Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

ABSTRACT: The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data from hundreds of TFs and embedding over 1 M DNA sequences, BindSpace achieves state-of-the-art multiclass binding prediction performance, in vitro and in vivo, and can distinguish between signals of closely related TFs.

SUBMITTER: Yuan H

PROVIDER: S-EPMC6717532 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

Yuan Han H Kshirsagar Meghana M Zamparo Lee L Lu Yuheng Y Leslie Christina S CS

Nature methods 20190812 9

The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data from hundreds of TFs and embedding over 1 M DNA sequences, BindSpace achieves state-of-the-art multiclass binding prediction performance, in vitro and in vivo, and can distinguish between signals of closely related TFs. ...[more]

PMID: 31406384

Similar Datasets

Large-scale turnover of functional transcription factor binding sites in Drosophila.

Project description:The gain and loss of functional transcription factor binding sites has been proposed as a major source of evolutionary change in cis-regulatory DNA and gene expression. We have developed an evolutionary model to study binding-site turnover that uses multiple sequence alignments to assess the evolutionary constraint on individual binding sites, and to map gain and loss events along a phylogenetic tree. We apply this model to study the evolutionary dynamics of binding sites of the Drosophila melanogaster transcription factor Zeste, using genome-wide in vivo (ChIP-chip) binding data to identify functional Zeste binding sites, and the genome sequences of D. melanogaster, D. simulans, D. erecta, and D. yakuba to study their evolution. We estimate that more than 5% of functional Zeste binding sites in D. melanogaster were gained along the D. melanogaster lineage or lost along one of the other lineages. We find that Zeste-bound regions have a reduced rate of binding-site loss and an increased rate of binding-site gain relative to flanking sequences. Finally, we show that binding-site gains and losses are asymmetrically distributed with respect to D. melanogaster, consistent with lineage-specific acquisition and loss of Zeste-responsive regulatory elements.

| S-EPMC1599766 | biostudies-literature

Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility.

Project description:Differential binding of transcription factors (TFs) at cis-regulatory loci drives the differentiation and function of diverse cellular lineages. Understanding the regulatory interactions that underlie cell fate decisions requires characterizing TF binding sites (TFBS) across multiple cell types and conditions. Techniques, e.g. ChIP-Seq can reveal genome-wide patterns of TF binding, but typically requires laborious and costly experiments for each TF-cell-type (TFCT) condition of interest. Chromosomal accessibility assays can connect accessible chromatin in one cell type to many TFs through sequence motif mapping. Such methods, however, rarely take into account that the genomic context preferred by each factor differs from TF to TF, and from cell type to cell type. To address the differences in TF behaviors, we developed Mocap, a method that integrates chromatin accessibility, motif scores, TF footprints, CpG/GC content, evolutionary conservation and other factors in an ensemble of TFCT-specific classifiers. We show that integration of genomic features, such as CpG islands improves TFBS prediction in some TFCT. Further, we describe a method for mapping new TFCT, for which no ChIP-seq data exists, onto our ensemble of classifiers and show that our cross-sample TFBS prediction method outperforms several previously described methods.

| S-EPMC5416775 | biostudies-literature

Large-scale identification of sequence variants influencing human transcription factor occupancy in vivo.

Project description:The function of human regulatory regions depends exquisitely on their local genomic environment and on cellular context, complicating experimental analysis of common disease- and trait-associated variants that localize within regulatory DNA. We use allelically resolved genomic DNase I footprinting data encompassing 166 individuals and 114 cell types to identify >60,000 common variants that directly influence transcription factor occupancy and regulatory DNA accessibility in vivo. The unprecedented scale of these data enables systematic analysis of the impact of sequence variation on transcription factor occupancy in vivo. We leverage this analysis to develop accurate models of variation affecting the recognition sites for diverse transcription factors and apply these models to discriminate nearly 500,000 common regulatory variants likely to affect transcription factor occupancy across the human genome. The approach and results provide a new foundation for the analysis and interpretation of noncoding variation in complete human genomes and for systems-level investigation of disease-associated variants.

| S-EPMC4666772 | biostudies-literature

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape.

Project description:MotivationAn accurate characterization of transcription factor (TF)-DNA affinity landscape is crucial to a quantitative understanding of the molecular mechanisms underpinning endogenous gene regulation. While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of TF-DNA binding affinity landscape still remains a challenging problem.ResultsHere we propose a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model which captures both position specific information and long-range dependency in the sequence. A cornerstone of our method is a novel message passing-like embedding algorithm, called Sequence2Vec, which maps these hidden Markov models into a common nonlinear feature space and uses these embedded features to build a predictive model. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA datasets which were measured by different high-throughput experimental technologies. Sequence2Vec outperforms alternative machine learning methods as well as the state-of-the-art binding affinity prediction methods.Availability and implementationOur program is freely available at https://github.com/ramzan1990/sequence2vec.Contactxin.gao@kaust.edu.sa or lsong@cc.gatech.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC5870668 | biostudies-literature

Gaussian Embedding for Large-scale Gene Set Analysis.

Project description:Gene sets, including protein complexes and signaling pathways, have proliferated greatly, in large part as a result of high-throughput biological data. Leveraging gene sets to gain insight into biological discovery requires computational methods for converting them into a useful form for available machine learning models. Here, we study the problem of embedding gene sets as compact features that are compatible with available machine learning codes. We present Set2Gaussian, a novel network-based gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space, according to the proximity of these genes in a protein-protein interaction network. We demonstrate that Set2Gaussian improves gene set member identification, accurately stratifies tumors, and finds concise gene sets for gene set enrichment analysis. We further show how Set2Gaussian allows us to identify a previously unknown clinical prognostic and predictive subnetwork around NEFM in sarcoma, which we validate in independent cohorts.

| S-EPMC7505077 | biostudies-literature

ChIP-on-chip significance analysis reveals large-scale binding and regulation by human transcription factor oncogenes.

Project description:ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed at minimizing false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Its application to human T cells, followed by extensive biochemical validation, reveals that 3 oncogenic transcription factors, NOTCH1, MYC, and HES1, bind to several thousand target gene promoters, up to an order of magnitude increase over conventional analysis methods. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the increased sensitivity reveals a combinatorial regulatory program in which MYC cobinds to virtually all NOTCH1-bound promoters. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs.

| S-EPMC2613038 | biostudies-literature

Transcription Factor Response Elements on Tip: A Sensitive Approach for Large-Scale Endogenous Transcription Factor Quantitative Identification

Project description:The ability to map endogenous transcription factors (TFs) DNA binding activity at the proteome scale will greatly enhance our understanding of various biological processes. Here we report a highly sensitive, rapid, and high-throughput approach, transcription factor response elements on tip-mass spectrometry (TOT-MS), that allows for quantitative measurement of endogenous TFs. A total of 150 TFs from 1 μg of nuclear extracts can be quantified with single shot mass spectrometry detection in 1 h of machine time. Up to 755 TFs, which is comparable to the depth of RNA-seq, were identified by TOT coupled with on-tip small size reverse-phase liquid chromatography. We further demonstrated the capability of TOT-MS by interrogating the dynamic change of TFs in the epidermal growth factor (EGF) signaling pathway. This approach should find broad applications in elucidating the TF landscape from limited amounts of biological materials.

2021-08-25 | PXD028104 |

Cellular arrays for large-scale analysis of transcription factor activity.

Project description:Identifying molecular mechanisms or therapeutic targets is typically based on large-scale cellular analysis that measures the abundance of mRNA or protein; however, abundance does not necessarily correlate with activity. We report a method for direct large-scale quantification of active pathways that employs a cellular array with parallel gene delivery of constructs that report pathway activity. The reporter constructs encode luciferase, whose expression is influenced by binding of transcription factors (TFs), which are the downstream targets of signaling pathways. Luciferase levels are quantified by bioluminescence imaging (BLI), which allows for rapid, non-invasive measurements. Activity profiles by BLI of 32 TFs were robust, consistent, and reproducible, and correlated with standard cell lysis techniques. The array identified five TFs with differential activity during phorbol-12-myristate-13-acetate (PMA)-induced differentiation of breast cancer cells. A system for rapid, large-scale, BLI quantification of pathway activity provides an enabling technology for mechanistic studies of cellular responses and processes.

| S-EPMC3022829 | biostudies-literature

Upstream binding factor association induces large-scale chromatin decondensation.

Project description:The function of upstream binding factor (UBF), an essential component of the RNA polymerase (pol) I preinitiation complex, is unclear. Recently, UBF was found distributed throughout ribosomal gene repeats rather than being restricted to promoter regions. This observation has led to the speculation that one role of UBF binding may be to induce chromatin remodeling. To directly evaluate the impact of UBF on chromatin structure, we used an in vivo assay in which UBF is targeted via a lac repressor fusion protein to a heterochromatic, amplified chromosome region containing lac operator repeats. We show that the association of UBF with this locus induces large-scale chromatin decondensation. This process does not appear to involve common remodeling complexes, including SWI/SNF and histone acetyltransferases, and is independent of histone H3 lysine 9 acetylation. However, UBF recruits the pol I-specific, TATA box-binding protein containing complex SL1 and pol I subunits. Our results suggest a working hypothesis in which the dynamic association of UBF with ribosomal DNA clusters recruits the pol I transcription machinery and maintains these loci in a transcriptionally competent configuration. These studies also provide an in vivo model simulating ribosomal DNA transactivation outside the nucleolus, allowing temporal and spatial analyses of chromatin remodeling and assembly of the pol I transcription machinery.

| S-EPMC524054 | biostudies-literature

Gene Ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort.

Project description:Transcription factors control which information in a genome becomes transcribed to produce RNAs that function in the biological systems of cells and organisms. Reliable and comprehensive information about transcription factors is invaluable for large-scale network-based studies. However, existing transcription factor knowledge bases are still lacking in well-documented functional information. Here, we provide guidelines for a curation strategy, which constitutes a robust framework for using the controlled vocabularies defined by the Gene Ontology Consortium to annotate specific DNA binding transcription factors (DbTFs) based on experimental evidence reported in literature. Our standardized protocol and workflow for annotating specific DNA binding RNA polymerase II transcription factors is designed to document high-quality and decisive evidence from valid experimental methods. Within a collaborative biocuration effort involving the user community, we are now in the process of exhaustively annotating the full repertoire of human, mouse and rat proteins that qualify as DbTFs in as much as they are experimentally documented in the biomedical literature today. The completion of this task will significantly enrich Gene Ontology-based information resources for the research community. Database URL: www.tfcheckpoint.org.

| S-EPMC3753819 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data