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Establishing Nanopore Long-Read Sequencing as a Cost-Effective Approach for Axolotl Regeneration Research

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2024-12-20

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Singer, Hani Dani. 2025. Establishing Nanopore Long-Read Sequencing as a Cost-Effective Approach for Axolotl Regeneration Research. Master's thesis, Harvard University Division of Continuing Education.

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Abstract

RNA sequencing (RNA-seq) has become a cornerstone methodology to decipher the regulation of intricate molecular pathways. The development of RNA-seq technology in the last decade has exponentially increased the scope in which researchers collect data on gene expression, yet the high cost of executing modern sequencing methodologies can be a prohibitive barrier. Oxford Nanopore Technology (ONT) has rapidly emerged as a transformative force in the field of RNA-seq by enabling low-cost long-read sequencing. The goal of this study was to investigate whether nanopore long-read sequencing can serve as a cost-effective and viable alternative to conventional RNA-seq technologies for investigating transcriptomic expression patterns in axolotl salamanders (Ambystoma mexicanum). The first aim of this study was to optimize the MinION long-read RNA-seq protocol for the axolotl model from RNA isolation to differential gene expression (DGE) analysis. The second aim was to utilize the optimized protocol within various experimental research contexts in axolotl regeneration research. The third aim was to benchmark the more robust PromethION sequencer utilizing the optimized workflow from aim one, to assess its utility for experimental applications relative to the MinION. Our study presents a novel and optimized workflow in which nanopore sequencing accurately captures transcriptomic expression patterns in the axolotl, demonstrating its potential as a viable, cost-effective alternative to traditional RNA-seq platforms in regeneration research. This study marks the first successful published execution of nanopore RNA-seq in the axolotl salamander.

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axolotl, bioinformatics, long read sequencing, nanopore, regeneration, RNA-seq, Biology, Genetics, Bioinformatics

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