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Single nucleotide variant (SNV) plays an important role in cellular proliferation and tumorigenesis in various types of human cancer. Next-generation sequencing (NGS) has provided high-throughput data at an unprecedented resolution to predict SNVs. Currently, there exist many computational methods for either germline or somatic SNV discovery from NGS data, but very few of them are versatile enough to adapt to any situations. In the absence of matched normal samples, the prediction of somatic SNVs from single-tumor samples becomes considerably challenging, especially when the tumor purity is unknown. Here, we propose a new approach, STIC, to predict somatic SNVs and estimate tumor purity from NGS data without matched normal samples. The main features of STIC include: (1) extracting a set of SNV-relevant features on each site and training the BP neural network algorithm on the features to predict SNVs; (2) creating an iterative process to distinguish somatic SNVs from germline ones by disturbing allele frequency; and (3) establishing a reasonable relationship between tumor purity and allele frequencies of somatic SNVs to accurately estimate the purity. We quantitatively evaluate the performance of STIC on both simulation and real sequencing datasets, the results of which indicate that STIC outperforms competing methods. [ABSTRACT FROM AUTHOR]

Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models. We compare CONDEL to six peer methods on a large number of simulation datasets, showing improved performance in terms of true positive and false positive rates, and further validate CONDEL on three real datasets derived from the 1000 Genomes Project and the EGA archive. CONDEL obtained higher consistent results in comparison with other three single sample-based methods, and exclusively identified a number of CNVs that were previously associated with cancers. We conclude that CONDEL is a powerful tool for detecting copy number variations on single tumor samples even if these are sequenced at low-coverage. [ABSTRACT FROM AUTHOR]

Analyzing copy number variations (CNVs) from next-generation sequencing (NGS) data has become a common approach to detect disease susceptibility genes. The main challenge is how to utilize the NGS data with limited coverage depth to detect significant CNVs. Here, we introduce a new statistical method, the derivative of correlation coefficient (DCC), to detect significant CNVs that recurrently occur in multiple samples using read depth signals. We use a sliding window to calculate a correlation coefficient for each genome bin, and compute corresponding derivatives by fitting curves to the correlation coefficient. Then, the detection of significant CNVs was transformed into a problem of detecting significant derivatives reflecting genome breakpoints that can be solved using statistical hypothesis testing. We tested and compared the performance of DCC against several peer methods using a large number of simulation data sets, and validated DCC using several real sequencing data sets derived from the European Genome-Phenome archive, DNA Data Bank of Japan, and the 1000 Genomes Project. Experimental results suggest that DCC is an effective approach for identifying CNVs, outperforming peer methods in the terms of detection power and accuracy. DCC can be used to detect significant or recurrent CNVs in various NGS data sets, thus providing useful information to study genomic mutations and find disease susceptibility genes. [ABSTRACT FROM PUBLISHER]

Objective: Next-generation sequencing data has been widely used for DNA variant discovery and tumor study through computational tools. Effective simulation of such data with many realistic features is very necessary for testing existing tools and guiding the development of new tools. Methods: We present an integrated simulation system, IntSIM, to simulate common DNA variants and to generate sequencing reads for mixture genomes. IntSIM has three novel features in comparison with other simulation programs: 1) it is able to simulate both germline and somatic variants in the same sequence, 2) it deals with tumor purity so as to generate reads corresponding to heterogeneous genomes and also produce tumor-normal matched samples, and 3) it simulates correlations among SNPs, among CNVs/CNAs based on HMM models trained from real sequencing genomes, and can simulates broad and focal CNV/CNA events. Results: The simulation data of IntSIM can reflect characteristics observed from real data and are consistent with input parameters. The IntSIM software package is freely available at http://intsim.sourceforge.net/. Conclusion: Based on a great number of experiments, IntSIM performs better than other program for some scenarios, such as simulation of heterozygous SNPs, CNVs/CNAs, and can achieve some functions that other programs cannot achieve. Significance: Simulation with IntSIM can be expected to evaluate performance of methods in detecting various types of variants, analyzing tumor samples, and especially providing a realistic assessment of effect of tumor purity on identification of somatic mutations. [ABSTRACT FROM AUTHOR]