Search

Copy number variation (CNV) is one of the main structural variations in the human genome and accounts for a considerable proportion of variations. As CNVs can directly or indirectly cause cancer, mental illness, and genetic disease in humans, their effective detection in humans is of great interest in the fields of oncogene discovery, clinical decision-making, bioinformatics, and drug discovery. The advent of next-generation sequencing data makes CNV detection possible, and a large number of CNV detection tools are based on next-generation sequencing data. Due to the complexity (e.g., bias, noise, alignment errors) of next-generation sequencing data and CNV structures, the accuracy of existing methods in detecting CNVs remains low. In this work, we design a new CNV detection approach, called shortest path-based Copy number variation (SPCNV), to improve the detection accuracy of CNVs. SPCNV calculates the k nearest neighbors of each read depth and defines the shortest path, shortest path relation, and shortest path cost sets based on which further calculates the mean shortest path cost of each read depth and its k nearest neighbors. We utilize the ratio between the mean shortest path cost for each read depth and the mean of the mean shortest path cost of its k nearest neighbors to construct a relative shortest path score formula that is able to determine a score for each read depth. Based on the score profile, a boxplot is then applied to predict CNVs. The performance of the proposed method is verified by simulation data experiments and compared against several popular methods of the same type. Experimental results show that the proposed method achieves the best balance between recall and precision in each set of simulated samples. To further verify the performance of the proposed method in real application scenarios, we then select real sample data from the 1,000 Genomes Project to conduct experiments. The proposed method achieves the best F1-scores in almost all samples. Therefore, the proposed method can be used as a more reliable tool for the routine detection of CNVs.

Copy number variations (CNVs) contribute significantly to human genomic variability, some of which lead to diseases. However, effective detection of CNVs from whole genome next generation sequencing data (NGS) remains challenging. Here, we present BagGMM, a new method to call CNVs using tumor-normal matched samples from NGS data. BagGMM extracts read depth ratios of tumor samples to normal samples, divides the genomic sequences into segments by sliding windows to count the average coverage ratio of each segment, filters candidate deletions and duplications based on a coarse criterion of coverage ratio, and then builds Gaussian Mixture Model (GMM) for remaining ratios to identify the remaining ambiguous copy number states after filtration. Bagging multiple GMMs makes false positive calls descent instead of using one GMM, thus enhancing the detection power of BagGMM. Considering the computation speed of GMMs and false positive calls, we employ a segmentation procedure “large window and then small windows”, which is also helpful to determine boundary of CNV regions. We apply BagGMM to three simulation datasets and two groups of human whole genome sequencing (WGS) data for breast cancer patients and ovarian cancer patients to identify CNVs, respectively. All performed experiments demonstrate that BagGMM has the capability of robustly identification of CNVs with different sizes and states. The performance of this tool is compared to four peer existing CNV detection methods. BagGMM shows a significant improvement in both sensitivity and specificity for detecting both copy number gains and losses.

Copy number variation (CNV) is a common phenomenon in the human genome, accounting for a large proportion of structural variations. Accurate detection of CNVs has been regarded as a regular approach for studying cancer mechanisms and evolution and seeking targeted drugs for cancer diagnosis. Next-generation sequencing (NGS) data has provided an unprecedented opportunity for this task. Currently, a large number of methods have been developed for CNV detection by using NGS data. Each of them has its own viewpoint on the characteristics of CNVs associated with the features of NGS data. Due to the intrinsic complexity of CNVs, new methods with a comprehensive viewpoint on the characteristics of CNVs combined with NGS data are required for the detection of various forms of CNVs. In this paper, we propose an intra-hybrid approach, IhybCNV, for the detection of CNVs from NGS data. The central idea of the IhybCNV is that it regards CNVs as outlier events from various viewpoints on the read depth (RD) profile to be analyzed. More precisely, the IhybCNV integrates five individual detectors to calculate five types of outlier scores for each genome segment and then combines the five types of outlier scores into an anomaly score via locally selective combination in parallel outlier ensembles (LSCP). With the anomaly scores of the genome segments, the IhybCNV determines abnormal segments (CNVs) or normal segments by designing a binary clustering model (BCM). The performance of the IhybCNV is evaluated by carrying out simulation studies and real data applications and is compared with five peer methods. The experimental results demonstrate that the IhybCNV obtains a higher F1-score value than the other methods. Thus, the IhybCNV could be expected as a supplementary to current methods and might become a promising tool for the detection of genome mutations.

Interspersed duplicated insertion (idINS) is a common type of genomic insertion and plays an important role in genomic instability in cancer genesis. Nevertheless, the detection of such type of insertions is challenging, since the reads originated from idINS regions in the donor sample are most likely to be mapped perfectly to other regions in the reference. Most of the existing approaches adopt paired-end mapping to detect idINSs, but the characterization of idINSs larger than the mean insert size is still challenging due to the short sequencing reads. Therefore, there is still a need for practical algorithms to detect and infer idINSs regardless of their lengths. Here, we present a new algorithm, called DIPins, which can accurately detect and infer idINSs contents from paired-end reads. DIPins is capable of detecting breakpoint positions and inferring the contents of idINSs even when the length of variation exceeds the paired-end insert size. The major principle of DIPins is that it extracts multiple signatures from split reads and integrates them to determine idINS positions and adopts a dynamic process to construct idINS contents by iteratively generating unobserved split reads from the restricted area around the idINS breakpoint. We test the performance of DIPins on both simulation and real data. The results demonstrate its advantages over other methods and its potential application prospects in the accurate characterization of idINSs in human genome.

DDRA strategy for avoiding generating invalid solutions.Dynamic fret width (fw) strategy of two phases for balance global exploration and local exploitation.DIHS algorithm for improving the solution quality and search efficiency.Take-one strategy for a fast convergence to globally optimal solution is analyzed. Harmony search (HS) and its variants have been found successful applications, however with poor solution accuracy and convergence performance for high-dimensional (?200) multimodal optimization problems. The reason is mainly huge search space and multiple local minima. To tackle the problem, we present a new HS algorithm called DIHS, which is based on Dynamic-Dimensionality-Reduction-Adjustment (DDRA) and dynamic fret width (fw) strategy. The former is for avoiding generating invalid solutions and the latter is to balance global exploration and local exploitation. Theoretical analysis on the DDRA strategy for success rate of update operation is given and influence of related parameters on solution accuracy is investigated. Our experiments include comparison on solution accuracy and CPU time with seven typical HS algorithms and four widely used evolutionary algorithms (SaDE, CoDE, CMAES and CLPSO) and statistical comparison by the Wilcoxon Signed-Rank Test with the seven HS algorithms and four evolutionary algorithms. The problems in experiments include twelve multimodal and four complex uni-modal functions with high-dimensionality.Experimental results indicate that the proposed approach can provide significant improvement on solution accuracy with less CPU time in solving high-dimensional multimodal optimization problems, and the more dimensionality that the optimization problem is, the more benefits it provides.

Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by incorporating alternating word frequency and normalized Lempel-Ziv complexity. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on three widely used benchmark datasets, 25PDB, 1189 and 640, respectively. We report 83.6%, 81.8% and 83.6% prediction accuracies for 25PDB, 1189 and 640 benchmarks, respectively. Comparison of our results with other methods shows that the proposed method is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity datasets and may at least play an important complementary role to existing methods.

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression. In recent years, many novel microRNAs have been discovered at unprecedented depth after advent of deep sequencing technology, but accurate identification of miRNAs is still a challenge due to a large number of isoforms, dynamic range of miRNA expression and unobvious biological characteristics in short mature miRNA sequences. We present a pattern-based approach, mirPD, which uses a two-stage filtration to identify miRNAs from deep sequencing data. In the first filtration stage, patterns capturing conserved knowledge of real miRNAs are extracted from real (published) miRNAs to filter reads. The reads passing the pattern filtration are then mapped to the genome to get candidate precursors which are further filtered according to miRNA biological features in the second stage. Compared with the classic miRNA identification method miRDeep (v1 and v2) on a typical dataset, the experimental result indicates that the mirPD provides higher sensitivity and similar precision, accuracy and specificity.