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Tekinsoy, M.

Publications and source records attributed to Tekinsoy, M..

2 recordsLinked to original sources

Optimal sequencing depth for measuring the concentrations of molecular barcodes

In combinatorial genetic engineering experiments, next-generation sequencing (NGS) allows for measuring the concentrations of barcoded or mutated genes within highly diverse libraries. When designing and interpreting these experiments, sequencing depths are thus important parameters to take into account. Service providers follow established guidelines to determine NGS depth depending on the type of experiment, such as RNA sequencing or whole genome sequencing. However, guidelines specifically tailored for measuring barcode concentrations have not yet reached an accepted consensus. To address this issue, we combine the analysis of NGS datasets from barcoded libraries with a mathematical model taking into account the PCR amplification in library preparation. We demonstrate on several datasets that noise in the NGS counts increases with the sequencing depth; consequently, beyond certain limits, deeper sequencing does not improve the precision of measuring barcode concentrations. We propose, as rule of thumb, that the optimal sequencing depth should be about ten times the initial amount of barcoded DNA molecules before any amplification step.

bioinformatics↗

In-silico monitoring of directed evolution convergence to unveil best performing variants with credibility score

Directed evolution (DE) is a versatile protein-engineering strategy, successfully applied to a range of proteins, including enzymes, antibodies, and viral vectors. However, DE can be time-consuming and costly, as it typically requires many rounds of selection to identify desired mutants. Next-generation sequencing allows monitoring of millions of variants during DE and can be leveraged to reduce the number of selection rounds. Unfortunately the noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in DE by providing accurate statistical scores. We tested ACIDES first on a novel random-peptide-insertion experiment and then on several public datasets from DE of viral vectors and phage-display. ACIDES allows experimentalists to reliably estimate variant performance on the fly and can aid protein engineering pipelines in a range of applications, including gene therapy.

bioinformatics↗