March 12, 2021 at 15:56
Viailly et al, BMC Bioinformatics 2021
BMC Bioinformatics. 2021 Mar 12;22(1):120.
doi: 10.1186/s12859-021-04060-4.
Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers
Pierre-Julien Viailly, Vincent Sater, Mathieu Viennot, Elodie Bohers, Nicolas Vergne, Caroline Berard, Hélène Dauchel, Thierry Lecroq, Alison Celebi, Philippe Ruminy, Vinciane Marchand, Marie-Delphine Lanic, Sydney Dubois, Dominique Penther, Hervé Tilly, Sylvain Mareschal, Fabrice Jardin
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.
doi: 10.1186/s12859-021-04060-4.
Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers
Pierre-Julien Viailly, Vincent Sater, Mathieu Viennot, Elodie Bohers, Nicolas Vergne, Caroline Berard, Hélène Dauchel, Thierry Lecroq, Alison Celebi, Philippe Ruminy, Vinciane Marchand, Marie-Delphine Lanic, Sydney Dubois, Dominique Penther, Hervé Tilly, Sylvain Mareschal, Fabrice Jardin
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.
Pubmed, PMID: 33711922