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Related Concept Videos

Cis-regulatory Sequences02:02

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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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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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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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ChromDL: a next-generation regulatory DNA classifier.

Christopher Hill1,2, Sanjarbek Hudaiberdiev1, Ivan Ovcharenko1

  • 1Computational Biology Branch, Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD 20892, United States.

Bioinformatics (Oxford, England)
|June 30, 2023
PubMed
Summary
This summary is machine-generated.

ChromDL, a novel deep learning model, accurately predicts gene regulatory elements by analyzing DNA sequences. This advancement in genomics aids in understanding transcription factor binding and motif specificities.

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Area of Science:

  • Genomics
  • Computational Biology
  • Machine Learning

Background:

  • Predicting the regulatory function of non-coding DNA solely from sequence data presents a significant challenge in genomics.
  • Advancements in optimization algorithms, GPU speed, and machine learning libraries enable the development of sophisticated neural network architectures.

Purpose of the Study:

  • To develop a deep learning model that accurately predicts regulatory elements in non-coding DNA.
  • To improve upon existing methods for predicting transcription factor binding sites, histone modifications, and DNase-I hypersensitive sites.

Main Methods:

  • A comparative analysis of thousands of deep learning architectures was performed.
  • ChromDL was developed, integrating bidirectional gated recurrent units, convolutional neural networks, and bidirectional long short-term memory units.
  • A secondary model was employed for gene regulatory element classification.

Main Results:

  • ChromDL significantly enhances prediction metrics for transcription factor binding sites, histone modifications, and DNase-I hypersensitive sites compared to previous models.
  • The model demonstrates improved detection of weak transcription factor binding.
  • ChromDL facilitates accurate classification of gene regulatory elements and aids in delineating transcription factor binding motif specificities.

Conclusions:

  • ChromDL represents a significant advancement in predicting the regulatory function of non-coding DNA.
  • The model's ability to detect weak transcription factor binding and classify regulatory elements holds potential for future genomic research.
  • The source code for ChromDL is publicly available, promoting further research and development.