Bioinformatics advances最新文献

{"title":"TAPPR PCR assay design-targeted, automated, primer and probe retrieval for scalable molecular assay design.","authors":"Phillip E Davis, Colin W Price, Taylor Otwell, Anshika Kapoor, Vita Domnenko, Joseph A Russell","doi":"10.1093/bioadv/vbag274","DOIUrl":"https://doi.org/10.1093/bioadv/vbag274","url":null,"abstract":"<p><strong>Motivation: </strong>The rapid and reliable detection of infectious disease agents is critical for effective biosurveillance, diagnostics, and outbreak response. However, existing molecular assay design methods face significant limitations in scalability, speed, and adaptability to rapidly evolving pathogens, often leading to outdated or suboptimal assays. Here, we present Targeted, Automated Primer and Probe Retrieval (TAPPR), a novel, automated pipeline for scalable molecular assay design. TAPPR uses alignment-free methods to identify conserved regions and marker sequences across large-scale genomic datasets, supporting customizable design parameters for inclusivity, exclusivity, and assay specificity. To evaluate TAPPR's performance, assays were designed for diverse microbial targets, including Mpox, <i>Mycobacterium tuberculosis</i>, SARS-CoV-2, and <i>Candida albicans</i>, representing viral, bacterial, and fungal pathogens. The pipeline incorporates k-mer set operations and clustering strategies to address sequence diversity and streamline conserved region identification. Designed assays underwent in silico PCR simulations and laboratory testing to assess specificity, sensitivity, and exclusivity. Results demonstrated high accuracy across targets, with superior sensitivity to existing fielded assays where available. Additionally, we compare TAPPR to alternative available molecular assay design tools to demonstrate its advantages. This work highlights TAPPR's capability to accelerate the development of molecular diagnostics by efficiently leveraging vast genomic datasets and addressing computational bottlenecks. TAPPR represents a scalable, adaptable tool for rapidly designing high-quality molecular assays, positioning itself as a critical asset for biosurveillance and public health response to emerging and re-emerging infectious disease threats.</p><p><strong>Results: </strong>TAPPR demonstrates rapid and scalable data-driven molecular assay design through alignment-free estimations of conserved regions and marker regions. Through both <i>in silico</i> and lab bench evaluation, TAPPR assays are demonstrated to perform equivalently or better than previously utilized publicly available qPCR assays for emergent disease diagnostics. TAPPR is also shown to produce results where other available automated molecular assays design solutions fail to do so on the order of hours.</p><p><strong>Availability and implementation: </strong>TAPPR is available at https://github.com/mriglobal/tappr under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag274"},"PeriodicalIF":2.6,"publicationDate":"2026-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13630449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149119300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer Bork (1963-2026): pioneer of computational biology, architect of bioinformatics resources, and visionary microbiome researcher.","authors":"Alex Bateman","doi":"10.1093/bioadv/vbag227","DOIUrl":"https://doi.org/10.1093/bioadv/vbag227","url":null,"abstract":"","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag227"},"PeriodicalIF":2.6,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13653603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149171420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phil Bourne (1953-2026): from small molecules to big data-the journey of a multifaceted visionary.","authors":"Andreas Prlić, Cameron Mura","doi":"10.1093/bioadv/vbag239","DOIUrl":"10.1093/bioadv/vbag239","url":null,"abstract":"<p><p>Born in London in 1953 and raised in Australia, Phil Bourne spent over four decades in science moving across scales: from crystal structures to the world's premier structural biology database; from scientific journals to national data policy; from molecules to institutions. The International Society for Computational Biology (ISCB) chose Phil to receive its 2026 Outstanding Service Award. He passed away on 8 March 2026, in Charlottesville, VA, before he could receive it. This piece is in memory of Phil, who most recently served as the Founding Stephenson Dean of the University of Virginia's School of Data Science and as a Professor of Biomedical Engineering-and who was, over the course of his career, a steadfast builder of Computational Biology and Bioinformatics, of Open Science and Data Science. A tribute site, https://philbourne.muchloved.com, invites those who knew Phil to share memories, photos, and reflections.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag239"},"PeriodicalIF":2.6,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13576737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148979000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Central Dogma Transformer II: an AI microscope for understanding cellular regulatory mechanisms.","authors":"Nobuyuki Ota","doi":"10.1093/bioadv/vbag268","DOIUrl":"https://doi.org/10.1093/bioadv/vbag268","url":null,"abstract":"<p><strong>Motivation: </strong>Interpretability is not optional in biology: understanding gene regulation requires models whose learned structure can be directly interrogated, not merely accurate predictors whose internals resist mapping onto regulatory relationships. We ask whether an architecture mirroring the central dogma yields attention and gradient maps that recover known regulatory elements and networks in inspectable form.</p><p><strong>Results: </strong>Central Dogma Transformer II (CDT-II) mirrors the central dogma in its architecture-DNA self-attention, RNA self-attention, and DNA-to-RNA cross-attention-requiring only genomic embeddings and raw per-cell expression. On K562 CRISPR interference (CRISPRi) data with five genes held out entirely, CDT-II predicts perturbation effects (per-gene mean <math><mrow><mi>r</mi> <mo> = </mo> <mn>0.84</mn></mrow> </math> ), recovers the <i>GFI1B</i> regulatory network (6.6-fold enrichment, <math><mrow><mi>P</mi> <mo> = </mo> <mn>3.5</mn> <mo>×</mo> <mrow> <mrow> <msup> <mrow><mrow><mn>10</mn></mrow> </mrow> <mrow><mo>-</mo> <mn>17</mn></mrow> </msup> </mrow> </mrow> </mrow> </math> ), and concentrates cross-attention on ENCODE regulatory elements including CTCF sites (mean 7.67 <math><mo>×</mo></math> across 28 target genes, <math><mi>P</mi> <mo><</mo> <mn>.001</mn></math> ). Gradient attribution predicts consequences of perturbing therapeutic targets (mean <math><mrow><mi>r</mi> <mo> = </mo> <mn>0.82</mn></mrow> </math> ). For <i>TFRC</i>, target of the anti-TfR1 antibody PPMX-T003, it identifies erythrocyte-structure, iron-dependent DNA-synthesis and oxidative-stress genes, matching anemia and ferroptosis reported clinically and preclinically-without clinical data as input. CDT-II acts as an AI microscope, surfacing clinically relevant regulatory structure from perturbation experiments alone.</p><p><strong>Availability and implementation: </strong>Source code is available at https://github.com/nobusama/CDT2. Pre-computed embeddings, training data, and model weights are available at https://huggingface.co/datasets/nobusama17/CDT2-data.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag268"},"PeriodicalIF":2.6,"publicationDate":"2026-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13649580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149168618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ScGeo reveals non-canonical trajectories beyond RNA velocity in radiation-induced hematopoietic recovery.","authors":"Yu-Chen Liu, Kengo Yoshida","doi":"10.1093/bioadv/vbag266","DOIUrl":"10.1093/bioadv/vbag266","url":null,"abstract":"<p><p>Low-dimensional representations are central to single-cell RNA sequencing analysis, yet perturbation-associated geometry is often interpreted visually without explicit assessment of estimator, sampling, or representation dependence. We introduce ScGeo, a representation-aware framework that treats embeddings as quantitative objects and reports robust state displacement, biological-sample uncertainty, local geometric preservation, cross-representation stability, distributional change, and agreement between condition-dependent displacement and independently supplied dynamics estimates. In GSE280305 post-irradiation hematopoietic recovery, ScGeo identified heterogeneous D8-to-D21 cluster displacement and partial agreement between geometric shifts and RNA velocity, while avoiding interpretation of time-point mixing as proof of valid integration. A prespecified synthetic benchmark showed that robust center estimators reduced outlier sensitivity, global representation corruption was detectable, and fine-grained localization of local distortion remained limited. A GSE132188-derived pancreatic-development workflow provided descriptive geometry-dynamics validation. In GSE249479, inflammatory effects in hematopoietic stem and progenitor cells were broadly stable across the primary representation ensemble but remained descriptive because biological-replicate identity was unavailable. In replicate-aware GSE211713 lung-radiation analysis, early 17 Gy effects were representation-sensitive, whereas late remodeling was stable in five of six major compartments. ScGeo provides an auditable downstream layer for distinguishing stable, neutral, insufficient-coverage, and representation-sensitive interpretations rather than assuming any single latent space is biologically definitive.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag266"},"PeriodicalIF":2.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13615651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ClinIAN: Clinically Informed Attention Network.","authors":"Florian König, Jonas C Ditz, Elham Shamsara, Pontus Hedberg, Iuri Fanti, Pontus Nauclér, Luca Carioti, Andreas Walker, Milosz Parczewski, Francis Drobniewski, Francesca Ceccherini-Silberstein, Maurizio Zazzi, Björn-Erik Ole Jensen, Anders Sönnerborg, Nico Pfeifer","doi":"10.1093/bioadv/vbag254","DOIUrl":"10.1093/bioadv/vbag254","url":null,"abstract":"<p><strong>Motivation: </strong>Artificial Neural Networks (ANNs) hold promise in predicting disease severity from viral protein sequences. To gain clinical insights, the models must adhere to two key factors: first, they need to be interpretable, as using black-box models within a healthcare setting poses risks, and second, they should integrate viral and clinical features to correct for biases within the data. We propose ClinIAN (Clinically Informed Attention Network), an inherently interpretable end-to-end learning model that effectively combines clinical and sequence parameters. We evaluate ClinIAN within the context of the challenging task of predicting the severity of SARS-CoV-2 infection, but it could be applied to other medical and biological contexts, with sequence and tabular features, such as bacterial infections or cancer research.</p><p><strong>Results: </strong>We evaluate the performance capabilities of ClinIAN in predicting SARS-CoV-2 severity using a subset of the EuCARE hospitalized cohort. We show ClinIAN's ability to capture biologically relevant features across multiple layers of resolution while retaining stable state-of-the-art performance.</p><p><strong>Availability and implementation: </strong>The code is available on Zenodo and on GitHub.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag254"},"PeriodicalIF":2.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13596770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149026993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A little longer, a lot better: simulation-guided exploration of extended-length single-end barcoded reads for structural variant detection.","authors":"Can Luo, Yichen Henry Liu, Han Liu, Zhenmiao Zhang, Lu Zhang, Brock A Peters, Xin Maizie Zhou","doi":"10.1093/bioadv/vbag267","DOIUrl":"10.1093/bioadv/vbag267","url":null,"abstract":"<p><strong>Motivation: </strong>Accurate detection of genetic variants, including single nucleotide polymorphisms (SNPs), small insertions and deletions (INDELs), and structural variants (SVs), is essential for comprehensive genomic analysis. While short-read sequencing performs well for SNP and INDEL detection, it remains limited in resolving SVs, particularly in complex genomic regions, due to its short read length. Linked-read sequencing technologies, such as single-tube Long Fragment Read (stLFR), partially address this limitation by incorporating molecular barcodes to provide long-range information.</p><p><strong>Results: </strong>In this study, we evaluate conventional paired-end linked reads (PE100_stLFR) and explore a conceptual extension: long single-end barcoded reads of 500 bp (SE500_stLFR) and 1000 bp (SE1000_stLFR). We developed stLFR-sim, a Python-based simulator that reproduces the stLFR workflow and enables realistic benchmarking. Using a high-quality T2T assembly of HG002, we generated multiple datasets across 12 sequencing configurations. SVs were called using Aquila_stLFR (v2) and benchmarked against the Genome in a Bottle (GIAB) HG002 SV truth set with Truvari. We show that simulated PE100_stLFR has the same trade-off pattern between precision and recall in SV calling compared to real data. Increasing read length consistently improves SV detection accuracy, with SE1000_stLFR achieving the best performance among the evaluated stLFR configurations and showing competitive performance relative to ICLR- and pangenome-based approaches, while approaching the performance of long-read methods. Collectively, our results highlight the potential of extended-length single-end barcoded reads for improving SV detection and demonstrate how simulation can be used to evaluate prospective linked-read sequencing designs.</p><p><strong>Availability and implementation: </strong>stLFR-sim is a Python-based, open-source simulator for paired-end and extended-length single-end barcoded sequencing. The software is freely available under the MIT License at https://github.com/maiziezhoulab/stLFRsim.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag267"},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MosaicLev: modified Levenshtein distance for mobile element-aware genome comparison.","authors":"Harry Stoltz, Thomas E Kuhlman","doi":"10.1093/bioadv/vbag261","DOIUrl":"10.1093/bioadv/vbag261","url":null,"abstract":"<p><strong>Motivation: </strong>Genomes diverge, in part due to the activity of mobile elements, including elements that excise and reinsert (cut-paste) or propagate via an RNA intermediate (copy-paste). Standard sequence comparison methods are not motif-aware, penalizing mobile element insertions based on length rather than recognizing them as single biological events, while alignment-free methods still fail to adequately describe a known mobile-element sequence as a unified change.</p><p><strong>Results: </strong>We introduce a modified Levenshtein distance ( <math><mrow><mi>mlev</mi></mrow> </math> ) that discounts specified block insertions via a tunable parameter ( <math><mrow><mi>m</mi> <mo> </mo> <mo>∈</mo> <mo> </mo> <mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo></mrow> </math> ): at <math><mrow><mi>m</mi> <mo>=</mo> <mn>0</mn></mrow> </math> it recovers ordinary Levenshtein distance and at <math><mrow><mi>m</mi> <mo>=</mo> <mn>1</mn></mrow> </math> a whole chunk acts like a single edit. On 67 Cluster G1 mycobacteriophage genomes (a viral group with MPME1 and MPME2), MPME1 targeting yielded <math><mrow><mo>∼</mo> <mn>53</mn> <mi>%</mi></mrow> </math> discount for the 31-genome MPME1-score group and <math><mrow><mo>∼</mo> <mn>9</mn> <mi>%</mi></mrow> </math> for the 11-genome MPME2-score group. MPME2 targeting reversed this pattern, with 18 phages low on both scores. For PV92, a 346-bp Yb8-containing insertion received 99.71% forward reduction at <math><mrow><mi>m</mi> <mo>=</mo> <mn>1</mn></mrow> </math> and none in reverse. Together, these results demonstrate that MosaicLev can quantify the contribution of known mobile elements to sequence differences across distinct genomic settings.</p><p><strong>Availability and implementation: </strong>Python implementation with Numba JIT compilation freely available at https://doi.org/10.5281/zenodo.18452982.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag261"},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13604845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theoretical estimates on the expected number of mutations needed to reconstruct clonal lineage trees.","authors":"Nishat Anjum Bristy, Russell Schwartz","doi":"10.1093/bioadv/vbag264","DOIUrl":"10.1093/bioadv/vbag264","url":null,"abstract":"<p><strong>Motivation: </strong>Phylogenetics faces a growing challenge from increasingly large and complicated data sets enabled by ever-improving sequencing technologies. The issue is particularly acute for somatic evolution studies, such as cancer cell lineages, where single-cell data sets may include tens of thousands of mutations in hundreds of thousands of genetically distinct cells. Simultaneously, the biological complexity of somatic evolution has led to complex phylogeny methods that struggle to scale to even modest data sizes.</p><p><strong>Results: </strong>We explore the theoretical and empirical basis for one strategy for managing these large data sets: subsampling mutations for the computationally challenging phylogeny problem followed by faster placement of mutations on a putatively known guide tree. We specifically focus on determining the number of mutations sufficient to recover the true phylogeny at some level of resolution with high probability. We theoretically analyze variants of several common models that underlie popular tools for building clonal lineage trees. We further test these bounds through simulations of these models, extensions of them, and real biological datasets. The results suggest that modest numbers of mutations suffice to reconstruct clonal tree topologies for typical numbers of clones, supporting subsampling as a general strategy for managing the challenges of ever-growing data.</p><p><strong>Availability and implementation: </strong>All analysis code and scripts used for data simulation are implemented in Python 3 and available at https://github.com/CMUSchwartzLab/mutation-subsampling.git.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag264"},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13614416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149066025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"It's a wrap: deriving distinct discoveries with FDR control after a GWAS analysis.","authors":"Benjamin B Chu, Zihuai He, Chiara Sabatti","doi":"10.1093/bioadv/vbag260","DOIUrl":"10.1093/bioadv/vbag260","url":null,"abstract":"<p><strong>Motivation: </strong>The standard analysis pipeline for genome-wide association studies (GWAS) is based on marginal tests of association. These are computationally convenient and portable, but the discoveries are not immediately interpretable and require post-processing such as \"clumping\" and \"fine mapping.\" An interesting alternative is provided by conditional independence hypotheses: their rejections lead to distinct signals across the genome, accounting for measured confounders and pointing to separate causal pathways. Recent work has shown how summary statistics resulting from the standard marginal GWAS can be used as input to test conditional independence hypotheses while controlling the false discovery rate (FDR). We previously developed a pipeline tailored to European genomes.</p><p><strong>Results: </strong>Here we introduce and release new software (solveblock) extending this capability to a much richer collection of studies. Given a set of genotyped samples, or a reference dataset, the new pipeline efficiently estimates the high-dimensional correlation matrices that describe dependencies across the genome, making rather common sparsity assumptions. Taking this sample-specific estimate as input, the software identifies groups of genetic variants that are highly correlated and uses them to define an appropriate resolution for conditional independence hypotheses. Finally, we compute the distribution for the exchangeable negative controls necessary to test these hypotheses. Simulations based on five UK Biobank sub-populations, illustrate the method's FDR control. The analysis of 26 phenotypes of varying polygenicity in British individuals, results in <math><mrow><mo>≈</mo> <mn>19</mn></mrow> </math> additional discoveries, compared to standard marginal association testing.</p><p><strong>Availability and implementation: </strong>Our code, precompiled software, and processed files for these five sub-populations are openly shared.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":"6 1","pages":"vbag260"},"PeriodicalIF":2.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13676318/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149217348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}