D-ORB: A Web Server to Extract Structural Features of Related But Unaligned RNA Sequences

[Display omitted] •D-ORB is a web server that simplifies the comparison of conformational landscapes and identification of native structural motifs from sets of functionally related RNA sequences (i.e., a family).•D-ORB utilizes the identified structural motifs to build a structure, a deep neural ne...

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Vydáno v:Journal of molecular biology Ročník 435; číslo 15; s. 168181
Hlavní autoři: Dupont, Mathieu J, Major, François
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier Ltd 01.08.2023
Témata:
Algorithms
Artificial intelligence
Base Sequence
covariance
Humans
Internet
molecular biology
Motif identification
Nucleic Acid Conformation
RNA
RNA - chemistry
RNA - genetics
RNA family
RNA structure
Sequence Alignment
Sequence Analysis, RNA - methods
Software
Structural composition
Motif identification
RNA family
RNA structure
Artificial intelligence
Structural composition
ISSN:0022-2836, 1089-8638, 1089-8638
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Shrnutí:[Display omitted] •D-ORB is a web server that simplifies the comparison of conformational landscapes and identification of native structural motifs from sets of functionally related RNA sequences (i.e., a family).•D-ORB utilizes the identified structural motifs to build a structure, a deep neural network classifier, and two decision trees that can be leveraged for identifying new members of the RNA family.•D-ORB's approach is distinct from other methods, as it does not require sequence alignments. Instead, it uses a novel approach that folds input sequences to extract valuable information from their conformational landscapes. This feature makes D-ORB an exceptional tool for RNA analysis and classification. Identifying the common structural elements of functionally related RNA sequences (family) is usually based on an alignment of the sequences, which is often subject to human bias and may not be accurate. The resulting covariance model (CM) provides probabilities for each base to covary with another, which allows to support evolutionarily the formation of double helical regions and possibly pseudoknots. The coexistence of alternative folds in RNA, resulting from its dynamic nature, may lead to the potential omission of motifs by CM. To overcome this limitation, we present D-ORB, a system of algorithms that identifies overrepresented motifs in the secondary conformational landscapes of a family when compared to those of unrelated sequences. The algorithms are bundled into an easy-to-use website allowing users to submit a family, and optionally provide unrelated sequences. D-ORB produces a non-pseudoknotted secondary structure based on the overrepresented motifs, a deep neural network classifier and two decision trees. When used to model an Rfam family, D-ORB fits overrepresented motifs in the corresponding Rfam structure; more than a hundred Rfam families have been modeled. The statistical approach behind D-ORB derives the structural composition of an RNA family, making it a valuable tool for analyzing and modeling it. Its easy-to-use interface and advanced algorithms make it an essential resource for researchers studying RNA structure. D-ORB is available at https://d-orb.major.iric.ca/.
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content type line 23
ISSN:0022-2836
1089-8638
1089-8638
DOI:10.1016/j.jmb.2023.168181