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Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing.

ABSTRACT: Next-generation sequencing (NGS) promises to revolutionize precision medicine, but the existing sequencing technologies are limited in accuracy. To overcome this limitation, we propose the correctable decoding sequencing strategy, which is a duplex sequencing protocol with conservative theoretical error rates of 0.0009%. This rate is lower than that for Sanger sequencing. Here, we simulate the sequencing reactions by the self-developed software, and find that this approach has great potential in NGS in terms of sequence decoding, reassembly, error correction, and sequencing accuracy. Besides, this approach can be compatible with most SBS-based sequencing platforms, and also has the ability to compensate for some of the shortcomings of NGS platforms, thereby broadening its application for researchers. Hopefully, it can provide a powerful new protocol that can be used as an alternative to the existing NGS platforms, enabling accurate identification of rare mutations in a variety of applications in biology and medicine.

SUBMITTER: Cheng C

PROVIDER: S-EPMC9012935 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing.

Cheng Chu C Xiao Pengfeng P

Life science alliance 20220414 8

Next-generation sequencing (NGS) promises to revolutionize precision medicine, but the existing sequencing technologies are limited in accuracy. To overcome this limitation, we propose the correctable decoding sequencing strategy, which is a duplex sequencing protocol with conservative theoretical error rates of 0.0009%. This rate is lower than that for Sanger sequencing. Here, we simulate the sequencing reactions by the self-developed software, and find that this approach has great potential in ...[more]

PMID: 35422436

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Project description:BackgroundForward genetic screens are a powerful approach for identifying the genes contributing to a trait of interest. However, mutants arising in genes already known can obscure the identification of new genes contributing to the trait. Here, we describe a strategy called Candidate gene-Sequencing (Can-Seq) for rapidly identifying and filtering out mutants carrying new alleles of known and candidate genes.ResultsWe carried out a forward genetic screen and identified 40 independent Arabidopsis mutants with defects in systemic spreading of RNA interference (RNAi), or more specifically in root-to-shoot transmission of post-transcriptional gene silencing (rtp). To classify the mutants as either representing a new allele of a known or candidate gene versus carrying a mutation in an undiscovered gene, bulk genomic DNA from up to 23 independent mutants was used as template to amplify a collection of 47 known or candidate genes. These amplified sequences were combined into Can-Seq libraries and deep sequenced. Subsequently, mutations in the known and candidate genes were identified using a custom Snakemake script (https://github.com/Carroll-Lab/can_seq), and PCR zygosity tests were then designed and used to identify the individual mutants carrying each mutation. Using this approach, we showed that 28 of the 40 rtp mutants carried homozygous nonsense, missense or splice site mutations in one or more of the 47 known or candidate genes. We conducted complementation tests to demonstrate that several of the candidate mutations were responsible for the rtp defect. Importantly, by exclusion, the Can-Seq pipeline also identified rtp mutants that did not carry a causative mutation in any of the 47 known and candidate genes, and these mutants represent an undiscovered gene(s) required for systemic RNAi.ConclusionsCan-Seq offers an accurate, cost-effective method for classifying new mutants into known versus unknown genes. It has several advantages over existing genetic and DNA sequencing approaches that are currently being used in forward genetic screens for gene discovery. Using Can-Seq in conjunction with map-based gene cloning is a cost-effective approach towards identifying the full complement of genes contributing to a trait of interest.

Project description:BackgroundThe analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.ResultsHere, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.ConclusionsThe methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

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Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing.

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Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing.

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