KnotAli: informed energy minimization through the use of evolutionary information.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35505276", "kind": "science-record", "revision": 3, "content_hash": "sha256:bbf0d8831d21a80c1a2ff92571aefd8bd181ebdab8542daea828b64b28c5f08e", "created_at": "2026-11-01T18:14:23Z", "updated_at": "2026-11-01T21:27:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "KnotAli: informed energy minimization through the use of evolutionary information.", "doi": "10.1186/s12859-022-04673-3", "pmid": "35505276", "authors": ["Mateo Gray", "Sean Chester", "Hosna Jabbari"], "journal": "BMC bioinformatics", "year": 2022, "abstract": "BACKGROUND: Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters.\nRESULTS: We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli's performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT).\nCONCLUSIONS: We found KnotAli's performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli's predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: https://doi.org/10.5281/zenodo.5794719.", "claims": [{"id": "claim-method", "statement": "We consider the performance of an algorithm to be superior or inferior to another one if the difference in their accuracy is considered significant based on a two-sided permutation test [25, 63].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35505276", "locator": "sections[15] \"Significance test\" paragraph 2", "quote": "We consider the performance of an algorithm to be superior or inferior to another one if the difference in their accuracy is considered significant based on a two-sided permutation test [25, 63]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The source code and datasets generated and/or analysed during the current study are available at the Zenodo image: 10.5281/zenodo.5794719.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35505276", "locator": "sections[30] \"Availability of data and materials\" paragraph 2", "quote": "The source code and datasets generated and/or analysed during the current study are available at the Zenodo image: 10.5281/zenodo.5794719."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We found KnotAli’s performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35505276", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We found KnotAli’s performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We found KnotAli’s performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35505276"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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