MOTIVATION: Large-scale rearrangements and copy number changes combined with different modes of clonal evolution create extensive somatic genome diversity, making it difficult to develop versatile and scalable variant calling tools and create well-calibrated benchmarks. RESULTS: We developed a new simulation framework tHapMix that enables the creation of tumour samples with different ploidy, purity and polyclonality features. It easily scales to simulation of hundreds of somatic genomes, while re-use of real read data preserves noise and biases present in sequencing platforms. We further demonstrate tHapMix utility by creating a simulated set of 140 somatic genomes and showing how it can be used in training and testing of somatic copy number variant calling tools. AVAILABILITY AND IMPLEMENTATION: tHapMix is distributed under an open source license and can be downloaded from https://github.com/Illumina/tHapMix CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Current high-throughput sequencing has greatly transformed genome sequence analysis. In the context of very low-coverage sequencing (<0.1×), performing 'binning' or 'windowing' on mapped short sequences ('reads') is critical to extract genomic information of interest for further evaluation, such as copy-number alteration analysis. If the window size is too small, many windows will exhibit zero counts and almost no pattern can be observed. In contrast, if the window size is too wide, the patterns or genomic features will be 'smoothed out'. Our objective is to identify an optimal window size in between the two extremes. RESULTS: We assume the reads density to be a step function. Given this model, we propose a data-based estimation of optimal window size based on Akaike's information criterion (AIC) and cross-validation (CV) log-likelihood. By plotting the AIC and CV log-likelihood curve as a function of window size, we are able to estimate the optimal window size that minimizes AIC or maximizes CV log-likelihood. The proposed methods are of general purpose and we illustrate their application using low-coverage next-generation sequence datasets from real tumour samples and simulated datasets. AVAILABILITY AND IMPLEMENTATION: An R package to estimate optimal window size is available at http://www1.maths.leeds.ac.uk/∼arief/R/win/.

MOTIVATION: Comparison of read depths from next-generation sequencing between cancer and normal cells makes the estimation of copy number alteration (CNA) possible, even at very low coverage. However, estimating CNA from patients' tumour samples poses considerable challenges due to infiltration with normal cells and aneuploid cancer genomes. Here we provide a method that corrects contamination with normal cells and adjusts for genomes of different sizes so that the actual copy number of each region can be estimated. RESULTS: The procedure consists of several steps. First, we identify the multi-modality of the distribution of smoothed ratios. Then we use the estimates of the mean (modes) to identify underlying ploidy and the contamination level, and finally we perform the correction. The results indicate that the method works properly to estimate genomic regions with gains and losses in a range of simulated data as well as in two datasets from lung cancer patients. It also proves a powerful tool when analysing publicly available data from two cell lines (HCC1143 and COLO829). AVAILABILITY: An R package, called CNAnorm, is available at http://www.precancer.leeds.ac.uk/cnanorm or from Bioconductor. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.