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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.7K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

7.1K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
7.1K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Related Experiment Video

Updated: Jul 6, 2025

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

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Predictive analyses of regulatory sequences with EUGENe.

Adam Klie1,2, David Laub1,2, James V Talwar1,2

  • 1Department of Medicine, University of California San Diego, La Jolla, CA, USA.

Nature Computational Science
|January 4, 2024
PubMed
Summary
This summary is machine-generated.

We developed EUGENe, a FAIR toolkit for deep learning in regulatory genomics. This software streamlines genomic sequence analysis, making deep learning applications more accessible and reusable for researchers.

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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Related Experiment Videos

Last Updated: Jul 6, 2025

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Deep learning is increasingly used for studying cis-regulatory function.
  • Existing software for deep learning in regulatory genomics often lacks FAIR (findable, accessible, interoperable, reusable) principles.
  • There is a need for robust, user-friendly tools to facilitate deep learning analyses in genomics.

Purpose of the Study:

  • To introduce EUGENe, a FAIR toolkit designed for deep learning analyses of genomic sequences.
  • To provide a flexible and extensible platform for end-to-end deep learning workflows in genomics.
  • To demonstrate the utility of EUGENe through practical applications.

Main Methods:

  • EUGENE comprises modules for data handling (extracting, transforming, loading), model implementation (instantiating, training diverse architectures), and analysis (evaluation, interpretation).
  • The toolkit supports various common genomic file formats.
  • It was applied to three distinct predictive modeling tasks in genomics.

Main Results:

  • EUGENE successfully facilitates the extraction, transformation, and loading of genomic sequence data.
  • The toolkit enables the instantiation, initialization, and training of diverse deep learning model architectures.
  • EUGENE aids in evaluating and interpreting the behavior of trained models, demonstrating its utility in predictive genomics.

Conclusions:

  • EUGENE is a FAIR-compliant toolkit that addresses limitations in current deep learning software for regulatory genomics.
  • The toolkit offers a streamlined, flexible, and extensible interface for deep learning sequence analyses.
  • EUGENE aims to foster a collaborative ecosystem for deep learning applications in genomics research.