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Deconvolution of nucleic-acid length distributions: a gel electrophoresis analysis tool and applications
Author(s) -
Riccardo Ziraldo,
Massa J. Shoura,
Andrew Fire,
Stephen D. Levene
Publication year - 2019
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gkz534
Subject(s) - deconvolution , biology , software , dna , biological system , gel electrophoresis , nucleic acid , univariate , computational biology , sample (material) , dna sequencing , computer science , data mining , algorithm , chromatography , genetics , multivariate statistics , machine learning , chemistry , programming language
Next-generation DNA-sequencing (NGS) technologies, which are designed to streamline the acquisition of massive amounts of sequencing data, are nonetheless dependent on various preparative steps to generate DNA fragments of required concentration, purity and average size (molecular weight). Current automated electrophoresis systems for DNA- and RNA-sample quality control, such as Agilent’s Bioanalyzer® and TapeStation® products, are costly to acquire and use; they also provide limited information for samples having broad size distributions. Here, we describe a software tool that helps determine the size distribution of DNA fragments in an NGS library, or other DNA sample, based on gel-electrophoretic line profiles. The software, developed as an ImageJ plug-in, allows for straightforward processing of gel images, including lane selection and fitting of univariate functions to intensity distributions. The user selects the option of fitting either discrete profiles in cases where discrete gel bands are visible or continuous profiles, having multiple bands buried under a single broad peak. The method requires only modest imaging capabilities and is a cost-effective, rigorous alternative characterization method to augment existing techniques for library quality control.

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