Browse
Submit Data
Databases
API
Help

Dataset Information

14 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A sequence-based global map of regulatory activity for deciphering human genetics.

ABSTRACT: Epigenomic profiling has enabled large-scale identification of regulatory elements, yet we still lack a systematic mapping from any sequence or variant to regulatory activities. We address this challenge with Sei, a framework for integrating human genetics data with sequence information to discover the regulatory basis of traits and diseases. Sei learns a vocabulary of regulatory activities, called sequence classes, using a deep learning model that predicts 21,907 chromatin profiles across >1,300 cell lines and tissues. Sequence classes provide a global classification and quantification of sequence and variant effects based on diverse regulatory activities, such as cell type-specific enhancer functions. These predictions are supported by tissue-specific expression, expression quantitative trait loci and evolutionary constraint data. Furthermore, sequence classes enable characterization of the tissue-specific, regulatory architecture of complex traits and generate mechanistic hypotheses for individual regulatory pathogenic mutations. We provide Sei as a resource to elucidate the regulatory basis of human health and disease.

SUBMITTER: Chen KM

PROVIDER: S-EPMC9279145 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Deciphering human ribonucleoprotein regulatory networks.

Project description:RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP-RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules represented functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP-RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

| S-EPMC6344852 | biostudies-other

Deciphering the distinct transcriptomic and gene regulatory map in adult macaque basal ganglia cells.

Project description:BackgroundThe basal ganglia are a complex of interconnected subcortical structures located beneath the mammalian cerebral cortex. The degeneration of dopaminergic neurons in the basal ganglia is the primary pathological feature of Parkinson's disease. Due to a lack of integrated analysis of multiomics datasets across multiple basal ganglia brain regions, very little is known about the regulatory mechanisms of this area.FindingsWe utilized high-throughput transcriptomic and epigenomic analysis to profile over 270,000 single-nucleus cells to create a cellular atlas of the basal ganglia, characterizing the cellular composition of 4 regions of basal ganglia in adult macaque brain, including the striatum, substantia nigra (SN), globus pallidum, and amygdala. We found a distinct epigenetic regulation on gene expression of neuronal and nonneuronal cells across regions in basal ganglia. We identified a cluster of SN-specific astrocytes associated with neurodegenerative diseases and further explored the conserved and primate-specific transcriptomics in SN cell types across human, macaque, and mouse. Finally, we integrated our epigenetic landscape of basal ganglia cells with human disease heritability and identified a regulatory module consisting of candidate cis-regulatory elements that are specific to medium spiny neurons and associated with schizophrenia.ConclusionsIn general, our macaque basal ganglia atlas provides valuable insights into the comprehensive transcriptome and epigenome of the most important and populous cell populations in the macaque basal ganglia. We have identified 49 cell types based on transcriptomic profiles and 47 cell types based on epigenomic profiles, some of which exhibit region specificity, and characterized the molecular relationships underlying these brain regions.

| S-EPMC10716911 | biostudies-literature

A global map of human gene expression.

Project description: Not available

| S-EPMC2974261 | biostudies-literature

Epigenomic Based Identification of the Global Gene Regulatory Map of the Murine Salivary Gland

Project description:The characterization of the epigenetic landscape of mouse salivary gland epithelial cells via histone modification ChIP-Seq and whole tissue RNA-Seq Analysis

2021-06-01 | GSE145753 | GEO

A sequence-based variation map of zebrafish.

Project description:Zebrafish (Danio rerio) is a popular vertebrate model organism largely deployed using outbred laboratory animals. The nonisogenic nature of the zebrafish as a model system offers the opportunity to understand natural variations and their effect in modulating phenotype. In an effort to better characterize the range of natural variation in this model system and to complement the zebrafish reference genome project, the whole genome sequence of a wild zebrafish at 39-fold genome coverage was determined. Comparative analysis with the zebrafish reference genome revealed approximately 5.2 million single nucleotide variations and over 1.6 million insertion-deletion variations. This dataset thus represents a new catalog of genetic variations in the zebrafish genome. Further analysis revealed selective enrichment for variations in genes involved in immune function and response to the environment, suggesting genome-level adaptations to environmental niches. We also show that human disease gene orthologs in the sequenced wild zebrafish genome show a lower ratio of nonsynonymous to synonymous single nucleotide variations.

| S-EPMC3629779 | biostudies-literature

Genome sequence and global sequence variation map with 5.5 million SNPs in Chinese rhesus macaque.

Project description:BACKGROUND: Rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate animal in biomedical research. A global map of genetic variations in rhesus macaque is valuable for both evolutionary and functional studies. RESULTS: Using next-generation sequencing technology, we sequenced a Chinese rhesus macaque genome with 11.56-fold coverage. In total, 96% of the reference Indian macaque genome was covered by at least one read, and we identified 2.56 million homozygous and 2.94 million heterozygous SNPs. We also detected a total of 125,150 structural variations, of which 123,610 were deletions with a median length of 184 bp (ranging from 25 bp to 10 kb); 63% of these deletions were located in intergenic regions and 35% in intronic regions. We further annotated 5,187 and 962 nonsynonymous SNPs to the macaque orthologs of human disease and drug-target genes, respectively. Finally, we set up a genome-wide genetic variation database with the use of Gbrowse. CONCLUSIONS: Genome sequencing and construction of a global sequence variation map in Chinese rhesus macaque with the concomitant database provide applicable resources for evolutionary and biomedical research.

| S-EPMC3218825 | biostudies-literature

Deciphering the genetics of mammalian limb growth

Project description:We have used DGE-SAGE, a digital transcriptomics tool, to determine the expression profile of E14.5 mouse forelimbs and hindlimbs. The forelimb, hindlimb developmental lag combined with the analysis of these datasets allow us a better insight into the dynamics of the limb growth genetic network, in particular the characterisation of genes that are differentially expressed and are putative modulators of limb growth and or candidates for limb malformation syndromes. Conclusions: The datasets and results presented in this study allow us to extend the current knowledge of the limb development and constitute an extremely relevant resource for research into the genetics of organ growth and thus ontogenesis. DGE-SAGE expression profiles for E14.5 mouse forelimb and hindlimb

2014-01-01 | E-GEOD-41222 | biostudies-arrayexpress

Cross-species regulatory sequence activity prediction.

Project description:Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, model organisms have been less explored. Model organism genomes offer both additional training sequences and unique annotations describing tissue and cell states unavailable in humans. Here, we develop a strategy to train deep convolutional neural networks simultaneously on multiple genomes and apply it to learn sequence predictors for large compendia of human and mouse data. Training on both genomes improves gene expression prediction accuracy on held out and variant sequences. We further demonstrate a novel and powerful approach to apply mouse regulatory models to analyze human genetic variants associated with molecular phenotypes and disease. Together these techniques unleash thousands of non-human epigenetic and transcriptional profiles toward more effective investigation of how gene regulation affects human disease.

| S-EPMC7392335 | biostudies-literature

Deciphering the genetics of mammalian limb growth

2014-01-01 | GSE41222 | GEO

Global sequence features based translation initiation site prediction in human genomic sequences.

Project description:Gene prediction has been increasingly important in genome annotation due to advancements in sequencing technology. Genome annotation further helps in determining the structure and function of these genes. Translation initiation site prediction (TIS) in human genomic sequences is one of the fundamental and essential steps in gene prediction. Thus, accurate prediction of TIS in these sequences is highly desirable. Although many computational methods were developed for this problem, none of them focused on finding these sites in human genomic sequences. In this paper, a new TIS prediction method is proposed by incorporating global sequence based features. Support vector machine is used to assess the prediction power of these features. The proposed method achieved accuracy of above 90% when tested for genomic as well as cDNA sequences. The experimental results indicate that the method works well for both genomic and cDNA sequences. The method can be integrated into gene prediction system in future.

| S-EPMC7490824 | 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