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Related Concept Videos

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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Related Experiment Video

Updated: Dec 5, 2025

CRISPR-Cas9-based Genome Engineering to Generate Jurkat Reporter Models for HIV-1 Infection with Selected Proviral Integration Sites
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DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Rui Tian1, Ping Zhou2, Mengyuan Li3

  • 1Translational Medicine of the First Affiliated Hospital, Sun Yat-sen University.

Briefings in Bioinformatics
|October 15, 2020
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Summary

Human papillomavirus (HPV) integration sites, crucial for cervical cancer, can now be predicted using the DeepHPV deep learning model. This tool analyzes genomic environments to identify integration preferences and mechanisms.

Keywords:
HPV integrationTF motifsdeep learning

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Area of Science:

  • Genomics and Bioinformatics
  • Oncology
  • Molecular Biology

Background:

  • Human papillomavirus (HPV) integration into the human genome is a primary driver of cervical carcinogenesis.
  • HPV integration site selection is strongly influenced by the local genomic environment, suggesting predictability.
  • A lack of publicly available bioinformatic tools hinders the prediction of HPV integration sites.

Purpose of the Study:

  • To develop an attention-based deep learning model, DeepHPV, for predicting HPV integration sites.
  • To automatically learn and leverage genomic environment features for accurate prediction.
  • To provide insights into the mechanisms and preferences of HPV integration.

Main Methods:

  • Trained an attention-based deep learning model (DeepHPV) using 3608 known HPV integration sites.
  • Utilized a testing dataset of 584 reviewed HPV integration sites.
  • Enhanced model performance by incorporating RepeatMasker and TCGA Pan Cancer peak data; analyzed attention mechanisms and enriched transcription factor binding sites.

Main Results:

  • The baseline DeepHPV model achieved an AUROC of 0.6336 and AUPR of 0.5670.
  • Incorporating RepeatMasker and TCGA Pan Cancer peaks significantly improved performance (AUROC up to 0.8501, AUPR up to 0.8106).
  • The model incorporating TCGA Pan Cancer data showed superior performance on an independent database (VISDB) compared to the RepeatMasker-enhanced model.
  • Attention analysis identified enriched transcription factor binding sites near integration hotspots.

Conclusions:

  • DeepHPV is a robust and explainable deep learning tool for predicting HPV integration sites.
  • The model offers new insights into the preference and mechanism of HPV integration.
  • DeepHPV is available as open-source software, facilitating further research in cervical cancer.