Using a constraint-based regression method for relative quantification of somatic mutations in pyrosequencing signals: a case for NRAS analysis
| Autor: | Anne-France Dekairelle, Louise Nienhaus, Jamal Badir, Annie Robert, Jean-Luc Gala, Jérôme Ambroise |
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| Jazyk: | angličtina |
| Předmět: |
AdvISER-PYRO-SMQ
0301 basic medicine Genetics Neuroblastoma RAS viral oncogene homolog somatic Somatic cell Research Point mutation Absolute quantification Applied Mathematics Pyrosequencing Biology Regression DNA sequencing 03 medical and health sciences R package 030104 developmental biology Computational Theory and Mathematics Structural Biology Sparse representation Molecular Biology |
| Zdroj: | Algorithms for Molecular Biology : AMB |
| ISSN: | 1748-7188 |
| DOI: | 10.1186/s13015-016-0086-4 |
| Popis: | Background Pyrosequencing Allele Quantification (AQ) is a cost-effective DNA sequencing method that can be used for detecting somatic mutations in formalin-fixed paraffin-embedded (FFPE) samples. The method displays a low turnaround time and a high sensitivity. Pyrosequencing suffers however from two main drawbacks including (i) low specificity and (ii) difficult signal interpretation when multiple mutations are reported in a hotspot genomic region. Results Using a constraint-based regression method, the new AdvISER-PYRO-SMQ algorithm was developed in the current study and implemented into an R package. As a proof-of-concept, AdvISER-PYRO-SMQ was used to identify a set of 9 distinct point mutations affecting codon 61 of the NRAS oncogene. In parallel, a pyrosequencing assay using the Qiagen software and its AQ module was used to assess selectively the presence of a single point mutation (NRAS\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c.182A>G$$\end{document}c.182A>G - Q61R-1) among the set of codon 61 mutations, and to analyze related pyrosequencing signals. AdvISER-PYRO-SMQ produced a lower limit of blank (0 %) than the AQ module of Qiagen software (5.1 %) and similar limit of detection were obtained for both software (5.6 vs 4.8 %). AdvISER-PYRO-SMQ was able to screen for the presence of 9 distinct mutations with a single pyrosequencing reaction whereas the AQ module was limited to screen a single mutation per reaction. Conclusion Using a constraint-based regression method enables to analyze pyrosequencing signal and to detect multiple mutations within a hotspot genomic region with an optimal compromise between sensitivity and specificity. The AdvISER-PYRO-SMQ R package provides a generic tool which can be applied on a wide range of somatic mutations. Its implementation in a Shiny web interactive application (available at https://ucl-irec-ctma.shinyapps.io/Pyrosequencing-NRAS-61/) enables its use in research or clinical routine applications. Electronic supplementary material The online version of this article (doi:10.1186/s13015-016-0086-4) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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