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Computational Simulation of Adapter Length-Dependent LASSO Probe Capture Efficiency.

ABSTRACT: Multiplexed cloning of long DNA sequences is a valuable technique in many biotechnology applications, such as long-read genome sequencing and the creation of open reading frame (ORF) libraries. Long-adapter single-stranded oligonucleotide (LASSO) probes have shown promise as a tool to clone long DNA fragments. LASSO probes are molecular inversion probes (MIP) engineered with an adapter region of user-defined length, flanked between template-specific probe sequences. Herein, we demonstrate that the adapter length is a key feature of LASSO that influences the efficiency of gene capture and cloning. Furthermore, we applied a model based on Monte Carlo molecular simulation in order to study the relationship between the long-adapter length of LASSO and capture enrichment. Our results suggest that the adapter length is a factor that contributes to the free energy of target-probe interaction, thereby determining the efficiency of capture. The results indicate that LASSOs with extremely long adapters cannot capture the targets well. They also suggest that targets of different lengths may prefer adapters of different lengths.

SUBMITTER: Liu J

PROVIDER: S-EPMC6571849 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Computational Simulation of Adapter Length-Dependent LASSO Probe Capture Efficiency.

Liu Jingqian J Shukor Syukri S Li Shuxiang S Tamayo Alfred A Tosi Lorenzo L Larman Benjamin B Nanda Vikas V Olson Wilma K WK Parekkadan Biju B

Biomolecules 20190522 5

Multiplexed cloning of long DNA sequences is a valuable technique in many biotechnology applications, such as long-read genome sequencing and the creation of open reading frame (ORF) libraries. Long-adapter single-stranded oligonucleotide (LASSO) probes have shown promise as a tool to clone long DNA fragments. LASSO probes are molecular inversion probes (MIP) engineered with an adapter region of user-defined length, flanked between template-specific probe sequences. Herein, we demonstrate that t ...[more]

PMID: 31121947

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Project description:BackgroundLong Adapter Single-Stranded Oligonucleotide (LASSO) probes were developed as a novel tool for massively parallel cloning of kilobase-long genomic DNA sequences. LASSO dramatically improves the capture length limit of current DNA padlock probe technology from approximately 150 bps to several kbps. High-throughput LASSO capture involves the parallel assembly of thousands of probes. However, malformed probes are indiscernible from properly formed probes using gel electrophoretic techniques. Therefore, we used next-generation sequencing (NGS) to assess the efficiency of LASSO probe assembly and how it relates to the nature of DNA capture and amplification. Additionally, we introduce a simplified single target LASSO protocol using classic molecular biology techniques for qualitative and quantitative assessment of probe specificity.ResultsA LASSO probe library targeting 3164 unique E. coli ORFs was assembled using two different probe assembly reaction conditions with a 40-fold difference in DNA concentration. Unique probe sequences are located within the first 50 bps of the 5' and 3' ends, therefore we used paired-end NGS to assess probe library quality. Properly mapped read pairs, representing correctly formed probes, accounted for 10.81 and 0.65% of total reads, corresponding to ~ 80% and ~ 20% coverage of the total probe library for the lower and higher DNA concentration conditions, respectively. Subsequently, we used single-end NGS to correlate probe assembly efficiency and capture quality. Significant enrichment of LASSO targets over non-targets was only observed for captures done using probes assembled with a lower DNA concentration. Additionally, semi-quantitative polyacrylamide gel electrophoresis revealed a ~ 10-fold signal-to-noise ratio of LASSO capture in a simplified system.ConclusionsThese results suggest that LASSO probe coverage for target sequences is more predictive of successful capture than probe assembly depth-enrichment. Concomitantly, these results demonstrate that DNA concentration at a critical step in the probe assembly reaction significantly impacts probe formation. Additionally, we show that a simplified LASSO capture protocol coupled to PAGE (polyacrylamide gel electrophoresis) is highly specific and more amenable to small-scale LASSO approaches, such as screening novel probes and templates.

Project description:BACKGROUND: Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Bühlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models. METHODS: We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index.We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer. RESULTS: Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms. CONCLUSIONS: Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes.

Dataset Information

Computational Simulation of Adapter Length-Dependent LASSO Probe Capture Efficiency.

Publications

Computational Simulation of Adapter Length-Dependent LASSO Probe Capture Efficiency.

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