Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
We developed linear regression models which predict strength of transcriptional activity of promoters from their sequences. Intrinsic transcriptional strength data of 451 human promoter sequences in three cell lines (HEK293, MCF7 and 3T3), which were measured by systematic luciferase reporter gene assays, were used to build the models. The models sum up contributions of CG dinucleotide content and transcription factor binding sites (TFBSs) to transcriptional strength. We evaluated prediction accuracies of the models by cross validation tests and found that they have adequate ability for predicting transcriptional strength of promoters in spite of their simple formalization. We also evaluated statistical significance of the contributions and proposed a picture of regulatory code hidden in promoter sequences. That is, CG dinucleotide content and TFBSs mainly determine strength of transcriptional activity under ubiquitous and specific environments, respectively.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Transcription factors regulate gene expression with DNA-binding domains (DBDs) and activation domains. Despite mounting evidence to the contrary, it is frequently assumed that DBDs are solely responsible for interacting with DNA and chromatin. Here, we used single-molecule tracking of transcription factors in living cells to show that short activation domains can control the fraction of molecules bound to chromatin.
View Article and Find Full Text PDFAn Escherichia coli Nissle 1917-based live therapeutics platform with integrated phage resistance and programmable temperature sensitivity.
J Control Release
September 2025
State Key Laboratory of Bioreactor Engineering, Newworld Institute of Biotechnology, East China University of Science and Technology, Shanghai 200237, PR China. Electronic address:
Live bacterial therapeutics (LBT) represent a transformative modality for managing refractory chronic diseases. However, the absence of optimized microbial chassis systems is a significant barrier to clinical translation. To bridge this gap, we engineered Escherichia coli Nissle 1917 (EcN) into a versatile platform that meets the requirements for strain development and clinical application.
View Article and Find Full Text PDFIntelligent Design of Terminators by Coupling Prediction and Generation Models.
ACS Synth Biol
September 2025
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Terminators are specific nucleotide sequences located at the 3' end of a gene and contain transcription termination information. As a fundamental genetic regulatory element, terminators play a crucial role in the design of gene circuits. Accurately characterizing terminator strength is essential for improving the precision of gene circuit designs.
View Article and Find Full Text PDFCasemanagers Positioned as Key Advance Care Planning Conversationalists in Oncology Care: A Qualitative Interview Study on the Perspectives of Healthcare Professionals and Patients.
Cancer Med
September 2025
Radboudumc, Nijmegen, the Netherlands.
Introduction: The increasing global cancer burden and advances in treatments have extended patients' life expectancy, leading to greater prognostic uncertainty and a higher demand for palliative care. Advance care planning (ACP) is essential in this context, ensuring that patients' future care aligns with their values and preferences. Oncology case managers, being nurses with specialized training, have emerged as key ACP conversationalists.
View Article and Find Full Text PDFComparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.
Genome Biol
September 2025
College of Computer and Control Engineering, Northeast Forestry University, Harbin, 150040, China.
Background: Differences in data distribution, feature dimensions, and quality between different single-cell modalities pose challenges for clustering. Although clustering algorithms have been developed for single-cell transcriptomic or proteomic data, their performance across different omics data types and integration scenarios remains poorly investigated, which limits the selection of methods and future method development.
Results: In this study, we conduct a systematic and comparative benchmark analysis of 28 computational algorithms on 10 paired transcriptomic and proteomic datasets, evaluating their performance across various metrics in terms of clustering, peak memory, and running time.