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sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.

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  • معلومة اضافية
    • المصدر:
      Publisher: BioMed Central Country of Publication: England NLM ID: 100965258 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2164 (Electronic) Linking ISSN: 14712164 NLM ISO Abbreviation: BMC Genomics Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: London : BioMed Central, [2000-
    • الموضوع:
    • نبذة مختصرة :
      Background: Bacterial small regulatory RNA (sRNA) plays a crucial role in cell metabolism and could be used as a new potential drug target in the treatment of pathogen-induced disease. However, experimental methods for identifying sRNAs still require a large investment of human and material resources.
      Methods: In this study, we propose a novel sRNA prediction model called sRNAdeep based on the DistilBERT feature extraction and TextCNN methods. The sRNA and non-sRNA sequences of bacteria were considered as sentences and then fed into a composite model consisting of deep learning models to evaluate classification performance.
      Results: By filtering sRNAs from BSRD database, we obtained a validation dataset comprised of 2438 positive and 4730 negative samples. The benchmark experiments showed that sRNAdeep displayed better performance in the various indexes compared to previous sRNA prediction tools. By applying our tool to Mycobacterium tuberculosis (MTB) genome, we have identified 21 sRNAs within the intergenic and intron regions. A set of 272 targeted genes regulated by these sRNAs were also captured in MTB. The coding proteins of two genes (lysX and icd1) are implicated in drug response, with significant active sites related to drug resistance mechanisms of MTB.
      Conclusion: In conclusion, our newly developed sRNAdeep can help researchers identify bacterial sRNAs more precisely and can be freely available from https://github.com/pyajagod/sRNAdeep.git .
      (© 2024. The Author(s).)
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    • Grant Information:
      61903107 National Natural Science Foundation of China
    • Contributed Indexing:
      Keywords: Mycobacterium tuberculosis; Bacterial sRNA; Deep learning; Genome analysis
    • الرقم المعرف:
      0 (RNA, Bacterial)
      0 (RNA, Small Untranslated)
    • الموضوع:
      Date Created: 20241101 Date Completed: 20241101 Latest Revision: 20241103
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC11526673
    • الرقم المعرف:
      10.1186/s12864-024-10951-6
    • الرقم المعرف:
      39482572