sRNAdeep:基于 DistilBERT 编码模式和深度学习算法的细菌 sRNA 预测新工具。

IF 3.5 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY BMC Genomics Pub Date : 2024-10-31 DOI:10.1186/s12864-024-10951-6
Weiye Qian, Jiawei Sun, Tianyi Liu, Zhiyuan Yang, Stephen Kwok-Wing Tsui
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引用次数: 0

摘要

背景:细菌小调控 RNA(sRNA)在细胞代谢中起着至关重要的作用,可作为治疗病原体引起的疾病的潜在新药靶点。然而,鉴定 sRNA 的实验方法仍然需要投入大量的人力和物力:本研究基于 DistilBERT 特征提取和 TextCNN 方法,提出了一种新型 sRNA 预测模型 sRNAdeep。将细菌的sRNA和非sRNA序列视为句子,然后将其输入由深度学习模型组成的复合模型,以评估分类性能:通过过滤 BSRD 数据库中的 sRNA,我们得到了由 2438 个阳性样本和 4730 个阴性样本组成的验证数据集。基准实验结果表明,与之前的sRNA预测工具相比,sRNAdeep在各项指标上都有更好的表现。通过将我们的工具应用于结核分枝杆菌(MTB)基因组,我们在基因间区和内含子区发现了 21 个 sRNA。我们还在 MTB 中捕获了一组受这些 sRNA 调控的 272 个目标基因。两个基因(lysX 和 icd1)的编码蛋白与药物反应有关,其重要活性位点与 MTB 的耐药机制相关:总之,我们新开发的 sRNAdeep 可以帮助研究人员更精确地识别细菌 sRNA,并可从 https://github.com/pyajagod/sRNAdeep.git 免费获取。
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sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms.

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 .

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来源期刊
BMC Genomics
BMC Genomics 生物-生物工程与应用微生物
CiteScore
7.40
自引率
4.50%
发文量
769
审稿时长
6.4 months
期刊介绍: BMC Genomics is an open access, peer-reviewed journal that considers articles on all aspects of genome-scale analysis, functional genomics, and proteomics. BMC Genomics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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