Biology subjects

Wenjie Shu

Publications and source records attributed to Wenjie Shu.

3 recordsLinked to original sources

bioRxiv2016

iFORM: incorporating Find Occurrence of Regulatory Motifs

MotivationAccurately identifying binding sites of transcription factors (TFs) is crucial to understand the mechanisms of transcriptional regulation and human disease.\n\nResultsWe present incorporating Find Occurrence of Regulatory Motifs (iFORM), an easy-to-use tool for scanning DNA sequence with TF motifs described as position weight matrices (PWMs). iFORM achieves higher accuracy and sensitivity by integrating the results from five classical motif discovery programs based on Fishers combined probability test. We have used iFORM to provide accurate results on a variety of data in the ENCODE Project and the NIH Roadmap Epigenomics Project, and has demonstrated its utility to further understand individual roles of functional elements.\n\nAvailabilityiFORM can be freely accessed athttps://github.com/wenjiegroup/iFORM.\n\nContactshuwj@bmi.ac.cn and boxc@bmi.ac.cn

Bioinformatics↗

bioRxiv2016

PEDLA: predicting enhancers with a deep learning-based algorithmic framework

Transcriptional enhancers are non-coding segments of DNA that play a central role in the spatiotemporal regulation of gene expression programs. However, systematically and precisely predicting enhancers on a genome-wide scale remain a major challenge. Although existing methods have achieved some success in enhancer prediction, they still suffer from a limited number of training samples, a simplicity of features, class-imbalanced data, and inconsistent performance across diverse cell types/tissues. Here, we developed a deep learning-based algorithmic framework named PEDLA (https://github.com/wenjiegroup/PEDLA), which can directly learn an enhancer predictor from massively heterogeneous data and generalize in ways that are mostly consistent across various cell types/tissues. We first trained PEDLA with 1,114-dimensional heterogeneous features in H1 cells, and we demonstrated that our PEDLA framework integrates diverse heterogeneous features and gives state-of-the-art performance relative to five existing methods for enhancer prediction. We further extended PEDLA to continuously learn from 22 training cell types/tissues, and the results showed that PEDLA manifested superior performance consistency in both training and independent test sets. On average, PEDLA achieved 95.0% accuracy and a 96.8% geometric mean (GM) across 22 training cell types/tissues, as well as 95.7% accuracy and a 96.8% GM across 20 independent test cell types/tissues. Together, our work illustrates the power of harnessing state-of-the-art deep learning techniques to consistently identify regulatory elements at a genome-wide scale from massively heterogeneous data across diverse cell types/tissues.

Bioinformatics↗

bioRxiv2016

Genome-wide identification and characterisation of HOT regions in the human genome

HOT (high-occupancy target) regions, which are bound by a surprisingly large number of transcription factors, are considered to be among the most intriguing findings of recent years. An improved understanding of the roles that HOT regions play in biology would be afforded by knowing the constellation of factors that constitute these domains and by identifying HOT regions across the spectrum of human cell types. We characterised and validated HOT regions in embryonic stem cells (ESCs) and produced a catalogue of HOT regions in a broad range of human cell types. We found that HOT regions are associated with genes that control and define the developmental processes of the respective cell and tissue types. We also showed evidence of the developmental persistence of HOT regions at primitive enhancers and demonstrate unique signatures of HOT regions that distinguish them from typical enhancers and super-enhancers. Finally, we performed an epigenetic analysis to reveal the dynamic epigenetic regulation of HOT regions upon H1 differentiation. Taken together, our results provide a resource for the functional exploration of HOT regions and extend our understanding of the key roles of HOT regions in development and differentiation.

Genomics↗