Journal of Computational Biology最新文献

{"title":"Uncovering Epistatic Interactions in SARS-CoV-2 Evolution Through Hidden Markov Models.","authors":"Ayotomiwa Ezekiel Adeniyi, Akshay Juyal, Pavel Skums, Murray Patterson, Alex Zelikovsky","doi":"10.1177/15578666261423963","DOIUrl":"10.1177/15578666261423963","url":null,"abstract":"<p><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-Welch algorithm. We implement permutation-based validation with temporal order noise reduction (>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-December 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 69-70 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.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"692-704"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146258239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DANCE: Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images.","authors":"Taslim Murad, Prakash Chourasia, Sarwan Ali, Imdad Ullah Khan, Murray Patterson","doi":"10.1177/15578666261441311","DOIUrl":"10.1177/15578666261441311","url":null,"abstract":"<p><p>Cancer is a complex disease characterized by uncontrolled cell growth and requires an accurate classification for effective treatment. T cell receptors (TCRs), crucial proteins in the immune system, play a pivotal role in antigen recognition. Advancements in sequencing technologies have facilitated the comprehensive profiling of TCR repertoires, uncovering TCRs with potent anticancer activity and enabling TCR-based immunotherapies. Performing an effective analysis of these complex biomolecules requires representations that accurately capture both their structural and functional characteristics. T cell protein sequences pose unique challenges because of their relatively shorter lengths compared to other biomolecules. Traditional vector-based embedding methods may encounter issues such as information loss. Therefore, an image-based representation approach becomes a preferred choice for efficient embedding, allowing the preservation of essential details and enabling a comprehensive analysis of T cell protein sequences. We propose generating images from protein sequences using the concept of chaos game representation (CGR). We design images using the kaleidoscopic images approach. This Deep Learning-Assisted Analysis of ProteiN Sequences Using Chaos Enhanced Kaleidoscopic Images (called DANCE) provides a unique way to visualize protein sequences by recursively applying chaos game rules around a central seed point. The resulting kaleidoscopic images exhibit symmetrical patterns that offer a visual representation of the protein sequences. To investigate the effectiveness of this approach, we perform classification of the TCR protein sequences in terms of their respective target cancer cells, since TCRs are known for their immune response against cancer disease. The DANCE technique is used to turn the TCR sequences into pictures before classification. We employ deep learning (DL) vision models to classify the generated images to obtain insight into the relationship between the visual patterns in the generated kaleidoscopic images and the underlying protein properties. By combining CGR-based image generation with DL classification, this study opens new possibilities in protein analysis.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"680-691"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147698965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing Ancestral Non-Coding RNAs of Multiple Families Using Sequence and Structural Information with Tree Decomposition.","authors":"Songdi Hu, Vladimir Reinharz, Olivier Tremblay-Savard","doi":"10.1177/15578666261423964","DOIUrl":"10.1177/15578666261423964","url":null,"abstract":"<p><p>The reconstruction of ancestral non-coding RNA (ncRNA) sequences is particularly challenging due to the main conservation forces being applied to the structure, rather than the sequence. Naively trying to preserve the structure during the reconstruction tends to produce ancestors that are more energetically fit to the structure than their descendants, a clear contradiction. While most sequences are associated to only one functional structure, RNA families have an old and complex history. It has been hypothesized that some ancestral RNAs were combining multiple functions, with multistable structures. At some point, a duplication event happened, and each copy subspecialized into a specific structure. To circumvent the bias introduced by reconstructing sequences when only one structure is conserved, we recently proposed an approach using substitution and base pair costs that focuses on simultaneously reconstructing the ancestor of two related ncRNA families, assuming that they were created by this process of duplication followed by subspecialization. In this work, we improve the previous approach by leveraging advances in tree decomposition algorithms to (1) incorporate simultaneously more constraints and positions in the reconstruction which (2) allows to use a more realistic energetic model. Results on simulated datasets demonstrate significant improvements in ancestral sequence inference accuracy while reducing the number of optimal sequences inferred by several orders of magnitude. On real datasets of RFam clans (<i>Glm</i> and <i>FinP-traJ</i>), we show that the new approach is able to infer fewer optimal ancestral sequences that are more fit to both structures compared with previous methods.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"499-517"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147390115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Synteny: A Scalable Phylogenomics Method for Whole-Genome Duplication Detection.","authors":"Reza Kalhor, Manuel Lafond, Celine Scornavacca","doi":"10.1177/15578666251415567","DOIUrl":"10.1177/15578666251415567","url":null,"abstract":"<p><p>Gene duplication is a fundamental driver of species adaptation and the evolution of new functions, making the reconstruction of historical duplication events crucial for understanding evolutionary processes. Whole-genome duplications (WGDs), which duplicate all gene families simultaneously, have profoundly influenced the evolution of plants, yeast, and vertebrates. Genome-scale data, such as syntenic blocks and gene family counts, are commonly employed to infer WGDs. However, detecting ancient WGDs remains challenging, as their genomic signatures are often overshadowed by extensive rearrangements and gene losses. Phylogenetic reconciliation methods between species and gene trees offer a potential means of identifying such ancient events, but frequently assume independence among gene families. This can lead to missed detections of WGDs, where gene duplications are inherently interdependent. Phylogenomics reconciliation addresses this challenge by reconciling multiple gene families at once. Unfortunately, existing models often constrain the space of possible reconciliations, overlook gene losses resulting from fractionation, or depend on conserved synteny across multiple species. This limits the number of genes that can be analyzed concurrently.In this work, we explore a phylogenomics reconciliation model that avoids synteny reliance, explicitly incorporates gene losses, and permits flexible remapping of duplications. Reconciliation under this model is NP-hard, and existing algorithms lack the scalability for large-scale datasets. To address this need, we present novel algorithmic strategies that efficiently handle tens of thousands of gene trees-a level of scalability previously unattained. We also evaluate our approach against existing methods. Experiments on both simulations and real data show that traditional LCA-mapping can yield incorrect WGD predictions after fractionation, whereas our approach is more robust. By comparing predictions using true and reconstructed gene trees, we further show that reconstruction errors greatly affect method performance and that gene tree correction is necessary for reliable results. Real data tests also reveal that our approach can recover WGDs missed by other reconciliation methods.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"456-481"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147473954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probability-Based Sequence Comparison Finds Pre-Eutherian Nuclear Mitochondrial DNA Segments in Mammalian Genomes.","authors":"Muyao Huang, Martin C Frith","doi":"10.1177/15578666261416560","DOIUrl":"10.1177/15578666261416560","url":null,"abstract":"<p><p>The insertion of mitochondrial genome-derived DNA sequences into the nuclear genome is a frequent event in organismal evolution, resulting in nuclear-mitochondrial DNA segments (NUMTs), which serve as a significant driving force for genome evolution. Once incorporated into the nuclear genome, some NUMTs can be conserved for extended periods and may potentially acquire novel cellular roles. However, current mainstream methods for detecting NUMTs are inefficient at identifying ancient and highly degraded NUMTs, leading to their prevalence and impact being underestimated. These ancient NUMTs likely play a far greater role in genetic functions than previously recognized, including contributing to the acquisition of functional exons. This study focuses on identifying ancient NUMTs in mammalian genomes using enhanced high-sensitivity sequence comparison methods. A sensitive and accurate NUMT searching pipeline was established, predicting 1013 NUMTs in the human reference genome, 364 (36%) of which are newly detected compared to the University of California, Santa Cruz (UCSC) reference human NUMTs database. Notably, 90 pre-eutherian human NUMTs were identified, representing significantly older NUMTs than previously reported, with origins dating back at least 100 million years. The most ancient mammalian NUMT could even date back over 160 million years, inserted into the nuclear genome of the common ancestor of therian mammals. This study provides a comprehensive exploration of the quantity and evolutionary history of mammalian NUMTs, paving the way for future research on endosymbiotic impact on the evolution of nuclear genomes.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"401-419"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146105709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Small Parsimony to Horizontal Gene Transfer: Inferring Horizontal Transfer and Gene Loss for Single-Origin Characters.","authors":"Alitzel López Sánchez, Guillaume E Scholz, Peter F Stadler, Manuel Lafond","doi":"10.1177/15578666261426009","DOIUrl":"10.1177/15578666261426009","url":null,"abstract":"<p><p>The simple underlying pattern of presence-absence of a character within a species tree provides useful steps to trace complex evolutionary histories. Character-based models such as <i>perfect transfer networks</i> and its galled variant aim to leverage this information to predict horizontal gene transfers. Under the assumption that characters have a single origin, are rarely lost, and can be transferred horizontally, they remain an efficient inference method for almost tree-like scenarios. Nevertheless, they can sometimes predict overly complicated scenarios, and its simplest structural variants are too restrictive for practical uses. With the goal of extending this model to include loss events, we present a Sankoff-Rousseau-like algorithm that aims to recover the simplest possible scenarios that combine gene transfers and losses using solely the single character information already contained in a given species tree. We establish a link between the small parsimony problem and the inference of scenarios with a minimum number of losses and transfers, allowing losses and transfers to have a user-defined penalization for this end. We also explore the utility of our model for tracing possible highways of gene transfers by presenting a real case study on a dataset of bacterial species and Kyoto Encyclopedia of Genes and Genome functions as characters.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"535-557"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147638938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Cross-Order Comparative Genomics Analysis of Insect Innate Immunity Suggests Niche-Specific Adaptation.","authors":"Triveni Shelke, Vanika Gupta, Ishaan Gupta","doi":"10.1177/15578666261426005","DOIUrl":"10.1177/15578666261426005","url":null,"abstract":"<p><p>Insects thrive in highly diverse environmental niches, exposing them to varying pathogens. This complex interaction has led to the development of a variety of defense mechanisms collectively termed as innate immunity. Innate immunity is the first line of defense against pathogens in most organisms. Despite the availability of genomic and protein data of insects, we do not understand how innate immunity has evolved. This study reports class-level analysis of innate immunity in <i>Insecta</i> spanning approximately 300 million years ago of evolution. We used the available data on 27 insect species of five predominant orders to track the evolutionary paths of innate immune proteins. We analyzed orthogroups and gene family dynamics to identify core conserved components and lineage-specific innovations in immune genes. Through orthogroup analysis, we find an asymmetrical or incoherent distribution of orthologs within the immune orthogroups among the orders that present intriguing events such as gene duplications, losses and functional diversification. For example, instances of missing orthologs in corresponding immune orthogroups were noted for specific orders, such as Defensin in <i>Lepidoptera</i>, transferrin in <i>Hemiptera,</i> and Cathepsin B in <i>Hymenoptera</i>. Lineage-specific conservation of antimicrobial peptides was observed in <i>Lepidoptera</i> and <i>Hymenoptera</i>. Interestingly, proteins like Peptidoglycan recognition protein (PGRP)-SA, Spatzle, and others have undergone major family expansion and contraction events in the insect species studied. By constructing phylogenies based on immune-related proteins and assessing the signatures of positive selection, we gained insights into diversification and adaptive evolution. Signatures of positive selection were only demonstrated by PGRP-SA and diptericin proteins, with a specific site under selection found in the latter. Our findings present a broad picture of how the five closely placed yet highly divergent orders of the class <i>Insecta</i> have maintained innate immunity in close reference to their ecological niches. These findings can have practical implications in strategizing pest management and insect conservation in the wild.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"439-455"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147512495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simple and Thorough Detection of Related Sequences with Position-Varying Probabilities of Substitutions, Insertions, and Deletions.","authors":"Martin C Frith, Patrick Boppert, Patrick Styll, Milot Mirdita","doi":"10.1177/15578666261428480","DOIUrl":"10.1177/15578666261428480","url":null,"abstract":"<p><p>One way to understand biology is by finding genetic sequences that are related to each other. Often, a family of related sequences has position-varying probabilities of substitutions, insertions, and deletions: we can use these to find distantly related sequences. There are popular software tools to do this, which all have limitations. They either do not use all probability evidence (e.g., PSI-BLAST, MMseqs2) or have excessive complexity and minor biases (e.g., HMMER). This complexity inhibits fertile development of alternative tools.This study describes a simplest reasonable way to find related sequences, making full use of position-varying probabilities. The algorithms likely use the fewest operations that such algorithms possibly could, so they are fast and simple. This has been implemented in prototype software named DUMMER (Dumb Uncomplicated Match ModelER). Its sensitivity and specificity are competitive with HMMER. It finds evidence that the human genome has many more relics of some ancient transposons, including LF-SINE, which was co-opted for various functions in common ancestors of all land vertebrates.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"420-438"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147512471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Branching-Process Modeling of Homology Distribution in Salmonid Genomes.","authors":"Yue Zhang, David Sankoff","doi":"10.1177/15578666261426392","DOIUrl":"10.1177/15578666261426392","url":null,"abstract":"<p><p>Comparative analysis of sequence similarity distributions reveals evolutionary mechanisms shaping gene families. In Salmonidae, whole-genome duplication (WGD) and rapid speciation pose a challenge for modeling retained homologs and sequence divergence. We introduce a stochastic branching-process framework that models sequence similarity decay over evolutionary time and quantifies fractionation rates across successive duplication events. We derive moment-generating functions of pairwise similarity scores and carry out simulation-based validation. Applying our model to multiple salmonid genomes (Atlantic salmon, rainbow trout, Chinook salmon, …), we not only recapitulate observed bimodal similarity distributions, but we also quantify gene retention across evolutionary branches. Results indicate that the estimated fractionation rates for both WGDs (<math><mrow><mrow><msub><mrow><mi>μ</mi></mrow><mn>1</mn></msub></mrow><mo>,</mo><mrow><msub><mrow><mi>μ</mi></mrow><mn>2</mn></msub></mrow><mo>≈</mo><mn>0.0009</mn></mrow></math>-0.0013 per Myr) remain highly consistent across species and insensitive to synteny block size, supporting a conserved post-WGD gene loss dynamic. In contrast, lineage-specific differences in duplicate retention arise primarily in the temporal gap between duplication events rather than differences in instantaneous loss rates. These findings underscore the stability of fractionation dynamics and the critical role of structural genome decay in shaping retention patterns in salmonid evolution and sucker fish.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"558-572"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CherryRed: A Software Implementation of Cherry Distance with a New Optimization and Heuristic.","authors":"Kaari Landry, Olivier Tremblay-Savard","doi":"10.1177/15578666261424919","DOIUrl":"10.1177/15578666261424919","url":null,"abstract":"<p><p>Representing complex evolutionary relationships, such as hybridization and horizontal gene transfer, increasingly requires phylogenetic networks (over phylogenetic trees). Methods of construction of such networks rely on a measure of difference (a distance) between them to identify discrepancies between the newly built networks and a reference. Here, we focus on the cherry distance, a newly developed distance based on the number of cherry operations required to transform one input network into the other. Our work takes an existing algorithm design to calculate cherry distance on level-1 orchards and refines it using a preprocessing filter that maps reticulated elements of the input networks. We also present a heuristic strategy, which operates on only the most promising substructures of the input. CherryRed is a new, publicly available Rust package, which includes both of these improvements. Using CherryRed, we experimentally show how effective our refinement to the exact algorithm is (and when it is most effective), and we show how our heuristic maintains a high degree of accuracy while making large runtime efficiency gains. Characteristics of cherry distance are explored as well, with experiments on a real data set from the Rose family. Particularly, we compare cherry distance with a network adaptation of the ubiquitous Robinson-Foulds (RF) distance on trees, the soft RF distance (softwired distance). We do so with a common rearrangement operation (rooted nearest-neighbor interchange) and a leaf-moving operation, to show a higher degree of sensitivity in cherry distance, and a natural reflection of the number of taxa that are impacted by changes in the network.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"518-534"},"PeriodicalIF":1.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}