Summary: Genes sharing functions, expression patterns or quantitative traits are not randomly distributed along eukaryotic genomes. In order to study the distribution of genes that share a given feature, we present Cluster Locator, an online analysis and visualization tool. Cluster Locator determines the number, size and position of all the clusters formed by the protein-coding genes on a list according to a given maximum gap, the percentage of gene clustering of the list and its statistical significance. The output includes a visual representation of the distribution of genes and gene clusters along the reference genome. Availability and implementation: Cluster Locator is freely available at http://clusterlocator.bnd.edu.uy/. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: SNP arrays output two signals that reflect the total genomic copy number (LRR) and the allelic ratio (BAF), which in combination allow the characterisation of allele-specific copy numbers (ASCNs). While methods based on hidden Markov models (HMMs) have been extended from array comparative genomic hybridisation (aCGH) to jointly handle the two signals, only one method based on change-point detection, ASCAT, performs bivariate segmentation. RESULTS: In the present work, we introduce a generic framework for bivariate segmentation of SNP array data for ASCN analysis. For the matter, we discuss the characteristics of the typically applied BAF transformation and how they affect segmentation, introduce concepts of multivariate time series analysis that are of concern in this field and discuss the appropriate formulation of the problem. The framework is implemented in a method named CnaStruct, the bivariate form of the structural change model (SCM), which has been successfully applied to transcriptome mapping and aCGH. CONCLUSIONS: On a comprehensive synthetic dataset, we show that CnaStruct outperforms the segmentation of existing ASCN analysis methods. Furthermore, CnaStruct can be integrated into the workflows of several ASCN analysis tools in order to improve their performance, specially on tumour samples highly contaminated by normal cells.

BACKGROUND: The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. A number of tools have been recently developed but their performance needs yet to be thoroughly assessed. To this aim, a comprehensive model that integrates the factors of normal cell contamination and intra-tumour heterogeneity and that can be translated to synthetic data on which to perform benchmarks is indispensable. RESULTS: We propose such model and implement it in an R package called CnaGen to synthetically generate a wide range of alterations under different normal cell contamination levels. Six recently published methods for CNA and loss of heterozygosity (LOH) detection on tumour samples were assessed on this synthetic data and on a dilution series of a breast cancer cell-line: ASCAT, GAP, GenoCNA, GPHMM, MixHMM and OncoSNP. We report the recall rates in terms of normal cell contamination levels and alteration characteristics: length, copy number and LOH state, as well as the false discovery rate distribution for each copy number under different normal cell contamination levels.Assessed methods are in general better at detecting alterations with low copy number and under a little normal cell contamination levels. All methods except GPHMM, which failed to recognize the alteration pattern in the cell-line samples, provided similar results for the synthetic and cell-line sample sets. MixHMM and GenoCNA are the poorliest performing methods, while GAP generally performed better. This supports the viability of approaches other than the common hidden Markov model (HMM)-based. CONCLUSIONS: We devised and implemented a comprehensive model to generate data that simulate tumoural samples genotyped using SNP arrays. The validity of the model is supported by the similarity of the results obtained with synthetic and real data. Based on these results and on the software implementation of the methods, we recommend GAP for advanced users and GPHMM for a fully driven analysis.