MOTIVATION: Transcription factors (TFs) and their target genes form regulatory networks that control gene expression and influence diverse biological processes and disease outcomes. Although multiple computational methods and curated databases have been developed to identify TF-target interactions, they often require specialized expertise. Large language models (LLMs) chatbots offer a more accessible alternative for querying TF-target interactions. In this study, we benchmarked four prominent LLMs, Anthropic's Claude 3.5 Sonnet, Google's Gemini 1.0 Pro, OpenAI's GPT-4o, and Meta's Llama3 8b, using 8432 literature-curated human TF-target interactions. We examined four regulatory categories: bidirectional, ambiguous, self-regulated, and unidirectional interactions. RESULTS: Under single-turn queries, Claude 3.5 Sonnet and GPT-4o outperformed the others, with balanced accuracies reaching 50.0 ± 7.6% (GPT-4o, self-regulated) and 48.2 ± 1.0% (Claude 3.5 Sonnet, unidirectional). Zero-temperature settings generally enhanced reproducibility, and multi-turn prompting improved performance for most models, increasing Claude 3.5 Sonnet's accuracy on self-regulated pairs by 32.6%. Excluding TF-target pairs with all unknown regulation types also generally improved accuracy, with unidirectional regulation reaching near 70% balanced accuracy in some cases. We also benchmarked Anthropic's Claude 3.5 Sonnet, Google's Gemini 2.0 Flash, OpenAI's GPT-4o, and Meta's Llama3 using 5148 experimentally derived TF-target interactions. Claude 3.5 Sonnet consistently outperformed the other models across conditions. Our findings highlight that prompt engineering and strategic use of model parameters consistently influence LLM chatbots' performance on TF-target identifications. This study establishes a benchmarking framework and demonstrates the potential of pre-trained general-purpose LLMs to support regulatory biology research, especially for researchers without extensive computational expertise. AVAILABILITY AND IMPLEMENTATION: The literature-based TF-target interactions ground truth were obtained from TRRUST v2 human dataset (www.grnpedia.org/trrust). The experimental derived TF-target interactions ground truth were obtained from TFLink Home Sapiens small-scale interaction table (https://tflink.net/). Processed TF-target interactions data and the analytical pipeline has been compiled as an interactive Python notebook file and is available at https://github.com/pengpclab/LLM-TF-interactions.

OBJECTIVE: Accurate phenotyping is an essential task for researchers utilizing electronic health record (EHR)-linked biobank programs like the All of Us Research Program to study human genetics. However, little guidance is available on how to select an EHR-based phenotyping procedure that maximizes downstream statistical power. This study aims to estimate accuracy of three phenotype definitions of ovarian, female breast, and colorectal cancers in All of Us (v7 release) and determine which is most likely to optimize downstream statistical power for genetic association testing. MATERIALS AND METHODS: We used empirical carrier frequencies of deleterious variants in known risk genes to estimate the accuracy of each phenotype definition and compute statistical power after accounting for the probability of outcome misclassification. RESULTS: We found that the choice of phenotype definition can have a substantial impact on statistical power for association testing and that no approach was optimal across all tested diseases. The impact on power was particularly acute for rarer diseases and target risk alleles of moderate penetrance or low frequency. Additionally, our results suggest that the accuracy of higher-complexity phenotyping algorithms is inconsistent across Black and non-Hispanic White participants in All of Us, highlighting the potential for case ascertainment biases to impact downstream association testing. DISCUSSION: EHR-based phenotyping presents a bottleneck for maximizing power to detect novel risk alleles in All of Us, as well as a potential source of differential outcome misclassification that researchers should be aware of. We discuss the implications of this as well as potential mitigation strategies.

Competitive endogenous RNA (ceRNA) represents a novel mechanism of gene regulation that controls several biological and pathological processes. Recently, an increasing number of in silico methods have been developed to accelerate the identification of such regulatory events. However, there is still a need for a tool supporting the hypothesis that ceRNA regulatory events only occur at specific miRNA expression levels. To this end, we present an R package, ceRNAR, which allows identification and analysis of ceRNA-miRNA triplets via integration of miRNA and RNA expression data. The ceRNAR package integrates three main steps: (i) identification of ceRNA pairs based on a rank-based correlation between pairs that considers the impact of miRNA and a running sum correlation statistic, (ii) sample clustering based on gene-gene correlation by circular binary segmentation, and (iii) peak merging to identify the most relevant sample patterns. In addition, ceRNAR also provides downstream analyses of identified ceRNA-miRNA triplets, including network analysis, functional annotation, survival analysis, external validation, and integration of different tools. The performance of our proposed approach was validated through simulation studies of different scenarios. Compared with several published tools, ceRNAR was able to identify true ceRNA triplets with high sensitivity, low false-positive rates, and acceptable running time. In real data applications, the ceRNAs common to two lung cancer datasets were identified in both datasets. The bridging miRNA for one of these, the ceRNA for MAP4K3, was identified by ceRNAR as hsa-let-7c-5p. Since similar cancer subtypes do share some biological patterns, these results demonstrated that our proposed algorithm was able to identify potential ceRNA targets in real patients. In summary, ceRNAR offers a novel algorithm and a comprehensive pipeline to identify and analyze ceRNA regulation. The package is implemented in R and is available on GitHub (https://github.com/ywhsiao/ceRNAR).

RNA-Seq analysis has revolutionized researchers' understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps; however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).