{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T10:05:33Z","timestamp":1779876333616,"version":"3.53.1"},"reference-count":20,"publisher":"SAGE Publications","issue":"5-6","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000089","name":"Office of International Science and Engineering","doi-asserted-by":"crossref","award":["2212508"],"award-info":[{"award-number":["2212508"]}],"id":[{"id":"10.13039\/100000089","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ministry of Research, Innovation and Digitization, under the Romania\u00e2\u20ac\u2122s National Recovery and Resilience Plan","award":["760286"],"award-info":[{"award-number":["760286"]}]},{"DOI":"10.13039\/100000145","name":"Division of Information & Intelligent Systems","doi-asserted-by":"crossref","award":["2212508"],"award-info":[{"award-number":["2212508"]}],"id":[{"id":"10.13039\/100000145","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Biology"],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:p>Understanding epistatic interactions, where mutations collectively influence viral fitness, is critical for predicting pathogen evolution. We present a hidden Markov model (HMM) framework that captures the temporal dynamics of epistatic relationships in SARS-CoV-2, addressing limitations of static network-based approaches. Our method models single amino acid variant pairs as a two-state system (linked\/unlinked), with emission probabilities derived from linkage disequilibrium theory and transition probabilities optimized via the Baum\u2013Welch algorithm. We implement permutation-based validation with temporal order noise reduction (&gt;80% agreement across five iterations) to distinguish biological signals from stochastic noise. Applied to 2,192,008 spike protein sequences from the United States (March 2020\u2013December 2021), our approach identified three classes of epistatic dynamics: permanent (0.3%), transient (0.3%), and oscillating (0.7%) linkages. Analysis of Alpha variant positions revealed 78% epistatic linkage compared with 1.3% across all spike protein position pairs, with 60% exhibiting oscillating patterns suggestive of frequency-dependent selection. We detected all 17 previously reported epistatic pairs plus 18 novel interactions, including critical connections between positions 69\u201370 and other functional sites. Notably, Alpha variant epistatic networks were detectable as early as April 2020, months before widespread circulation. Our framework scales to variant-wide analysis, revealing distinct patterns across variants: Delta (96% linkage, 71% oscillating) and Omicron (87% linkage, 56% oscillating). The computational pipeline, implemented with parallelized HMM training and Viterbi decoding, processes hundreds of thousands of position pairs efficiently. By transforming epistasis detection from static to temporal analysis, this work provides computational tools for early variant detection and demonstrates how probabilistic modeling can capture evolutionary dynamics in real-time genomic surveillance systems.<\/jats:p>","DOI":"10.1177\/15578666261423963","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T17:05:23Z","timestamp":1771607123000},"page":"692-704","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering Epistatic Interactions in SARS-CoV-2 Evolution Through Hidden Markov Models"],"prefix":"10.1177","volume":"33","author":[{"given":"Ayotomiwa Ezekiel","family":"Adeniyi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay","family":"Juyal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pavel","family":"Skums","sequence":"additional","affiliation":[{"name":"School of Computing, University of Connecticut, Storrs, Connecticut, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murray","family":"Patterson","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Zelikovsky","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Georgia State University, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.3349\/ymj.2021.62.11.961"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.abg3055"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-genom-083118-014857"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1534\/genetics.108.099127"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/ve\/veaa103"},{"issue":"4","key":"e_1_3_3_7_1","article-title":"IEEE Information Theory Society Newsletter","volume":"53","author":"IEEE Information Theory Society","year":"2003","unstructured":"IEEE Information Theory Society. IEEE Information Theory Society Newsletter. IEEE, 2003; 53(4). Available from: https:\/\/www.itsoc.org\/publications\/newsletters\/itNL1203.pdf","journal-title":"IEEE"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41559-017-0077"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/genetics\/120.3.849"},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40359-024-01743-4"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/v10080407"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0712255105"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0016672308009579"},{"key":"e_1_3_3_14_1","article-title":"Community structure and temporal dynamics of sars-cov-2 epistatic network allows for early detection of emerging variants with altered phenotypes","author":"Mohebbi F","year":"2023","unstructured":"Mohebbi F, , Zelikovsky A, , Mangul S, et al. Community structure and temporal dynamics of sars-cov-2 epistatic network allows for early detection of emerging variants with altered phenotypes. bioRxiv, 2023.","journal-title":"bioRxiv"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-47304-6"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms8385"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.2807\/1560-7917.ES.2017.22.13.30494"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg2361"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/pro.2897"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1008384"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.2174\/138920209789177575"}],"container-title":["Journal of Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261423963","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/15578666261423963","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261423963","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/15578666261423963","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T09:46:24Z","timestamp":1779875184000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/15578666261423963"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,20]]},"references-count":20,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2026,6,1]]}},"alternative-id":["10.1177\/15578666261423963"],"URL":"https:\/\/doi.org\/10.1177\/15578666261423963","relation":{},"ISSN":["1066-5277","1557-8666"],"issn-type":[{"value":"1066-5277","type":"print"},{"value":"1557-8666","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,20]]}}}