在果蝇3'非翻译区发现非编码RNA元件。

Q4 Health Professions
Cuncong Zhong, Justen Andrews, Shaojie Zhang
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引用次数: 1

摘要

已知3'非翻译区(3'- utr)中的非编码RNA (ncRNA)元件参与基因的转录后调控。通过聚类这些3'-UTR ncRNA元件推断基因的共表达模式将为研究它们的生物学功能提供宝贵的见解。在本文中,我们提出了一个改进的RNA结构聚类管道。新管道在Rfam数据上的基准测试表明,与传统的分层聚类管道相比,性能提高了10%以上。通过将新的聚类管道应用于果蝇基因组的3'- utr,我们成功地鉴定了184个ncRNA簇,准确率为91.3%。其中一个簇对应于雄性果蝇优先表达的基因。另一簇包含负责上皮细胞分隔连接功能的基因。这些发现鼓励对新的转录后调控机制进行更多的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discovering non-coding RNA elements in Drosophila 3' untranslated regions.

Discovering non-coding RNA elements in Drosophila 3' untranslated regions.

Discovering non-coding RNA elements in Drosophila 3' untranslated regions.

Discovering non-coding RNA elements in Drosophila 3' untranslated regions.

The Non-Coding RNA (ncRNA) elements in the 3' Untranslated Regions (3'-UTRs) are known to participate in the genes' post-transcriptional regulations. Inferring co-expression patterns of the genes through clustering these 3'-UTR ncRNA elements will provide invaluable insights for studying their biological functions. In this paper, we propose an improved RNA structural clustering pipeline. Benchmark of the new pipeline on Rfam data demonstrates over 10% performance improvements compared to the traditional hierarchical clustering pipeline. By applying the new clustering pipeline to 3'-UTRs of Drosophila melanogaster's genome, we have successfully identified 184 ncRNA clusters with 91.3% accuracy. One of these clusters corresponds to genes that are preferentially expressed in male Drosophila. Another cluster contains genes that are responsible for the functions of septate junction in epithelial cells. These discoveries encourage more studies on novel post-transcriptional regulation mechanisms.

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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