Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Summary: TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.

Availability: http://homepages.inf.ed.ac.uk/gsanguin/TFInfer.html

Contact: gsanguin@staffmail.ed.ac.uk

Supplementary Information: Supplementary data are available at Bioinformatics online.

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btq469DOI Listing

Publication Analysis

Top Keywords

inference transcription
8
transcription factor
8
factor activities
8
tfinfer tool
4
tool probabilistic
4
probabilistic inference
4
activities summary
4
summary tfinfer
4
tfinfer novel
4
novel open
4

Similar Publications

In most eubacteria the initiator protein DnaA triggers chromosomal replication by forming an initiation complex at the origin of replication and also functions as a transcriptional regulator, coordinating gene expression with cell cycle progression. While DnaA-regulated genes are relatively well characterized in exponentially growing cells, its role in gene regulation during stationary phase remains insufficiently explored. Here, using an aquatic bacterium Caulobacter crescentus as a model, we show that C.

View Article and Find Full Text PDF

InterVelo: A Mutually Enhancing Model for Estimating Pseudotime and RNA Velocity in Multi-Omic Single-Cell Data.

Bioinformatics

September 2025

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

Motivation: RNA velocity has become a powerful tool for uncovering transcriptional dynamics in snapshot single-cell data. However, current RNA velocity approaches often assume constant transcriptional rates and treat genes independently with gene-specific times, which may introduce biases and deviate from biological realities. Here, we present InterVelo, a novel deep learning framework that simultaneously learns cellular pseudotime and RNA velocity.

View Article and Find Full Text PDF

Spatial transcriptomics (ST) reveals gene expression distributions within tissues. Yet, predicting spatial gene expression from histological images still faces the challenges of limited ST data that lack prior knowledge, and insufficient capturing of inter-slice heterogeneity and intra-slice complexity. To tackle these challenges, we introduce FmH2ST, a foundation model-based method for spatial gene expression prediction.

View Article and Find Full Text PDF

CDK7 has emerged as a cancer target because of its pivotal roles in cell cycle progression and transcription. Several CDK7 inhibitors (CDK7i) are now in clinical evaluation. Identifying patients most likely to respond to treatment and early detection of tumour evolution towards resistance are necessary for optimal implementation of cancer therapies.

View Article and Find Full Text PDF

ClinicSum: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations.

Proc IEEE Int Conf Big Data

December 2024

Dept. of Computer Science and Engineering, Mississippi State University Potentia Analytics Inc.; Dave C. Swalm School of Chemical Engineering, Mississippi State University.

This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, Objective, Assessment, and Plan (SOAP) information from conversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs), which leverage the extracted SOAP data to generate abstracted clinical summaries. To fine-tune the PLM, we created a training dataset of consisting 1,473 conversations-summaries pair by consolidating two publicly available datasets, FigShare and MTS-Dialog, with ground truth summaries validated by Subject Matter Experts (SMEs).

View Article and Find Full Text PDF