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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. 

Abstract Several studies have now described instances where G-rich sequences in promoters and enhancers regulate gene expression through forming G-quadruplex (G4) structures. Relatedly, our group recently identified 301 long genomic stretches significantly enriched for minimal G4 motifs (LG4s) in humans and found the majority of these overlap annotated enhancers, and furthermore, that the promoters regulated by these LG4 enhancers are similarly enriched with G4-capable sequences. While the generally accepted model for enhancer:promoter specificity maintains that interactions are dictated by enhancer- and promoter-bound transcriptional activator proteins, the current study tested an alternative hypothesis: that LG4 enhancers interact with cognate promoters via a direct G4:G4 DNA-based mechanism. This work establishes the nuclear proximity of LG4 enhancer:promoter pairs, biochemically demonstrates the ability of individual LG4 single-stranded DNAs (ssDNAs) to directly interact target promoter ssDNAs, and confirms that these interactions, as well as the ability of LG4 enhancers to activate target promoters in culture, are mediated by G4 DNA. 

Small RNAs (sRNAs) are short noncoding RNAs of ~50-200 nucleotides believed to primarily function in regulating crucial activities in bacteria during periods of cellular stress. This study examined the relevance of specific sRNAs on biofilm formation in nutrient starved Salmonella enterica serovar Typhimurium. Eight unique sRNAs were selected for deletion primarily based on their genomic location and/or putative targets. Quantitative and qualitative analyses confirm one of these, sRNA1186573, is required for efficient biofilm formation in S. enterica further highlighting the significance of sRNAs during Salmonella stress response.