RNA Secondary Structure Analysis Using RNAstructure

Ali, Sara E.; Mittal, Abhinav; Mathews, David H.

doi:10.1002/cpz1.846

Cited by 5 publications

(3 citation statements)

References 85 publications

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“…As an example, we tested the solo locus mir-128-1 , which is predicted by RNAStructure ( 46 ) to contain two G:U pairs within its distal stem region ( Fig. 4 A ).…”

Section: Resultsmentioning

confidence: 99%

Parameters of clustered suboptimal miRNA biogenesis

Shang,

Lai

2023

Proc. Natl. Acad. Sci. U.S.A.

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The nuclear cleavage of a suboptimal primary miRNA hairpin by the Drosha/DGCR8 complex (“Microprocessor”) can be enhanced by an optimal miRNA neighbor, a phenomenon termed cluster assistance. Several features and biological impacts of this new layer of miRNA regulation are not fully known. Here, we elucidate the parameters of cluster assistance of a suboptimal miRNA and also reveal competitive interactions amongst optimal miRNAs within a cluster. We exploit cluster assistance as a functional assay for suboptimal processing and use this to invalidate putative suboptimal substrates, as well as identify a "solo" suboptimal miRNA. Finally, we report complexity in how specific mutations might affect the biogenesis of clustered miRNAs in disease contexts. This includes how an operon context can buffer the effect of a deleterious processing variant, but reciprocally how a point mutation can have a nonautonomous effect to impair the biogenesis of a clustered, suboptimal, neighbor. These data expand our knowledge regarding regulated miRNA biogenesis in humans and represent a functional assay for empirical definition of suboptimal Microprocessor substrates.

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“…As an example, we tested the solo locus mir-128-1 , which is predicted by RNAStructure ( 46 ) to contain two G:U pairs within its distal stem region ( Fig. 4 A ).…”

Section: Resultsmentioning

confidence: 99%

Parameters of clustered suboptimal miRNA biogenesis

Shang,

Lai

2023

Proc. Natl. Acad. Sci. U.S.A.

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“…Three additional features included the length of the 5´ UTR (5´ UTR length), the length of the longest poly(A) in 5´ UTR (poly(A) length), and the distance between the longest poly(A) tract and AUG (poly(A) position). The minimum free energy (MFE) of the entire 5´ UTR along with the first 50 nt in the ORF was calculated using RNAStructure [ 50 ]. Models were trained based on MLP-NN using a dataset comprising feature values of 241 5´ UTRs from the mini library, as well as the corresponding relative GFP abundance caused by these 5´ UTRs.…”

Section: Methodsmentioning

confidence: 99%

Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model

Zeng,

Song,

Wang

et al. 2024

Microb Cell Fact

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Background The 5´ untranslated region (5´ UTR) plays a key role in regulating translation efficiency and mRNA stability, making it a favored target in genetic engineering and synthetic biology. A common feature found in the 5´ UTR is the poly-adenine (poly(A)) tract. However, the effect of 5´ UTR poly(A) on protein production remains controversial. Machine-learning models are powerful tools for explaining the complex contributions of features, but models incorporating features of 5´ UTR poly(A) are currently lacking. Thus, our goal is to construct such a model, using natural 5´ UTRs from Kluyveromyces marxianus, a promising cell factory for producing heterologous proteins. Results We constructed a mini-library consisting of 207 5´ UTRs harboring poly(A) and 34 5´ UTRs without poly(A) from K. marxianus. The effects of each 5´ UTR on the production of a GFP reporter were evaluated individually in vivo, and the resulting protein abundance spanned an approximately 450-fold range throughout. The data were used to train a multi-layer perceptron neural network (MLP-NN) model that incorporated the length and position of poly(A) as features. The model exhibited good performance in predicting protein abundance (average R2 = 0.7290). The model suggests that the length of poly(A) is negatively correlated with protein production, whereas poly(A) located between 10 and 30 nt upstream of the start codon (AUG) exhibits a weak positive effect on protein abundance. Using the model as guidance, the deletion or reduction of poly(A) upstream of 30 nt preceding AUG tended to improve the production of GFP and a feruloyl esterase. Deletions of poly(A) showed inconsistent effects on mRNA levels, suggesting that poly(A) represses protein production either with or without reducing mRNA levels. Conclusion The effects of poly(A) on protein production depend on its length and position. Integrating poly(A) features into machine-learning models improves simulation accuracy. Deleting or reducing poly(A) upstream of 30 nt preceding AUG tends to enhance protein production. This optimization strategy can be applied to enhance the yield of K. marxianus and other microbial cell factories.

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“…CeU-seq [155] 用于假尿嘧啶修饰, RiboMeth-seq [156] 和 Nm-REP-seq 最终, 在获取到高置信的修饰位点候选集后, 可基于序列特征与结构特征使用HOMER [159] 和MEME [160] 等工具识别RNA的序列模式(motif), 从而推测与带有 motif序列互作的RNA修饰蛋白(如writer, reader, eraser 等). 可使用RNAfold [161] 和RNAstructure [162] 等工具预测 RNA的二级结构(2D structure), 从而推断RNA修饰对…”

Section: 工具(如Plexy等) 另外本课题组最近更新了unclassified

计算生物学在rna研究中的应用

Chen,

Huang,

et al. 2024

Sci. Sin.-Vitae

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RNA Secondary Structure Analysis Using RNAstructure

Cited by 5 publications

References 85 publications

Parameters of clustered suboptimal miRNA biogenesis

Parameters of clustered suboptimal miRNA biogenesis

Characterization and optimization of 5´ untranslated region containing poly-adenine tracts in Kluyveromyces marxianus using machine-learning model

计算生物学在rna研究中的应用

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