A novel signal processing approach for the detection of copy number variations in the human genome

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.

Preprocessing Sequence Coverage Data for More Precise Detection of Copy Number Variations

Copy number variation (CNV) is a type of genomic/genetic variation that plays an important role in phenotypic diversity, evolution, and disease susceptibility. Next generation sequencing (NGS) technologies have created an opportunity for more accurate detection of CNVs with higher resolution. However, efficient and precise detection of CNVs remains challenging due to high levels of noise and biases, data heterogeneity, and the "big data" nature of NGS data. Sequence coverage (readcount) data are mostly used for detecting CNVs, specially for whole exome sequencing data. Readcount data are contaminated with several types of biases and noise that hinder accurate detection of CNVs. In this work, we introduce a novel preprocessing pipeline for reducing noise and biases to improve the detection accuracy of CNVs in heterogeneous NGS data, such as cancer whole exome sequencing data. We have employed several normalization methods to reduce readcount's biases that are due to GC content of reads, read alignment problems, and sample impurity. We have also developed a novel efficient and effective smoothing approach based on Taut String to reduce noise and increase CNV detection power. Using simulated and real data we showed that employing the proposed preprocessing pipeline significantly improves the accuracy of CNV detection.

Noise cancellation using total variation for copy number variation detection

BACKGROUND

Due to recent advances in sequencing technologies, sequence-based analysis has been widely applied to detecting copy number variations (CNVs). There are several techniques for identifying CNVs using next generation sequencing (NGS) data, however methods employing depth of coverage or read depth (RD) have recently become a main technique to identify CNVs. The main assumption of the RD-based CNV detection methods is that the readcount value at a specific genomic location is correlated with the copy number at that location. However, readcount data's noise and biases distort the association between the readcounts and copy numbers. For more accurate CNV identification, these biases and noise need to be mitigated. In this work, to detect CNVs more precisely and efficiently we propose a novel denoising method based on the total variation approach and the Taut String algorithm.

RESULTS

To investigate the performance of the proposed denoising method, we computed sensitivities, false discovery rates and specificities of CNV detection when employing denoising, using both simulated and real data. We also compared the performance of the proposed denoising method, Taut String, with that of the commonly used approaches such as moving average (MA) and discrete wavelet transforms (DWT) in terms of sensitivity of detecting true CNVs and time complexity. The results show that Taut String works better than DWT and MA and has a better power to identify very narrow CNVs. The ability of Taut String denoising in preserving CNV segments' breakpoints and narrow CNVs increases the detection accuracy of segmentation algorithms, resulting in higher sensitivities and lower false discovery rates.

CONCLUSIONS

In this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection.

A gene interaction network‑based method to measure the common and heterogeneous mechanisms of gynecological cancer

Gynecological malignancies are a leading cause of mortality in the female population. The present study intended to identify the association between three severe types of gynecological cancer, specifically ovarian cancer, cervical cancer and endometrial cancer, and to identify the connective driver genes, microRNAs (miRNAs) and biological processes associated with these types of gynecological cancer. In the present study, individual driver genes for each type of cancer were identified using integrated analysis of multiple microarray data. Gene Ontology (GO) has been used widely in functional annotation and enrichment analysis. In the present study, GO enrichment analysis revealed a number of common biological processes involved in gynecological cancer, including 'cell cycle' and 'regulation of macromolecule metabolism'. Kyoto Encyclopedia of Genes and Genomes pathway analysis is a resource for understanding the high‑level functions and utilities of a biological system from molecular‑level information. In the present study, the most common pathway was 'cell cycle'. A protein‑protein interaction network was constructed to identify a hub of connective genes, including minichromosome maintenance complex component 2 (MCM2), matrix metalloproteinase 2 (MMP2), collagen type I α1 chain (COL1A1) and Jun proto‑oncogene AP‑1 transcription factor subunit (JUN). Survival analysis revealed that the expression of MCM2, MMP2, COL1A1 and JUN was associated with the prognosis of the aforementioned gynecological cancer types. By constructing an miRNA‑driver gene network, let‑7 targeted the majority of the driver genes. In conclusion, the present study demonstrated a connection model across three types of gynecological cancer, which was useful in identifying potential diagnostic markers and novel therapeutic targets, in addition to in aiding the prediction of the development of cancer as it progresses.

Neuronal networks in the developing brain are adversely modulated by early psychosocial neglect

The brain's neural circuitry plays a ubiquitous role across domains in cognitive processing and undergoes extensive reorganization during the course of development in part as a result of experience. In this study we investigated the effects of profound early psychosocial neglect associated with institutional rearing on the development of task-independent brain networks, estimated from longitudinally acquired electroencephalographic (EEG) data from <30 to 96 mo, in three cohorts of children from the Bucharest Early Intervention Project (BEIP), including abandoned children reared in institutions who were randomly assigned either to a foster care intervention or to remain in care as usual and never-institutionalized children. Two aberrantly connected brain networks were identified in children that had been reared in institutions: 1) a hyperconnected parieto-occipital network, which included cortical hubs and connections that may partially overlap with default-mode network, and 2) a hypoconnected network between left temporal and distributed bilateral regions, both of which were aberrantly connected across neural oscillations. This study provides the first evidence of the adverse effects of early psychosocial neglect on the wiring of the developing brain. Given these networks' potentially significant role in various cognitive processes, including memory, learning, social communication, and language, these findings suggest that institutionalization in early life may profoundly impact the neural correlates underlying multiple cognitive domains, in ways that may not be fully reversible in the short term.NEW & NOTEWORTHY This paper provides first evidence that early psychosocial neglect associated with institutional rearing profoundly affects the development of the brain's neural circuitry. Using longitudinally acquired electrophysiological data from the Bucharest Early Intervention Project (BEIP), the paper identifies multiple task-independent networks that are abnormally connected (hyper- or hypoconnected) in children reared in institutions compared with never-institutionalized children. These networks involve spatially distributed brain areas and their abnormal connections may adversely impact neural information processing across cognitive domains.

Optimization of Signal Decomposition Matched Filtering (SDMF) for Improved Detection of Copy-Number Variations

We aim to improve the performance of the previously proposed signal decomposition matched filtering (SDMF) method [26] for the detection of copy-number variations (CNV) in the human genome. Through simulations, we show that the modified SDMF is robust even at high noise levels and outperforms the original SDMF method, which indirectly depends on CNV frequency. Simulations are also used to develop a systematic approach for selecting relevant parameter thresholds in order to optimize sensitivity, specificity and computational efficiency. We apply the modified method to array CGH data from normal samples in the cancer genome atlas (TCGA) and compare detected CNVs to those estimated using circular binary segmentation (CBS) [19], a hidden Markov model (HMM)-based approach [11] and a subset of CNVs in the Database of Genomic Variants. We show that a substantial number of previously identified CNVs are detected by the optimized SDMF, which also outperforms the other two methods.

Early Psychosocial Neglect Adversely Impacts Developmental Trajectories of Brain Oscillations and Their Interactions

Rhythmicity is a fundamental property of neural activity at multiple spatiotemporal scales, and associated oscillations represent a critical mechanism for communication and transmission of information across brain regions. During development, these oscillations evolve dynamically as a function of neural maturation and may be modulated by early experiences, positive and/or negative. This study investigated the impact of psychosocial deprivation associated with institutional rearing in early life and the effects of subsequent foster care intervention on developmental trajectories of neural oscillations and their cross-frequency correlations. Longitudinally acquired nontask EEGs from three cohorts of children from the Bucharest Early Intervention Project were analyzed. These included abandoned children initially reared in institutions and subsequently randomized to be placed in foster care or receive care as usual (prolonged institutional rearing) and a group of never-institutionalized children. Oscillation trajectories were estimated from 42 to 96 months, that is, 1-3 years after all children in the intervention arm of the study had been placed in foster care. Significant differences between groups were estimated for the amplitude trajectories of cognitive-related gamma, beta, alpha, and theta oscillations. Similar differences were identified as a function of time spent in institutions, suggesting that increased time spent in psychosocial neglect may have profound and widespread effects on brain activity. Significant group differences in cross-frequency coupling were estimated longitudinally between gamma and lower frequencies as well as alpha and lower frequencies. Lower cross-gamma coupling was estimated at 96 months in the group of children that remained in institutions at that age compared to the other two groups, suggesting potentially impaired communication between local and long-distance brain networks in these children. In contrast, higher cross-alpha coupling was estimated in this group compared to the other two groups at 96 months, suggesting impaired suppression of alpha-theta and alpha-delta activity, which has been associated with neuropsychiatric disorders. Age at foster care placement had a significant positive modulatory effect on alpha and beta trajectories and their mutual coupling, although by 96 months these trajectories remained distinct from those of never-institutionalized children. Overall, these findings suggest that early psychosocial neglect may profoundly impact neural maturation, particularly the evolution of neural oscillations and their interactions across a broad frequency range. These differences may result in widespread deficits across multiple cognitive domains.

Resting and task-modulated high-frequency brain rhythms measured by scalp encephalography in infants with tuberous sclerosis complex

The electrophysiological correlates of cognitive deficits in tuberous sclerosis complex (TSC) are not well understood, and modulations of neural dynamics by neuroanatomical abnormalities that characterize the disorder remain elusive. Neural oscillations (rhythms) are a fundamental aspect of brain function, and have dominant frequencies in a wide frequency range. The spatio-temporal dynamics of these frequencies in TSC are currently unknown. Using a novel signal decomposition approach this study investigated dominant cortical frequencies in 10 infants with TSC, in the age range 18-30 months, and 12 age-matched healthy controls. Distinct spectral characteristics were estimated in the two groups. High-frequency [in the high-gamma (>50 Hz) and ripple (>80 Hz) ranges], non-random EEG components were identified in both TSC and healthy infants at 18 months. Additional components in the lower gamma (30-50 Hz) ranges were also identified, with higher characteristic frequencies in TSC than in controls. Lower frequencies were statistically identical in both sub-groups. A significant shift in the high-frequency spectral content of the EEG was observed as a function of age, independently of task performance, possibly reflecting an overall maturation of developing neural circuits. This shift occurred earlier in healthy infants than in TSC, i.e., by age 20 months the highest dominant frequencies were in the high gamma range, whereas in TSC dominant frequencies above 100 Hz were still measurable. At age 28-30 months a statistically significant decrease in dominant high frequencies was observed in both TSC and healthy infants, possibly reflecting increased myelination and neuronal connection strengthening with age. Although based on small samples, and thus preliminary, the findings in this study suggest that dominant cortical rhythms, a fundamental aspect of neurodynamics, may be affected in TSC, possibly leading to impaired information processing in the brain.

Function of cancer associated genes revealed by modern univariate and multivariate association tests

Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.

Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition

Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.

Use of autocorrelation scanning in DNA copy number analysis

MOTIVATION

Data quality is a critical issue in the analyses of DNA copy number alterations obtained from microarrays. It is commonly assumed that copy number alteration data can be modeled as piecewise constant and the measurement errors of different probes are independent. However, these assumptions do not always hold in practice. In some published datasets, we find that measurement errors are highly correlated between probes that interrogate nearby genomic loci, and the piecewise-constant model does not fit the data well. The correlated errors cause problems in downstream analysis, leading to a large number of DNA segments falsely identified as having copy number gains and losses.

METHOD

We developed a simple tool, called autocorrelation scanning profile, to assess the dependence of measurement error between neighboring probes.

RESULTS

Autocorrelation scanning profile can be used to check data quality and refine the analysis of DNA copy number data, which we demonstrate in some typical datasets.

CONTACT

lzhangli@mdanderson.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Space-time adaptive processing for improved estimation of preictal seizure activity

Detection of precursory, seizure-related activity in electroencephalograms (EEG) is a clinically important and difficult problem in the field of epilepsy. Seizure detection methods often aim to identify specific features and correlations between preictal EEG signals that differentiate them from interictal/nonictal signals. Typically, these methods use information from nonictal EEGs to establish detection thresholds, and do not otherwise incorporate their characteristics into the detection. A space-time adaptive approach is proposed to improve detection of seizure-related preictal activity in scalp EEG, using multiple patient-specific baseline signals to optimize the estimate of the baseline covariance matrix. A simplified model of the preictal EEG is assumed, which describes this signal as a linear superposition of seizure-related activity and baseline activity (treated as an interference signal). It is shown that when an improved estimate of the baseline covariance is included in the preictal detector, the true positive rate increases significantly and also the false positive rate decreases significantly.

Estimation of correlations between copy-number variants in non-coding DNA

Allelic DNA aberrations across our genome have been associated with normal human genetic heterogeneity as well as with a number of diseases and disorders. When copy-number variations (CNVs) occur in gene-coding regions, known relationships between genes may help us understand correlations between CNVs. However, a large number of these aberrations occur in non-coding, extragenic regions and their correlations may be characterized only quantitatively, e.g., probabilistically, but not functionally. Using a signal processing approach to CNV detection, we identified distributed CNVs in short, non-coding regions across chromosomes and investigated their potential correlations. We estimated predominantly local correlations between CNVs within the same chromosome, and a small number of apparently random long-distance correlations.

Multiscale information for network characterization in epilepsy

We have developed a multiscale approach for the estimation of neuronal network coordination in the epileptic brain, from continuous (long-term) non-invasive electroencephalograms (EEG). The proposed approach specifically assesses the effect of large-scale network behavior on local network coordination, at individual dominant frequencies (modes) of the EEG spectrum. For this purpose a set of conditional information parameters is proposed to explicitly quantify the effect of global network correlation in the brain on pairwise (local) mutual information, via conditioning. These parameters are shown to be modulated in a frequency-specific manner at baseline, as well as during seizure evolution.