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<p>Understanding the dark genome is a priority task following the complete sequencing of the human genome. Short open reading frames (sORFs) are a group of largely unexplored elements of the dark genome with the potential for being translated into microproteins. The definitive number of coding and regulatory sORFs is not known, however they could account for up to 1–2% of the human genome. This corresponds to an order of magnitude in the range of canonical coding genes. For a few sORFs a clinical relevance has already been demonstrated, but for the majority of potential sORFs the biological function remains unclear. A major limitation in predicting their disease relevance using large-scale genomic data is the fact that no population-level constraint metrics for genetic variants in sORFs are yet available. To overcome this, we used the recently released gnomAD 4.0 dataset and analyzed the constraint of a consensus set of sORFs and their genomic neighbors. We demonstrate that sORFs are mostly embedded into a moderately constrained genomic context, but within the gencode dataset we identified a subset of highly constrained sORFs comparable to highly constrained canonical genes.</p>

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Mutational constraint analysis workflow for overlapping short open reading frames and genomic neighbors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月14日

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  • Martin Danner,
  • Matthias Begemann,
  • Florian Kraft,
  • Miriam Elbracht,
  • Ingo Kurth,
  • Jeremias Krause

摘要

Understanding the dark genome is a priority task following the complete sequencing of the human genome. Short open reading frames (sORFs) are a group of largely unexplored elements of the dark genome with the potential for being translated into microproteins. The definitive number of coding and regulatory sORFs is not known, however they could account for up to 1–2% of the human genome. This corresponds to an order of magnitude in the range of canonical coding genes. For a few sORFs a clinical relevance has already been demonstrated, but for the majority of potential sORFs the biological function remains unclear. A major limitation in predicting their disease relevance using large-scale genomic data is the fact that no population-level constraint metrics for genetic variants in sORFs are yet available. To overcome this, we used the recently released gnomAD 4.0 dataset and analyzed the constraint of a consensus set of sORFs and their genomic neighbors. We demonstrate that sORFs are mostly embedded into a moderately constrained genomic context, but within the gencode dataset we identified a subset of highly constrained sORFs comparable to highly constrained canonical genes.