BACKGROUND: Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. RESULTS: Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. CONCLUSION: We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

MOTIVATION: Next-generation sequencing has become the go-to standard method for the detection of single-nucleotide variants in tumor cells. The use of such technologies requires a PCR amplification step and a sequencing step, steps in which artifacts are introduced at very low frequencies. These artifacts are often confused with true low-frequency variants that can be found in tumor cells and cell-free DNA. The recent use of unique molecular identifiers (UMI) in targeted sequencing protocols has offered a trustworthy approach to filter out artefactual variants and accurately call low-frequency variants. However, the integration of UMI analysis in the variant calling process led to developing tools that are significantly slower and more memory consuming than raw-reads-based variant callers. RESULTS: We present UMI-VarCal, a UMI-based variant caller for targeted sequencing data with better sensitivity compared to other variant callers. Being developed with performance in mind, UMI-VarCal stands out from the crowd by being one of the few variant callers that do not rely on SAMtools to do their pileup. Instead, at its core runs an innovative homemade pileup algorithm specifically designed to treat the UMI tags in the reads. After the pileup, a Poisson statistical test is applied at every position to determine if the frequency of the variant is significantly higher than the background error noise. Finally, an analysis of UMI tags is performed, a strand bias and a homopolymer length filter are applied to achieve better accuracy. We illustrate the results obtained using UMI-VarCal through the sequencing of tumor samples and we show how UMI-VarCal is both faster and more sensitive than other publicly available solutions. AVAILABILITY AND IMPLEMENTATION: The entire pipeline is available at https://gitlab.com/vincent-sater/umi-varcal-master under MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Although sequencing-based technologies are becoming the new reference in genome analysis, comparative genomic hybridization arrays (aCGH) still constitute a simple and reliable approach for copy number analysis. The most powerful algorithms to analyze such data have been freely provided by the scientific community for many years, but combining them is a complex scripting task. RESULTS: The cghRA framework combines a user-friendly graphical interface and a powerful object-oriented command-line interface to handle a full aCGH analysis, as is illustrated in an original series of 107 Diffuse Large B-Cell Lymphomas. New algorithms for copy-number calling, polymorphism detection and minimal common region prioritization were also developed and validated. While their performances will only be demonstrated with aCGH, these algorithms could actually prove useful to any copy-number analysis, whatever the technique used. AVAILABILITY AND IMPLEMENTATION: R package and source for Linux, MS Windows and MacOS are freely available at http://bioinformatics.ovsa.fr/cghRA. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.