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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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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, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA.

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Summary
This summary is machine-generated.

ChromDL, a novel deep learning model, accurately predicts gene regulatory elements by analyzing DNA sequences. This advancement improves the detection of transcription factor binding and histone modifications, overcoming key genomics challenges.

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Area of Science:

  • Genomics
  • Computational Biology
  • Machine Learning

Background:

  • Predicting non-coding DNA regulatory function from sequence remains a significant challenge in genomics.
  • Advancements in optimization algorithms, GPU speeds, and machine learning libraries enable sophisticated deep learning approaches.

Approach:

  • Developed ChromDL, a hybrid neural network architecture integrating Bidirectional Gated Recurrent Units (BiGRU), Convolutional Neural Networks (CNNs), and Bidirectional Long Short-Term Memory units (BiLSTM).
  • Evaluated thousands of Deep Learning (DL) architectures through comparative analysis to optimize performance.
  • Utilized a secondary model for accurate classification of gene regulatory elements.

Key Points:

  • ChromDL significantly improves prediction metrics for transcription factor binding site (TFBS), histone modification (HM), and DNase-I hypersensitive site (DHS) detection.
  • The model demonstrates enhanced accuracy in detecting weak transcription factor (TF) binding compared to existing methods.
  • ChromDL shows potential for precise delineation of TF binding motif specificities.

Conclusions:

  • ChromDL offers a powerful new tool for deciphering the regulatory function of non-coding DNA.
  • The architecture advances the field of genomics by improving the accuracy and scope of regulatory element prediction.
  • Source code is publicly available for further research and application.