SUMMARY: Single-cell RNA sequencing (scRNA-seq) data analysis often involves complex iterative workflow, requiring significant expertise and time. To navigate this complexity, we have developed SCassist, an R package that leverages the power of the large language models (LLM's) to guide and enhance scRNA-seq analysis. SCassist integrates LLM's into key workflow steps, to analyze user data and provide relevant recommendations for filtering, normalization and clustering parameters. It also provides LLM guided insightful interpretations of variable features and principal components, along with cell type annotations and enrichment analysis. SCassist provides intelligent assistance using popular LLM's like Google's Gemini, OpenAI's GPT and Meta's Llama3, making scRNA-seq analysis accessible to researchers at all levels. AVAILABILITY AND IMPLEMENTATION: The SCassist package, along with the detailed tutorials, is available at GitHub. https://github.com/NIH-NEI/SCassist.

BACKGROUND: Staphylococcus aureus is a human pathogen that causes a wide variety of life-threatening infections using a large number of virulence factors. One of the major global regulators used by S. aureus is the staphylococcal accessory regulator (sarA). We have identified and characterized a new gene (modulator of sarA: msa) that modulates the expression of sarA. Genetic and functional analysis shows that msa has a global effect on gene expression in S. aureus. However, the mechanism of Msa function is still unknown. Function predictions of Msa are complicated by the fact that it does not have a homologous partner in any other organism. This work aims at predicting the structure and function of the Msa protein. RESULTS: Preliminary sequence analysis showed that Msa is a putative membrane protein. It would therefore be very difficult to purify and crystallize Msa in order to acquire structure information about this protein. We have used several computational tools to predict the physico-chemical properties, secondary structural features, topology, 3D tertiary structure, binding sites, motifs/patterns/domains and cellular location. We have built a consensus that is derived from analysis using different algorithms to predict several structural features. We confirm that Msa is a putative membrane protein with three transmembrane regions. We also predict that Msa has phosphorylation sites and binding sites suggesting functions in signal transduction. CONCLUSION: Based on our predictions we hypothesise that Msa is a novel signal transducer that might be involved in the interaction of the S. aureus with its environment.

BACKGROUND: Naturally occurring antimicrobial peptides are currently being explored as potential candidate peptide drugs. Since antimicrobial peptides are part of the innate immune system of every living organism, it is possible to discover new candidate peptides using the available genomic and proteomic data. High throughput computational techniques could also be used to virtually scan the entire peptide space for discovering out new candidate antimicrobial peptides. RESULT: We have identified a unique indexing method based on biologically distinct characteristic features of known antimicrobial peptides. Analysis of the entries in the antimicrobial peptide databases, based on our indexing method, using Fourier transformation technique revealed a distinct peak in their power spectrum. We have developed a method to mine the genomic and proteomic data, for the presence of peptides with potential antimicrobial activity, by looking for this distinct peak. We also used the Euclidean metric to rank the potential antimicrobial peptides activity. We have parallelized our method so that virtually any given protein space could be data mined, in search of antimicrobial peptides. CONCLUSION: The results show that the Fourier transform based method with the property based coding strategy could be used to scan the peptide space for discovering new potential antimicrobial peptides.