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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

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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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Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Master Transcription Regulators02:23

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
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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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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences.

Raphaël Mourad1,2

  • 1MIAT, INRAE, 31320, Castanet-Tolosan, France. raphael.mourad@univ-tlse3.fr.

BMC Bioinformatics
|May 5, 2023
PubMed
Summary

Semi-supervised learning enhances the prediction of molecular processes from DNA sequences by utilizing both labeled and unlabeled data. This approach overcomes limitations of traditional supervised learning for non-coding single nucleotide polymorphisms (SNPs).

Keywords:
Deep learningGraph neural networkRegulatory genomicsSemi-supervised learning

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Machine Learning in Biology

Background:

  • Genome-wide association studies (GWAS) identify numerous single nucleotide polymorphisms (SNPs) linked to complex diseases.
  • Most disease-associated SNPs reside in non-coding regions, hindering mechanistic understanding.
  • Deep learning, particularly supervised learning, has advanced regulatory sequence prediction but is limited by scarce functional genomic data.

Purpose of the Study:

  • To address the data limitations of supervised learning in predicting molecular functions of non-coding genomic regions.
  • To propose and evaluate a semi-supervised learning approach for regulatory sequence prediction.
  • To leverage large amounts of unlabeled DNA sequences, including cross-species data, to improve predictive performance.

Main Methods:

  • Implementation of a semi-supervised learning framework applicable to various neural network architectures (shallow and deep).
  • Integration of both labeled (e.g., human ChIP-seq) and unlabeled (e.g., chimpanzee DNA) genomic sequences for model training.
  • The methodology is available at: https://forgemia.inra.fr/raphael.mourad/deepgnn

Main Results:

  • Semi-supervised learning significantly improves predictive performance compared to purely supervised methods, achieving up to a [Formula: see text] increase.
  • The proposed approach effectively utilizes vast amounts of unlabeled mammalian DNA sequences.
  • Demonstrated flexibility by integrating with diverse neural network architectures.

Conclusions:

  • Semi-supervised learning offers a powerful alternative to overcome data scarcity in functional genomics prediction.
  • This paradigm shift enables more accurate understanding of non-coding SNPs' roles in complex diseases.
  • The developed method provides a scalable and effective solution for regulatory sequence prediction using abundant unlabeled genomic data.