Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Deciphering cis-regulatory logic with 100 million synthetic promoters

ABSTRACT: Deciphering cis-regulatory logic with 100 million synthetic promoters

PROVIDER: PRJNA414104 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Deciphering cis-regulatory logic with 100 million synthetic promoters

Project description:In order to generate data suitable to decipher cis-regulatory logic, we generated ~100 million synthetic promoters (in yeast) comprised of random DNA and measured their expression by FACS (sorting into 18 bins).

2019-01-01 | GSE104878 | GEO

Deciphering cis-regulatory logic with 100 million synthetic promoters (MNase)

Project description:Deciphering cis-regulatory logic with 100 million synthetic promoters (MNase)

| PRJNA414115 | ENA

Deciphering cis-regulatory logic with 100 million synthetic promoters (MNase)

Project description:In order to test our model of gene regulation, we measured nucleosome occupancy of our random promoter library using Mnase-seq.

2019-01-01 | GSE104903 | GEO

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters.

Project description: Not available

| S-EPMC6954276 | biostudies-literature

Machine learning for discovery: deciphering RNA splicing logic

Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.

2022-10-01 | GSE200096 | GEO

Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic

Project description:Spatially varying cis-regulatory divergence in Drosophila embryos elucidates cis-regulatory logic

| PRJNA397062 | ENA

Using instrument logic to improve identifications in low stoichiometry SILAC/TMT experiments

Project description:Pulsed SILAC approaches allow measurement of protein dynamics, including protein translation and degradation. However, its use in quantifying acute changes has been limited due the low labeled peptide stoichiometry. Here, we describe the use of instrument logic to select peaks of interest via targeted mass differences (TMD) for overcoming this limitation. Comparing peptides artificially mixed at low heavy-to-light stoichiometry measured using standard data dependent acquisition with or without TMD revealed 2-3 fold increases in identification without significant loss in quantification precision for both MS2 and MS3 methods. Our benchmarked method approach increases throughput by reducing the necessary machine time. We anticipate that all pulsed SILAC measurements, if combined with TMT or not, would greatly benefit from instrument logic based approaches.

2020-05-29 | PXD018445 | Pride

Miswired enhancer logic drives translocation positive rhabdomyosarcoma

Project description:Core regularity transcription factors (CR TFs) define cell identity and lineage through an exquisitely precise and logical order during embryogenesis and development. These CR TFs regulated one another in three-dimensional space via distal enhancers that serve as logic gates embedded in their TF recognition sequences. Aberrant chromatin organization resulting in miswired circuitry of enhancer logic is a newly recognized feature in many cancers. Here, we report that PAX3-FOXO1 expression is driven by a translocated FOXO1 distal super enhancer (SE). ChIP-seq in tumors bearing rare PAX translocations implicate enhancer miswiring is a pervasive feature across all FP-RMS tumors. Therefore, our data reveal a mechanism of a translocated hijacked enhancer which disrupts the normal CR TF logic during skeletal muscle development (PAX3 to MYOD to MYOG), replacing it with an infinite loop logic that makes rhabdomyosarcoma cells unable to exit the undifferentiated proliferating stage.

2019-12-25 | GSE136799 | GEO

A cis-regulatory logic simulator.

Project description: Not available

| S-EPMC2375358 | biostudies-literature

Bradyrhizobium elkanii strain:USDA 100 | isolate:USDA 100

Project description:Bradyrhizobium elkanii USDA 100 genome sequencing

| PRJNA834281 | ENA