Most of the experimental methods to obtain the secondary structure of RNA are flawed. The high degradability of long-stranded RNA in the in vitro experimental environment makes it challenging to crystallize it to get the structural information of lncRNA; the number of reactions involving chemical reagents varies widely, and the statistical single-base activity is not accurate enough. Therefore, the acquisition of secondary structure features of lncRNAs is still mainly achieved by computational prediction. The computational methods for predicting the secondary structure of lncRNAs mainly focus on the base sequences with relevant functional roles in lncRNAs.

Lifeasible provides a structural analysis of plant lncRNA by computational methods, including comparative sequence analysis and structural modeling, to help our customers worldwide in plant science research. Our platform is equipped with cutting-edge facilities and professional experts to support research. Here, we provide various services according to customers' demands.

Comparative Sequence Analysis

• With the help of homology structure information of RNA, RNA secondary structure can be predicted using sequence matching algorithms. The multiple sequence comparison method predicts the RNA secondary structure of unknown sequences by analyzing multiple RNA sequences with similarities and looking for conserved elements.
• We provide structural analysis of lncRNA by comparative sequence analysis, including building covariance models to implement multiple sequences collapsing comparisons and multiple sequence comparisons through random context-independent grammar models.
• In addition, we predict the RNA secondary structure by calculating the covariance of different loci in multiple sequence alignments while combining the free energy and covariance scoring methods to improve the prediction accuracy.

Structure Modeling

Fig. 1 Loop-stem distribution of known functional lncRNAs.

• The RNA secondary structure is characterized mainly by stem regions and ring structures (inner ring, convex ring, hairpin ring, multi-branched ring). RNA folding algorithms are now the most used and studied methods aimed at predicting RNA secondary structure based on thermodynamic, statistical, or probabilistic properties to obtain the secondary structure model with the lowest free energy.
• Based on the sequence characterization of plant lncRNA, we provide structural analysis of lncRNA by structure modeling, combined with thermodynamic and kinetic theories. The first part of our workflow is to segment the RNA folding phase, and the second part is to search for the optimal structure in each phase. In addition, we also provide predictive algorithms for predicting RNA structures containing pseudoknots.

Lifeasible provides cost-effective, high-quality, and hassle-free services to our customers worldwide. We provide our clients with direct access to our experts and prompt responses to their questions. If you are interested in our services or have questions, please contact us or make an online inquiry.

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For research use only, not intended for any clinical use.

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• Structural Analysis of lncRNA by Experimental Methods