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Abstract Today, due to the size of many genomes and the increasingly large sizes of sequencing files, independently analyzing sequencing data is largely impossible for a biologist with little to no programming expertise. As such, biologists are typically faced with the dilemma of either having to spend a significant amount of time and effort to learn how to program themselves or having to identify (and rely on) an available computer scientist to analyze large sequence data sets. That said, the advent of AI‐powered programs like ChatGPT may offer a means of circumventing the disconnect between biologists and their analysis of genomic data critically important to their field. The work detailed herein demonstrates how implementing ChatGPT into an existing Course‐based Undergraduate Research Experience curriculum can provide a means for equipping biology students with no programming expertise the power to generate their own programs and allow those students to carry out a publishable, comprehensive analysis of real‐world Next Generation Sequencing (NGS) datasets. Relying solely on the students' biology background as a prompt for directing ChatGPT to generate Python codes, we found students could readily generate programs able to deal with and analyze NGS datasets greater than 10 gigabytes. In summary, we believe that integrating ChatGPT into education can help bridge a critical gap between biology and computer science and may prove similarly beneficial in other disciplines. Additionally, ChatGPT can provide biological researchers with powerful new tools capable of mediating NGS dataset analysis to help accelerate major new advances in the field. 

The excision of specific tRNA-derived small RNAs (tsRNAs), yRNA-derived small RNAs (ysRNAs) and ribosomal RNA-derived small RNAs (rsRNAs) is now well established. Several reports have suggested many of these fragments function much like traditional microRNAs (miRNAs). That said, whereas the expressions of the majority of appreciably expressed miRNAs in HCT116 colon cancer cells are significantly decreased in individual knockouts (KOs) of DROSHA, DGCR8, XPO5, and DICER, on average, only 3.5% of tsRNA, ysRNA, and rsRNA expressions are impaired. Conversely, tsRNA, ysRNA, and rsRNA expressions are significantly increased in each of these KOs as compared to WT. As such, although DICER has been suggested to be involved with the expression of specific tsRNAs, ysRNAs, and rsRNAs, our study finds no evidence supporting the involvement of any of these canonical miRNA biogenesis enzymes in their expressions. 

SARS-CoV-2 infection can result in a range of outcomes from asymptomatic/mild disease to severe COVID-19/fatality. In this study, we investigated the differential expression of small noncoding RNAs (sncRNAs) between patient cohorts defined by disease severity. We collected plasma samples, stratified these based on clinical outcomes, and sequenced their circulating sncRNAs. Excitingly, we found YRNA HY4 displays significant differential expression (p=0.025) between patients experiencing mild and severe disease. In agreement with recent reports identifying plasma YRNAs as indicators of influenza infection severity, our results strongly suggest that circulating HY4 levels represent a powerful prognostic indicator of likely SARS-CoV-2 patient infection outcome.