are displayed in a genomic view comprised of individual chromosome panels which can be rapidly scanned for interesting features. A chromosome panel containing a feature of interest can be selected to launch a detail window for that single chromosome. Selecting a data point of interest in the detail window launches a query to the UCSC or NCBI genome browser to allow the user to explore the gene content in the chromosomal region. Additionally, aCGHViewer can display aCGH and expression array data concurrently to visually correlate the two. aCGHViewer is a stand alone Java visualization application that should be used in conjunction with separate statistical programs. It operates on all major computer platforms and is freely available at http://falcon.roswellpark.org/aCGHview/ .

Computational Methods for the Analysis of Array Comparative Genomic Hybridization

Array comparative genomic hybridization (array CGH) is a technique for assaying the copy number status of cancer genomes. The widespread use of this technology has lead to a rapid accumulation of high throughput data, which in turn has prompted the development of computational strategies for the analysis of array CGH data. Here we explain the principles behind array image processing, data visualization and genomic profile analysis, review currently available software packages, and raise considerations for future software development.

Tumor Genome Wide DNA Alterations Assessed by Array CGH in Patients with Poor and Excellent Survival following Operation for Colorectal Cancer

Genome wide DNA alterations were evaluated by array CGH in addition to RNA expression profiling in colorectal cancer from patients with excellent and poor survival following primary operations.

DNA was used for CGH in BAC and cDNA arrays. Global RNA expression was determined by 44K arrays. DNA and RNA from tumor and normal colon were used from cancer patients grouped according to death, survival or Dukes A, B, C and D tumor stage. Confirmed DNA alterations in all Dukes A – D were judged relevant for carcinogenesis, while changes in Dukes C and D only were regarded relevant for tumor progression.

Copy number gain was more common than loss in tumor tissue (p < 0.01). Major tumor DNA alterations occurred in chromosome 8, 13, 18 and 20, where short survival included gain in 8q and loss in 8p. Copy number gains related to tumor progression were most common on chromosome 7, 8, 19, 20, while corresponding major losses appeared in chromosome 8. Losses at chromosome 18 occurred in all Dukes stages. Normal colon tissue from cancer patients displayed gains in chromosome 19 and 20. Mathematical Vector analysis implied a number of BAC-clones in tumor DNA with genes of potential importance for death or survival.

The genomic variation in colorectal cancer cells is tremendous and emphasizes that BAC array CGH is presently more powerful than available statistical models to discriminate DNA sequence information related to outcome. Present results suggest that a majority of DNA alterations observed in colorectal cancer are secondary to tumor progression. Therefore, it would require an immense work to distinguish primary from secondary DNA alterations behind colorectal cancer.

Magellan: A Web Based System for the Integrated Analysis of Heterogeneous Biological Data and Annotations; Application to DNA Copy Number and Expression Data in Ovarian Cancer

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.

Normalized, Segmented or Called aCGH Data?

Array comparative genomic hybridization (aCGH) is a high-throughput lab technique to measure genome-wide chromosomal copy numbers. Data from aCGH experiments require extensive pre-processing, which consists of three steps: normalization, segmentation and calling. Each of these pre-processing steps yields a different data set: normalized data, segmented data, and called data. Publications using aCGH base their findings on data from all stages of the pre-processing. Hence, there is no consensus on which should be used for further down-stream analysis. This consensus is however important for correct reporting of findings, and comparison of results from different studies. We discuss several issues that should be taken into account when deciding on which data are to be used. We express the believe that called data are best used, but would welcome opposing views.

Detection of clinically relevant copy number alterations in oral cancer progression using multiplexed droplet digital PCR

Copy number alterations (CNAs), a common genomic event during carcinogenesis, are known to affect a large fraction of the genome. Common recurrent gains or losses of specific chromosomal regions occur at frequencies that they may be considered distinctive features of tumoral cells. Here we introduce a novel multiplexed droplet digital PCR (ddPCR) assay capable of detecting recurrent CNAs that drive tumorigenesis of oral squamous cell carcinoma. Applied to DNA extracted from oral cell lines and clinical samples of various disease stages, we found good agreement between CNAs detected by our ddPCR assay with those previously reported using comparative genomic hybridization or single nucleotide polymorphism arrays. Furthermore, we demonstrate that the ability to target specific locations of the genome permits detection of clinically relevant oncogenic events such as small, submicroscopic homozygous deletions. Additional capabilities of the multiplexed ddPCR assay include the ability to infer ploidy level, quantify the change in copy number of target loci with high-level gains, and simultaneously assess the status and viral load for high-risk human papillomavirus types 16 and 18. This novel multiplexed ddPCR assay therefore may have clinical value in differentiating between benign oral lesions from those that are at risk of progressing to oral cancer.

A nCounter CNV Assay to Detect HER2 Amplification: A Correlation Study with Immunohistochemistry and In Situ Hybridization in Advanced Gastric Cancer

AIM

Screening amplified genes for targeted therapy with high-throughput technology is very important. The NanoString nCounter system allows multiplexed digital quantification of target molecules through the use of color-coded barcodes with the great advantage that formalin-fixed, paraffin-embedded (FFPE) tissue can be utilized.

METHODS

We tested nCounter custom copy number variation (CNV) panels in 220 gastric cancer samples and evaluated the utility of this method as a screening tool for the detection of CNV using HER2. For the validation of results, we compared the nCounter results with immunohistochemistry (IHC), and we further performed in situ hybridization (ISH) in discrepant cases.

RESULTS

The average HER2 gene copy numbers (CNs) by nCounter were 17.25, 2.0 and 2.61 for the HER2 IHC positive (3+), equivocal (2+), and negative cases, respectively. Out of the 16 IHC 3+ cases, 13 (81.3 %) were reported as HER2 CN gain (≥4). Gastric cancers with homogeneous HER2 overexpression or high tumor purity showed HER2 CN ≥10. Among the 192 cases with HER2 IHC negative and without HER2 gene amplification, 29 showed a HER2 CN ≥4 with the nCounter assay. The nCounter assay had a concordance rate of 83.4 % (kappa value, 0.35), a sensitivity of 66.7 %, a specificity of 85.2 %, a negative predictive value of 96 %, and a positive predictive value of 32.6 % compared with HER2 IHC/ISH results. Fresh frozen (FF) samples revealed a higher concordance rate (91.5 %, kappa value, 0.59) than FFPE samples (78.5 %, kappa value 0.27) and showed a high specificity (97.2 %).

CONCLUSION

The nCounter CNV assay is a reliable and practical method to detect high CN variations. Given the intra-tumoral HER2 heterogeneity and normal cell contamination, additional IHC and/or FISH is necessary and needs caution in interpretation, especially in FFPE tissue samples.

Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration

The advent of high-throughput multi-platform genomics technologies providing whole-genome molecular summaries of biological samples has revolutionalized biomedical research. These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges. The field of Bioinformatics has emerged to deal with these challenges, and is comprised of many quantitative and biological scientists working together to effectively process these data and extract the treasure trove of information they contain. Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts. In this article, we attempt to summarize some of the key contributions of statisticians to bioinformatics, focusing on four areas: (1) experimental design and reproducibility, (2) preprocessing and feature extraction, (3) unified modeling, and (4) structure learning and integration. In each of these areas, we highlight some key contributions and try to elucidate the key statistical principles underlying these methods and approaches. Our goals are to demonstrate major ways in which statisticians have contributed to bioinformatics, encourage statisticians to get involved early in methods development as new technologies emerge, and to stimulate future methodological work based on the statistical principles elucidated in this article and utilizing all availble information to uncover new biological insights.

Establishment of a novel cellular model for myxofibrosarcoma heterogeneity

Human cancers frequently display substantial intra-tumoural heterogeneity in virtually all distinguishable phenotypic features, such as cellular morphology, gene expression, and metastatic potential. In order to investigate tumour heterogeneity in myxofibrosarcoma, we established a novel myxofibrosarcoma cell line with two well defined sub-clones named MUG-Myx2a and MUG-Myx2b. The parental tumour tissue and both MUG-Myx2 cell lines showed the same STR profile. The fact that MUG-Myx2a showed higher proliferation activity, faster migration and enhanced tumourigenicity was of particular interest. NGS mutation analysis revealed corresponding mutations in the FGFR3, KIT, KDR and TP53 genes. In contrast, the MUG-Myx2a cell lines showed an additional PTEN mutation. Analysis of CNV uncovered a highly aberrant karyotype with frequent losses and gains in the tumour sample. The two MUG-Myx2 cell lines share several CNV features of the tumour tissue, while some CNVs are present only in the two cell lines. Furthermore, certain CNV gains and losses that are exclusive to either MUG-Myx2a or MUG-Myx2b, distinguish the two cell lines. As it is currently not possible to purchase two different sarcoma cell lines derived from the same patient, the novel myxofibrosarcoma cell lines MUG-Myx2a and MUG-Myx2b will be useful tools to study pathogenesis, tumour heterogeneity and treatment options.

A Robust Protocol for Using Multiplexed Droplet Digital PCR to Quantify Somatic Copy Number Alterations in Clinical Tissue Specimens

The ability of droplet digital PCR (ddPCR) to accurately determine the concentrations of amplifiable targets makes it a promising platform for measuring copy number alterations (CNAs) in genomic biomarkers. However, its application to clinical samples, particularly formalin-fixed paraffin-embedded specimens, will require strategies to reliably determine CNAs in DNA of limited quantity and quality. When applied to cancerous tissue, those methods must also account for global genetic instability and the associated probability that the abundance(s) of one or more chosen reference loci do not represent the average ploidy of cells comprising the specimen. Here we present an experimental design strategy and associated data analysis tool that enables accurate determination of CNAs in a panel of biomarkers using multiplexed ddPCR. The method includes strategies to optimize primer and probes design to cleanly segregate droplets in the data output from reaction wells amplifying multiple independent templates, and to correct for bias from artifacts such as DNA fragmentation. We demonstrate how a panel of reference loci can be used to determine a stable CNA-neutral benchmark. These innovations, when taken together, provide a comprehensive strategy that can be used to reliably detect biomarker CNAs in DNA extracted from either frozen or FFPE tissue biopsies.

A semiparametric Bayesian model for comparing DNA copy numbers

We propose a two-step method for the analysis of copy number data. We first define the partitions of genome aberrations and conditional on the partitions we introduce a semiparametric Bayesian model for the analysis of multiple samples from patients with different subtypes of a disease. While the biological interest is to identify regions of differential copy numbers across disease subtypes, our model also includes sample-specific random effects that account for copy number alterations between different samples in the same disease subtype. We model the subtype and sample-specific effects using a random effects mixture model. The subtype's main effects are characterized by a mixture distribution whose components are assigned Dirichlet process priors. The performance of the proposed model is examined using simulated data as well as a breast cancer genomic data set.

Molecular Karyotyping: Array CGH Quality Criteria for Constitutional Genetic Diagnosis

Array CGH (comparative genomic hybridization) enables the identification of chromosomal copy number changes. The availability of clone sets covering the human genome opens the possibility for the widespread use of array CGH for both research and diagnostic purposes. In this manuscript we report on the parameters that were critical for successful implementation of the technology, assess quality criteria, and discuss the potential benefits and pitfalls of the technology for improved pre- and postnatal constitutional genetic diagnosis. We propose to name the genome-wide array CGH “molecular karyotyping,” in analogy with conventional karyotyping that uses staining methods to visualize chromosomes.