NAR Genomics and Bioinformatics最新文献_第4页

PolyGenie: a reproducible Nextflow pipeline for phenome-wide association studies using polygenic risk scores. PolyGenie：一个可重复的Nextflow管道，用于使用多基因风险评分进行全表型关联研究。

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NAR Genomics and Bioinformatics Pub Date : 2026-06-09 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag056

Xavier Farré, Mireia Gasco, Natalia Blay, Rafael de Cid

{"title":"PolyGenie: a reproducible Nextflow pipeline for phenome-wide association studies using polygenic risk scores.","authors":"Xavier Farré, Mireia Gasco, Natalia Blay, Rafael de Cid","doi":"10.1093/nargab/lqag056","DOIUrl":"10.1093/nargab/lqag056","url":null,"abstract":"Phenome-wide association studies (PheWAS) using polygenic risk scores (PRS) offer a powerful framework for exploring the shared genetic architecture of complex traits across diverse phenotypic domains. However, no standardized, portable pipeline exists to facilitate their systematic execution and visualization in arbitrary population cohorts. We present PolyGenie, an open-source Nextflow pipeline that takes precomputed PRS and cohort phenotype data as input and performs scalable PheWAS analysis across binary and continuous outcomes. The pipeline produces regression results and percentile-based prevalence estimates, which are stored in a SQLite database and visualized through an interactive Dash web application. To demonstrate its utility and reproducibility, we provide a fully worked example using the GCAT cohort, applying 135 PRS to a broad range of clinical, molecular, and lifestyle phenotypes. PolyGenie is designed to be deployed on any cohort with minimal configuration, enabling standardized cross-trait analyses and interactive exploration of genetic risk.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag056"},"PeriodicalIF":3.0,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13247587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148219981","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}

引用次数: 0

Exploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markers. 用标记探索衰老的分子特征，一个用于评估基因集作为表型标记的R工具包。

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NAR Genomics and Bioinformatics Pub Date : 2026-06-08 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag057

Rita Martins-Silva, Alexandre Kaizeler, Nuno L Barbosa-Morais

{"title":"Exploring molecular signatures of senescence with marker, an R Toolkit for evaluating gene sets as phenotypic markers.","authors":"Rita Martins-Silva, Alexandre Kaizeler, Nuno L Barbosa-Morais","doi":"10.1093/nargab/lqag057","DOIUrl":"10.1093/nargab/lqag057","url":null,"abstract":"Many biological processes, including cellular senescence, manifest as diverse phenotypes across cell types and conditions. Lacking definitive markers, researchers often rely on the expression of sets of genes to identify these complex states. However, multiple approaches exist to summarize gene set expression into quantitative metrics (i.e. signatures), each with distinct strengths and limitations, and we know of no consensual framework to systematically evaluate their performance across datasets. We therefore developed markeR, an open-source, modular R package that evaluates gene sets as phenotypic markers using scoring and enrichment-based approaches. markeR generates interpretable metrics and intuitive visualizations for benchmarking gene signatures and exploring their associations with study variables. As a case study, we applied markeR to 9 published senescence-related gene sets across 25 RNA-seq datasets, 6 human cell types and 12 senescence-inducing conditions. Gene set performance varied widely: some signatures (e.g. SenMayo) were robust senescence markers across contexts, while others (e.g. MSigDB sets) performed poorly. We further applied markeR to 49 GTEx tissues, revealing tissue- and age-related differences in senescence-associated signals. Together, these findings emphasize the difficulty of characterizing molecular phenotypes and demonstrate markeR's potential for the systematic evaluation of gene sets in various biological contexts.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag057"},"PeriodicalIF":3.0,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13244156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212633","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}

引用次数: 0

Predicting isocitrate dehydrogenase mutation status in acute myeloid leukemia from gene expression profiles by machine learning. 通过机器学习从基因表达谱预测急性髓性白血病的异柠檬酸脱氢酶突变状态。

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NAR Genomics and Bioinformatics Pub Date : 2026-06-08 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag058

Ina Jung, Anne-Laure Vitte, Florent Chuffart, Simon Chevalier, Pascal Mossuz, Saadi Khochbin, Ekaterina Bourova-Flin

{"title":"Predicting isocitrate dehydrogenase mutation status in acute myeloid leukemia from gene expression profiles by machine learning.","authors":"Ina Jung, Anne-Laure Vitte, Florent Chuffart, Simon Chevalier, Pascal Mossuz, Saadi Khochbin, Ekaterina Bourova-Flin","doi":"10.1093/nargab/lqag058","DOIUrl":"10.1093/nargab/lqag058","url":null,"abstract":"We developed machine-learning models to predict isocitrate dehydrogenase (IDH) mutation status in acute myeloid leukemia (AML) from gene expression profiles and to reconstruct missing IDH annotations across public datasets. Transcriptomic data from 19 cohorts (5844 samples) were harmonized using batch correction, and 1546 samples with known IDH status were used to train a feed-forward neural network and a logistic regression (LR) classifier within a nested cross-validation framework, followed by independent validation in the TCGA-LAML dataset. The LR model showed superior performance, achieving receiver operating characteristic area under the curve [Formula: see text] 0.994 [Formula: see text] 0.007, accuracy [Formula: see text] 0.983 [Formula: see text] 0.006, balanced accuracy [Formula: see text] 0.979 [Formula: see text] 0.005, sensitivity for the IDH-mutant (IDH-MUT) class [Formula: see text] 0.972 [Formula: see text] 0.010, and specificity [Formula: see text] 0.986 [Formula: see text] 0.008, and correctly classified all IDH-MUT cases in the independent cohort. Applying the final model to samples lacking annotations enabled reconstruction of IDH status for 4148 AML cases, expanding the number of molecularly characterized transcriptomes available for downstream analyses. Predicted groups recapitulated known IDH-associated transcriptional signatures, supporting biological validity. This work demonstrates that IDH mutation status can be accurately inferred from transcriptomic data alone and provides a scalable framework to recover missing genomic annotations, thereby enhancing the utility of public AML resources for large-scale biological and translational research.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag058"},"PeriodicalIF":3.0,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13244157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212630","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}

引用次数: 0

TSProm: deep learning framework to predict tissue-specific regulatory logic. TSProm：预测组织特异性调控逻辑的深度学习框架。

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NAR Genomics and Bioinformatics Pub Date : 2026-06-03 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag050

Pallavi Surana, Pratik Dutta, Nimisha Papineni, Rekha Sathian, Zhihan Zhou, Han Liu, Ramana V Davuluri

{"title":"TSProm: deep learning framework to predict tissue-specific regulatory logic.","authors":"Pallavi Surana, Pratik Dutta, Nimisha Papineni, Rekha Sathian, Zhihan Zhou, Han Liu, Ramana V Davuluri","doi":"10.1093/nargab/lqag050","DOIUrl":"10.1093/nargab/lqag050","url":null,"abstract":"Characterizing tissue-specific (TSp) gene expression is crucial for understanding development and disease; however, traditional expression-based methods often overlook the latent \"regulatory grammar\" embedded in non-coding DNA, particularly across distal promoter regions. Here, we introduce TSProm, a framework that adapts a DNA foundation model (DNABERT2) to decode the regulatory logic of TSp promoters at the isoform level. Our contributions are two-fold: (i) a comparative design that trains two specialized models: Model A for general promoter biology and Model B for tissue-specific regulation enabling precise isolation of sequence motifs surrounding the transcription start site that uniquely define tissue identity; (ii) we develop an explainable AI module that integrates attention-based motif discovery with model-agnostic SHAP analysis to yield cross-validated interpretations of learned features. Applying TSProm to human brain, liver, and testis promoters, we identified clinically relevant transcription factors (TFs) in the brain, including SP1, MYC, and HES6, whose associations with gliomas and neuroblastomas highlight clinical relevance. Moreover, our results highlight C2H2 zinc finger proteins as a dominant family shaping the global landscape of TSp gene regulation. TSProm provides an interpretable and generalizable framework for identifying tissue-specific regulatory elements, offering powerful computational tools to investigate gene regulation in both normal and disease contexts.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag050"},"PeriodicalIF":3.0,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148164870","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}

引用次数: 0

Interplay of the ribosome A and CAR sites. 核糖体A和CAR位点的相互作用。

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NAR Genomics and Bioinformatics Pub Date : 2026-06-03 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag055

Mitsu Raval, Yancheng Zhou, Miki Lynch, Daniel Krizanc, Kelly M Thayer, Michael P Weir

{"title":"Interplay of the ribosome A and CAR sites.","authors":"Mitsu Raval, Yancheng Zhou, Miki Lynch, Daniel Krizanc, Kelly M Thayer, Michael P Weir","doi":"10.1093/nargab/lqag055","DOIUrl":"10.1093/nargab/lqag055","url":null,"abstract":"Protein translation is a highly regulated process influenced by multiple factors at the initiation, elongation, and termination stages. One notable regulatory element of the ribosome is the CAR interaction surface, a three-residue motif in the structure of the ribosome composed of C1274 and A1427 of Saccharomyces cerevisiae 18S rRNA (corresponding to C1054 and A1196 in Escherichia coli 16S rRNA) and R146 of ribosomal protein Rps3. CAR is highly conserved and positioned adjacent to the amino-acyl (A site) decoding center. It establishes hydrogen bonds with the +1 codon next in line to enter the ribosome A site, acting as an extension of the transfer RNA (tRNA) anticodon and forming base-stacking interactions with nucleotide 34 of the tRNA. However, despite CAR's enzymatically strategic positioning within the ribosome, its functional relationship with the A site remains poorly characterized. Using molecular dynamics simulations, we examined the interplay between the A site and CAR site, revealing sequence-dependent modulation of H-bonding and π-stacking interactions within and between the two sites. These findings highlight the interplay between the A site and CAR site, suggesting a structural and functional connection between these two regions of the ribosome that may contribute to messenger RNA sequence-specific tuning of translation elongation.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag055"},"PeriodicalIF":3.0,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13231172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148164921","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}

引用次数: 0

TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification. TF-DWGNet：用于多组学癌症亚型分类的张量融合有向加权图神经网络。

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NAR Genomics and Bioinformatics Pub Date : 2026-05-30 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag054

Tiantian Yang, Zhiqian Chen

{"title":"TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.","authors":"Tiantian Yang, Zhiqian Chen","doi":"10.1093/nargab/lqag054","DOIUrl":"10.1093/nargab/lqag054","url":null,"abstract":"Integration and analysis of multi-omics data provide valuable insights for improving cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Graph neural networks provide a principled framework for modeling these structures, but existing approaches often rely on prior knowledge or predefined similarity networks that produce either undirected or unweighted graphs, failing to capture task-specific directionality and interaction strengths. Interpretability at both the modality and feature levels also remains limited. To address these challenges, we propose \"TF-DWGNet,\" a novel Graph Neural Network framework that combines tree-based Directed Weighted graph construction with Tensor Fusion for multiclass cancer subtype classification. TF-DWGNet introduces two key methodological innovations: (i) a supervised tree-based strategy that constructs directed weighted graphs tailored to each omics modality, and (ii) a tensor fusion mechanism that captures unimodal, bimodal, and trimodal interactions using low-rank decomposition for computational efficiency. Experiments on three real-world cancer datasets demonstrate that TF-DWGNet consistently outperforms state-of-the-art baselines across multiple metrics and statistical tests. In addition, the model provides biologically meaningful insights through modality level contribution scores and ranked feature importance. These results highlight that TF-DWGNet is an effective and interpretable solution for multi-omics integration in cancer research.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag054"},"PeriodicalIF":3.0,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13221656/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139065","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}

引用次数: 0

Whole-genome sequencing with AVITI and NovaSeq X Plus reveals comparable performance with contextual biases. AVITI和NovaSeq X Plus的全基因组测序显示出与上下文偏差相当的性能。

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NAR Genomics and Bioinformatics Pub Date : 2026-05-26 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag053

Pontus Höjer, Johannes Alneberg, Pär Lundin, Tom Martin, Julia Hauenstein, Helena Fällmar, Magnus Lindell, Christian Natanaelsson, Susana Häggqvist, Adam Ameur, Jessica Nordlund, Robert Månsson Welinder

{"title":"Whole-genome sequencing with AVITI and NovaSeq X Plus reveals comparable performance with contextual biases.","authors":"Pontus Höjer, Johannes Alneberg, Pär Lundin, Tom Martin, Julia Hauenstein, Helena Fällmar, Magnus Lindell, Christian Natanaelsson, Susana Häggqvist, Adam Ameur, Jessica Nordlund, Robert Månsson Welinder","doi":"10.1093/nargab/lqag053","DOIUrl":"10.1093/nargab/lqag053","url":null,"abstract":"Element Biosciences' avidity sequencing has emerged as a competing technology to Illumina's short-read sequencing platform. Prior benchmarks of avidity sequencing have not included the latest Illumina NovaSeq X/X Plus instruments with XLEAP chemistry. Here, we have run polymerase chain reaction-free whole-genome sequencing on four human tumor cell lines using both Illumina NovaSeq X Plus and Element AVITI instruments. AVITI showed low duplication rates and reported higher base qualities; the latter contributed to improved mapping confidence and fewer spurious variant candidates. Both platforms were found to be highly comparable when benchmarking variant calling, with AVITI only providing a minor improvement on INDELs at lower coverages. Stratifying by genomic context revealed further differences, where AVITI genome coverage and variant calls were superior in high-GC regions while being inferior in GC homopolymers. Error-rate analysis highlighted further differences between the platforms; in particular, AVITI in some instances displayed an increased error rate on read 2 related to short fragments. AVITI error rate was also found to be more stable downstream of repetitive regions, except for GC homopolymers. We further found that AVITI sequencing was sensitive to G-quadruplex motifs. Overall, despite these identified differences, both platforms performed highly comparable for variant analysis.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag053"},"PeriodicalIF":3.0,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13202175/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044016","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}

引用次数: 0

From gene correlations to cell clusters: COTAN improved scRNA-seq analysis. 从基因相关性到细胞簇：COTAN改进了scRNA-seq分析。

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NAR Genomics and Bioinformatics Pub Date : 2026-05-21 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag048

Silvia Giulia Galfrè, Marco Fantozzi, Alina Sîrbu, Irene Testa, Matteo Tolloso, Andrea Alberti, Corrado Priami, Francesco Morandin

{"title":"From gene correlations to cell clusters: COTAN improved scRNA-seq analysis.","authors":"Silvia Giulia Galfrè, Marco Fantozzi, Alina Sîrbu, Irene Testa, Matteo Tolloso, Andrea Alberti, Corrado Priami, Francesco Morandin","doi":"10.1093/nargab/lqag048","DOIUrl":"10.1093/nargab/lqag048","url":null,"abstract":"Single-cell RNA-sequencing (scRNA-seq) now routinely profiles thousands of individual transcriptomes, yet no consensus pipeline exists for either laboratory protocol or downstream analysis. Droplet-based datasets are especially challenging: their count matrices are extremely sparse and many biologically important genes are expressed at very low levels. Standard workflows, borrowed from bulk RNA-seq, apply normalization, log-transformation, and feature filtering, steps that can distort true-zero counts and systematically discard low-expression regulators. A method that natively handles sparsity, avoids imputation, and retains informative low-abundance genes is therefore desirable. We present an extended version of the COTAN workflow, a framework based on gene correlations that models zero counts directly, requires no log-normalization or scaling, and is tailored to UMI droplet data. COTAN's key features are (i) robust gene-gene correlation matrices, validated on diverse public datasets, (ii) a superior detection of differentially expressed genes, including those with very low expression, consistently outperforming leading tools, (iii) a highly sensitive statistical scoring, able to detect heterogeneous cell groups and to confirm whether a cluster is truly transcriptomically uniform. Together, these advances provide a biologically grounded, end-to-end solution for scRNA-seq analysis. COTAN is available in Bioconductor and on GitHub. Supplementary information is available at this site.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag048"},"PeriodicalIF":3.0,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13191298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148017749","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}

引用次数: 0

Novel examples of NMD escape through alternative intronic polyadenylation. NMD通过选择性内含子聚腺苷化逃逸的新例子。

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NAR Genomics and Bioinformatics Pub Date : 2026-05-20 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag052

Maria Vlasenok, Antonina Kuznetsova, Dmitry A Skvortsov, Olga A Dontsova, Dmitri D Pervouchine

{"title":"Novel examples of NMD escape through alternative intronic polyadenylation.","authors":"Maria Vlasenok, Antonina Kuznetsova, Dmitry A Skvortsov, Olga A Dontsova, Dmitri D Pervouchine","doi":"10.1093/nargab/lqag052","DOIUrl":"10.1093/nargab/lqag052","url":null,"abstract":"The nonsense-mediated messenger RNA decay (NMD) surveillance system detects and selectively eliminates transcripts with premature stop codons. A stop codon is considered premature if it is followed by an exon-exon junction >50 nucleotides downstream. Pruning of the 3'-untranslated region containing such junctions through alternative polyadenylation may provide a mechanism of NMD escape. Here, we systematically examine a subclass of poison exons that carry a premature stop codon for the presence of polyadenylation sites (PAS) in the downstream intron. Using data from the GTEx consortium, we observed that poison exons are more often followed by an active polyadenylation site compared with cassette exons. We also identified tissue-specific switches between NMD-targeted and NMD-escape isoforms in several human genes, including the vaccinia-related kinase VRK3, nuclear transcription factor NFX1, Notch pathway regulator TM2D3, and RNA helicase DDX31. Blocking the cleavage and PAS in these genes using antisense oligonucleotides in human cells led to a switch from NMD-escape to NMD-target isoform, accompanied by a decrease in gene expression levels. This study reveals that NMD escape via alternative polyadenylation is a widespread, yet currently overlooked post-transcriptional mechanism of gene expression regulation.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag052"},"PeriodicalIF":3.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13187750/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147989382","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}

引用次数: 0

WayFindR: investigating feedback in biological pathways. WayFindR：研究生物途径中的反馈。

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NAR Genomics and Bioinformatics Pub Date : 2026-05-19 eCollection Date: 2026-06-01 DOI: 10.1093/nargab/lqag051

Polina Bombina, Reginald L McGee Ii, Jake Reed, Zachary B Abrams, Lynne V Abruzzo, Kevin R Coombes

{"title":"WayFindR: investigating feedback in biological pathways.","authors":"Polina Bombina, Reginald L McGee Ii, Jake Reed, Zachary B Abrams, Lynne V Abruzzo, Kevin R Coombes","doi":"10.1093/nargab/lqag051","DOIUrl":"10.1093/nargab/lqag051","url":null,"abstract":"Understanding biological pathways requires more than static diagrams. We present WayFindR, an R package that converts pathway data from WikiPathways and KEGG into graph structures using igraph, enabling computational analysis of regulatory features such as negative feedback loops. Rooted in control theory, negative feedback is essential for system stability, yet it is often underrepresented in curated pathway data. In this study, we systematically analyzed pathway information from both databases across multiple species and found that feedback loops-particularly negative ones-are rarely captured. This gap likely reflects both biological and technical challenges. Biologically, feedback mechanisms are inherently complex and often remain uncharted due to limited experimental focus. Technically, pathway databases frequently lack standardized annotations and complete representations of regulatory interactions, especially inhibitory edges that are crucial for identifying feedback. These observations underscore the need for improved data curation and consistent annotation practices to enhance our understanding of regulatory dynamics. By bridging the gap between static pathway diagrams and dynamic system-level insights, WayFindR enables reproducible and scalable investigation of feedback regulation in cellular networks. The WayFindR R package can be downloaded from the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/WayFindR/index.html). The processed data along with code for download can be accessed via the GitLab repository (https://gitlab.com/krcoombes/wayfindr).","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"8 2","pages":"lqag051"},"PeriodicalIF":3.0,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147983576","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}

引用次数: 0