REDalign: accurate RNA structural alignment using residual encoder-decoder network
Abstract Background RNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural al...
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2024-11-01
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| Online Access: | https://doi.org/10.1186/s12859-024-05956-7 |
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| author | Chun-Chi Chen Yi-Ming Chan Hyundoo Jeong |
| author_facet | Chun-Chi Chen Yi-Ming Chan Hyundoo Jeong |
| author_sort | Chun-Chi Chen |
| collection | DOAJ |
| description | Abstract Background RNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of $$O(L^6)$$ O ( L 6 ) for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities. Results In this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency. Conclusion REDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics. |
| format | Article |
| id | doaj-art-edc36e75fedd42a1a9ba86348eae56e8 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-edc36e75fedd42a1a9ba86348eae56e82024-11-10T12:45:14ZengBMCBMC Bioinformatics1471-21052024-11-0125111610.1186/s12859-024-05956-7REDalign: accurate RNA structural alignment using residual encoder-decoder networkChun-Chi Chen0Yi-Ming Chan1Hyundoo Jeong2Department of Electrical Engineering, National Chiayi UniversityMindtronicAI Co.Biomedical and Robotics Engineering, Incheon National UniversityAbstract Background RNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of $$O(L^6)$$ O ( L 6 ) for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities. Results In this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency. Conclusion REDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.https://doi.org/10.1186/s12859-024-05956-7RNA secondary structureStructural alignmentPseudoknot structureDeep learningResidual encoder decoder network |
| spellingShingle | Chun-Chi Chen Yi-Ming Chan Hyundoo Jeong REDalign: accurate RNA structural alignment using residual encoder-decoder network BMC Bioinformatics RNA secondary structure Structural alignment Pseudoknot structure Deep learning Residual encoder decoder network |
| title | REDalign: accurate RNA structural alignment using residual encoder-decoder network |
| title_full | REDalign: accurate RNA structural alignment using residual encoder-decoder network |
| title_fullStr | REDalign: accurate RNA structural alignment using residual encoder-decoder network |
| title_full_unstemmed | REDalign: accurate RNA structural alignment using residual encoder-decoder network |
| title_short | REDalign: accurate RNA structural alignment using residual encoder-decoder network |
| title_sort | redalign accurate rna structural alignment using residual encoder decoder network |
| topic | RNA secondary structure Structural alignment Pseudoknot structure Deep learning Residual encoder decoder network |
| url | https://doi.org/10.1186/s12859-024-05956-7 |
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