The arrangement (syntax) of transcription factor (TF) binding motifs is an important part of the cis-regulatory code, yet remains elusive. We introduce a deep learning model, BPNet, that uses DNA sequence to predict base-resolution chromatin immunoprecipitation (ChIP)-nexus binding profiles of pluripotency TFs. We develop interpretation tools to learn predictive motif representations and identify soft syntax rules for cooperative TF binding interactions. Strikingly, Nanog preferentially binds with helical periodicity, and TFs often cooperate in a directional manner, which we validate using clustered regularly interspaced short palindromic repeat (CRISPR)-induced point mutations. Our model represents a powerful general approach to uncover the motifs and syntax of cis-regulatory sequences in genomics data.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812996 | PMC |
| http://dx.doi.org/10.1038/s41588-021-00782-6 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging.
BMC Med Imaging
December 2024
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jiefang Avenue, Wuhan, Hubei Province, 430030, China.
Background: The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the two vaginal malformations, but in fact it's difficult to make an accurate differential diagnosis. This study is intended to assess the performance of multiple deep learning models based on ultrasonographic images for distinguishing longitudinal vaginal septum and oblique vaginal septum.
View Article and Find Full Text PDFWhole-genome DNA methylomes of tree shrew brains reveal conserved and divergent roles of DNA methylation on sex chromosome regulation.
BMC Biol
November 2024
Department of Ecology, Evolution and Marine Biology, University of California Santa Barbara, Santa Barbara, USA.
Article Synopsis
- The tree shrew (Tupaia belangeri) is being studied for its similarities to primates, particularly to understand DNA methylation and X chromosome inactivation (XCI) in their brains, using novel genome-wide methylation and transcriptomic data from their prefrontal cortex.
- The study found that female tree shrews exhibit global hypomethylation of the X chromosome compared to males, but this hypomethylation does not directly cause X chromosome gene silencing or sex-specific gene expression, although it affects the Xist gene's regulation.
- Overall, the research provides new insights into DNA methylation patterns in tree shrews and suggests that genomic CpG content may influence sex-differential
Nanopore direct RNA sequencing reveals N-methyladenosine and polyadenylation landscapes on long non-coding RNAs in Arabidopsis thaliana.
BMC Plant Biol
November 2024
School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
Background: Long non-coding RNAs (lncRNAs) play important roles in various biological processes, including stage development in plants. N-methyladenosine (mA) modification and polyadenylation are noteworthy regulatory processes that impact transcript functions by modulating their abundance. However, the specific landscapes of mA modification and polyadenylation on lncRNAs remain largely unexplored.
View Article and Find Full Text PDFA Genomic Language Model for Chimera Artifact Detection in Nanopore Direct RNA Sequencing.
bioRxiv
October 2024
Department of Urology, Northwestern University Feinberg School of Medicine, 303 E Superior St, Chicago, 60611, IL, USA.
Chimera artifacts in nanopore direct RNA sequencing (dRNA-seq) can significantly distort transcriptome analyses, yet their detection and removal remain challenging due to limitations in existing basecalling models. We present DeepChopper, a genomic language model that precisely identifies and removes adapter sequences from base-called dRNA-seq long reads at single-base resolution, operating independently of raw signal or alignment information to effectively eliminate chimeric read artifacts. By removing these artifacts, DeepChopper substantially improves the accuracy of critical downstream analyses, such as transcript annotation and gene fusion detection, thereby enhancing the reliability and utility of nanopore dRNA-seq for transcriptomics research.
View Article and Find Full Text PDFm6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.
Brief Bioinform
September 2024
Advanced Data Science Division, Research Center of Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku 153-8904, Tokyo, Japan.
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing and RNA degradation, playing an important role in a variety of diseases. To better understand the role of m6A, transcriptome-wide m6A profiling data are indispensable.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!