Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

BMC Bioinformatics

Sylvain Mareschal¹  Alison Celebi²  Hervé Tilly²  Fabrice Jardin²  Mathieu Viennot²  Elodie Bohers²  Marie-Delphine Lanic²  Vincent Sater²  Vinciane Marchand²  Pierre-Julien Viailly²  Sydney Dubois²  Philippe Ruminy²  Dominique Penther²  Hélène Dauchel³  Thierry Lecroq³  Caroline Berard³  Nicolas Vergne⁴ 

[1] INSERM U1052 UMR CNRS 5286, Cancer Research Center of Lyon;INSERM U1245, Team Genomics and Biomarkers of Lymphoma and Solid Tumors, Normandie Univ, UNIROUEN;LITIS EA 4108, Normandie Univ, UNIROUEN;LMRS UMRS 6085, Normandie Univ, UNIROUEN;

关键词: UMI;    CNV calling;    Next generation sequencing;   

DOI  :  10.1186/s12859-021-04060-4

来源: DOAJ

【 摘 要 】

Abstract 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.

【 授权许可】

Unknown