{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T10:44:46Z","timestamp":1791801886961,"version":"4.3.4"},"reference-count":88,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan Key Laboratory of Modern Analytical Mathematics and Applications","award":["202302AN360007"],"award-info":[{"award-number":["202302AN360007"]}]},{"name":"Modern Applied Mathematics and Life Sciences","award":["02405AS350003"],"award-info":[{"award-number":["02405AS350003"]}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["P50 AG016574"],"award-info":[{"award-number":["P50 AG016574"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["R01 AG032990"],"award-info":[{"award-number":["R01 AG032990"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["U01 AG046139"],"award-info":[{"award-number":["U01 AG046139"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["R01 AG018023"],"award-info":[{"award-number":["R01 AG018023"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["U01 AG006576"],"award-info":[{"award-number":["U01 AG006576"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["U01 AG006786"],"award-info":[{"award-number":["U01 AG006786"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["R01 AG025711"],"award-info":[{"award-number":["R01 AG025711"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["R01 AG017216"],"award-info":[{"award-number":["R01 AG017216"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"NIA","doi-asserted-by":"publisher","award":["R01 AG003949"],"award-info":[{"award-number":["R01 AG003949"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"NINDS","doi-asserted-by":"publisher","award":["R01 NS080820"],"award-info":[{"award-number":["R01 NS080820"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CurePSP Foundation"},{"DOI":"10.13039\/100007048","name":"Mayo Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007048","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>RNA-Seq data analysis is commonly biased towards detecting differentially expressed genes and insufficiently conveys the complexity of gene expression changes between biological conditions. This bias arises because discrete count models cannot fully and independently parameterize the mean, variance, and skewness of gene expression distributions. Therefore, a unified statistical framework that simultaneously tests differential expression, variability, and skewness is needed. We present SIEVEseq, a statistical methodology that provides such a framework. SIEVEseq embraces a compositional data analysis strategy to transform discrete RNA-Seq counts into continuous form with a distribution well-fitted by the skew-normal distribution. Both parametric and nonparametric simulations show that SIEVEseq better controls the false discovery rate and Type II error than existing differential expression methods. Analysis of the Mayo RNA-Seq dataset for Alzheimer's disease demonstrates that gene sets with significant differences in mean, variance, and skewness between control and disease groups strongly predict disease state. Furthermore, functional enrichment analysis indicates that relying solely on differentially expressed genes identifies only part of the biological spectrum, whereas incorporating genes with differential variability and skewness reveals additional disease-related aspects. Cross-data and cross-methodology validation suggest the detected biological signals are genuine. The SIEVEseq R package is available at https:\/\/cran.r-project.org\/web\/packages\/SIEVEseq.<\/jats:p>","DOI":"10.1093\/dnares\/dsag012","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T11:16:30Z","timestamp":1785237390000},"source":"Crossref","is-referenced-by-count":0,"title":["SIEVEseq: unified differential expression, variability, and skewness analyses using RNA-Seq data"],"prefix":"10.1093","volume":"33","author":[{"given":"Hongxiang","family":"Li","sequence":"first","affiliation":[{"name":"School of Mathematics, Yunnan Normal University , Kunming, Yunnan 650500 ,","place":["P.R. China"]},{"name":"Yunnan Key Laboratory of Modern Analytical Mathematics and Applications , Yunnan Normal University, Kunming, Yunnan 650500 ,","place":["P.R. China"]},{"name":"Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya , Kuala Lumpur 50603 ,","place":["Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsung Fei","family":"Khang","sequence":"additional","affiliation":[{"name":"Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya , Kuala Lumpur 50603 ,","place":["Malaysia"]},{"name":"Universiti Malaya Centre for Data Analytics, Universiti Malaya , Kuala Lumpur 50603 ,","place":["Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"2026101205465905500_dsag012-B1","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1038\/nature04844","article-title":"Increased cell-to-cell variation in gene expression in ageing mouse heart","volume":"441","author":"Bahar","year":"2006","journal-title":"Nature"},{"key":"2026101205465905500_dsag012-B2","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1038\/ng1094","article-title":"Natural variation in human gene expression assessed in lymphoblastoid cells","volume":"33","author":"Cheung","year":"2003","journal-title":"Nat Genet"},{"key":"2026101205465905500_dsag012-B3","doi-asserted-by":"publisher","first-page":"13266","DOI":"10.1073\/pnas.221465998","article-title":"Project normal: defining normal variance in mouse gene expression","volume":"98","author":"Pritchard","year":"2001","journal-title":"Proc Natl Acad Sci U S A."},{"key":"2026101205465905500_dsag012-B4","doi-asserted-by":"publisher","first-page":"i390","DOI":"10.1093\/bioinformatics\/btn142","article-title":"Differential variability analysis of gene expression and its application to human diseases","volume":"24","author":"Ho","year":"2008","journal-title":"Bioinformatics"},{"key":"2026101205465905500_dsag012-B5","doi-asserted-by":"publisher","first-page":"e1002207","DOI":"10.1371\/journal.pgen.1002207","article-title":"Variance of gene expression identifies altered network constraints in neurological disease","volume":"7","author":"Mar","year":"2011","journal-title":"PLoS Genet"},{"key":"2026101205465905500_dsag012-B6","doi-asserted-by":"publisher","first-page":"1241013","DOI":"10.1142\/S0219720012410132","article-title":"Beyond comparing means: the usefulness of analyzing interindividual variation in gene expression for identifying genes associated with cancer development","volume":"10","author":"Gorlov","year":"2012","journal-title":"J Bioinform Comput Biol"},{"key":"2026101205465905500_dsag012-B7","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1186\/1471-2105-13-272","article-title":"Gene expression anti-profiles as a basis for accurate universal cancer signatures","volume":"13","author":"Bravo","year":"2012","journal-title":"BMC Bioinformatics"},{"key":"2026101205465905500_dsag012-B8","doi-asserted-by":"publisher","first-page":"e38919","DOI":"10.1371\/journal.pone.0038919","article-title":"Modeling skewness in human transcriptomes","volume":"7","author":"Casellas","year":"2012","journal-title":"PLoS One"},{"key":"2026101205465905500_dsag012-B9","doi-asserted-by":"publisher","first-page":"e46935","DOI":"10.1371\/journal.pone.0046935","article-title":"Non-Gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes","volume":"7","author":"Marko","year":"2012","journal-title":"PLoS One"},{"key":"2026101205465905500_dsag012-B10","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1186\/s12859-019-3252-0","article-title":"Investigating skewness to understand gene expression heterogeneity in large patient cohorts","volume":"20","author":"Church","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2026101205465905500_dsag012-B11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s12551-018-0494-4","article-title":"The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond","volume":"11","author":"Mar","year":"2019","journal-title":"Biophys Rev"},{"key":"2026101205465905500_dsag012-B12","doi-asserted-by":"publisher","first-page":"2881","DOI":"10.1093\/bioinformatics\/btm453","article-title":"Moderated statistical tests for assessing differences in tag abundance","volume":"23","author":"Robinson","year":"2007","journal-title":"Bioinformatics"},{"key":"2026101205465905500_dsag012-B13","doi-asserted-by":"publisher","first-page":"4288","DOI":"10.1093\/nar\/gks042","article-title":"Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation","volume":"40","author":"McCarthy","year":"2012","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B14","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol"},{"key":"2026101205465905500_dsag012-B15","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.copbio.2013.03.010","article-title":"The details in the distributions: why and how to study phenotypic variability","volume":"24","author":"Geiler-Samerotte","year":"2013","journal-title":"Curr Opin Biotechnol"},{"key":"2026101205465905500_dsag012-B16","doi-asserted-by":"publisher","first-page":"e170","DOI":"10.1093\/nar\/gkq670","article-title":"A two-parameter generalized Poisson model to improve the analysis of RNA-seq data","volume":"38","author":"Srivastava","year":"2010","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B17","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1186\/1471-2105-14-254","article-title":"A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-Seq experiments","volume":"14","author":"Esnaola","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026101205465905500_dsag012-B18","doi-asserted-by":"publisher","first-page":"R29","DOI":"10.1186\/gb-2014-15-2-r29","article-title":"Voom: precision weights unlock linear model analysis tools for RNA-seq read counts","volume":"15","author":"Law","year":"2014","journal-title":"Genome Biol"},{"key":"2026101205465905500_dsag012-B19","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/0-387-29362-0_23","volume-title":"Bioinformatics and computational biology solutions using R and Bioconductor","author":"Smyth","year":"2005"},{"key":"2026101205465905500_dsag012-B20","doi-asserted-by":"publisher","first-page":"lqaa093","DOI":"10.1093\/nargab\/lqaa093","article-title":"Dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate","volume":"2","author":"Gauthier","year":"2020","journal-title":"NAR Genom Bioinform"},{"key":"2026101205465905500_dsag012-B21","doi-asserted-by":"publisher","first-page":"e140","DOI":"10.1093\/nar\/gkv711","article-title":"Data quality aware analysis of differential expression in RNA-seq with NOISeq R\/Bioc package","volume":"43","author":"Tarazona","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B22","doi-asserted-by":"publisher","first-page":"2213","DOI":"10.1101\/gr.124321.111","article-title":"Differential expression in RNA-seq: a matter of depth","volume":"21","author":"Tarazona","year":"2011","journal-title":"Genome Res"},{"key":"2026101205465905500_dsag012-B23","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1177\/0962280211428386","article-title":"Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data","volume":"22","author":"Li","year":"2013","journal-title":"Stat Methods Med Res"},{"key":"2026101205465905500_dsag012-B24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s13059-022-02648-4","article-title":"Exaggerated false positives by popular differential expression methods when analyzing human population samples","volume":"23","author":"Li","year":"2022","journal-title":"Genome Biol"},{"key":"2026101205465905500_dsag012-B25","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1186\/s13059-014-0465-4","article-title":"DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging","volume":"15","author":"Phipson","year":"2014","journal-title":"Genome Biol"},{"key":"2026101205465905500_dsag012-B26","doi-asserted-by":"publisher","first-page":"e127","DOI":"10.1093\/nar\/gkx456","article-title":"Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq","volume":"45","author":"Ran","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B27","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1111\/j.1467-9876.2005.00510.x","article-title":"Generalized additive models for location, scale and shape","volume":"54","author":"Rigby","year":"2005","journal-title":"J R Stat Soc Ser C Appl Stat"},{"key":"2026101205465905500_dsag012-B28","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1152\/physiolgenomics.00128.2018","article-title":"Gene expression variability: the other dimension in transcriptome analysis","volume":"51","author":"de Jong","year":"2019","journal-title":"Physiol Genomics"},{"key":"2026101205465905500_dsag012-B29","doi-asserted-by":"publisher","first-page":"lqab124","DOI":"10.1093\/nargab\/lqab124","article-title":"Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability","volume":"4","author":"Roberts","year":"2022","journal-title":"NAR Genom Bioinform"},{"key":"2026101205465905500_dsag012-B30","doi-asserted-by":"publisher","first-page":"e16126","DOI":"10.7717\/peerj.16126","article-title":"clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution","volume":"11","author":"Li","year":"2023","journal-title":"PeerJ"},{"key":"2026101205465905500_dsag012-B31","doi-asserted-by":"publisher","first-page":"e1005428","DOI":"10.1371\/journal.pgen.1005428","article-title":"Variability of gene expression identifies transcriptional regulators of early human embryonic development","volume":"11","author":"Hasegawa","year":"2015","journal-title":"PLoS Genet"},{"key":"2026101205465905500_dsag012-B32","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1093\/biostatistics\/kxp003","article-title":"A note on oligonucleotide expression values not being normally distributed","volume":"10","author":"Hardin","year":"2009","journal-title":"Biostatistics"},{"key":"2026101205465905500_dsag012-B33","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1186\/1471-2105-11-576","article-title":"Validation and characterization of DNA microarray gene expression data distribution and associated moments","volume":"11","author":"Thomas","year":"2010","journal-title":"BMC Bioinformatics"},{"key":"2026101205465905500_dsag012-B34","doi-asserted-by":"publisher","first-page":"e67019","DOI":"10.1371\/journal.pone.0067019","article-title":"ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq","volume":"8","author":"Fernandes","year":"2013","journal-title":"PLoS One"},{"key":"2026101205465905500_dsag012-B35","doi-asserted-by":"crossref","unstructured":"Fernandes AD \u00a0et al \u00a0Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome. 2014:2:15. 10.1186\/2049-2618-2-15","DOI":"10.1186\/2049-2618-2-15"},{"key":"2026101205465905500_dsag012-B36","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1186\/s12859-018-2261-8","article-title":"Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. Log-ratio transformation-based methods","volume":"19","author":"Quinn","year":"2018","journal-title":"BMC Bioinformatics"},{"key":"2026101205465905500_dsag012-B37","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1016\/j.jspi.2006.04.011","article-title":"How robust are tests for two independent samples?","volume":"137","author":"Rasch","year":"2007","journal-title":"J Stat Plan Inference"},{"key":"2026101205465905500_dsag012-B38","doi-asserted-by":"crossref","DOI":"10.1007\/978-94-009-4109-0","volume-title":"The statistical analysis of compositional data","author":"Aitchison","year":"1986"},{"key":"2026101205465905500_dsag012-B39","first-page":"171","article-title":"A class of distributions which includes the normal ones","volume":"12","author":"Azzalini","year":"1985","journal-title":"Scand J Stat"},{"key":"2026101205465905500_dsag012-B40","volume-title":"The skew-normal and related families","author":"Azzalini","year":"2014"},{"key":"2026101205465905500_dsag012-B41","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1016\/j.jspi.2012.06.022","article-title":"Maximum penalized likelihood estimation for skew-normal and skew-t distributions","volume":"143","author":"Azzalini","year":"2013","journal-title":"J Stat Plan Inference"},{"key":"2026101205465905500_dsag012-B42","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1214\/aos\/1013699998","article-title":"The control of the false discovery rate in multiple testing under dependency","volume":"29","author":"Benjamini","year":"2001","journal-title":"Ann Stat"},{"key":"2026101205465905500_dsag012-B43","doi-asserted-by":"publisher","DOI":"10.1101\/2025.11.19.689306","article-title":"Transcriptomic module fingerprint reveals heterogeneity of whole blood transcriptome in type 1 diabetic patients","author":"Valentim","year":"2025","journal-title":"bioRxiv"},{"key":"2026101205465905500_dsag012-B44","doi-asserted-by":"publisher","first-page":"e9596","DOI":"10.15252\/msb.20209596","article-title":"Reduced proteasome activity in the aging brain results in ribosome stoichiometry loss and aggregation","volume":"16","author":"Kelmer Sacramento","year":"2020","journal-title":"Mol Syst Biol"},{"key":"2026101205465905500_dsag012-B45","doi-asserted-by":"publisher","first-page":"15151","DOI":"10.1038\/s41598-022-18506-z","article-title":"A resource for integrated genomic analysis of the human liver","volume":"12","author":"Zhou","year":"2022","journal-title":"Sci Rep"},{"key":"2026101205465905500_dsag012-B46","doi-asserted-by":"publisher","first-page":"6764","DOI":"10.1038\/s41467-023-42327-x","article-title":"Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma","volume":"14","author":"Dolgalev","year":"2023","journal-title":"Nat Commun"},{"key":"2026101205465905500_dsag012-B47","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1164\/rccm.202001-0134OC","article-title":"Chronic hypersensitivity pneumonitis, an interstitial lung disease with distinct molecular signatures","volume":"202","author":"Furusawa","year":"2020","journal-title":"Am J Respir Crit Care Med"},{"key":"2026101205465905500_dsag012-B48","doi-asserted-by":"publisher","first-page":"160089","DOI":"10.1038\/sdata.2016.89","article-title":"Human whole genome genotype and transcriptome data for Alzheimer\u2019s and other neurodegenerative diseases","volume":"3","author":"Allen","year":"2016","journal-title":"Sci Data"},{"key":"2026101205465905500_dsag012-B49","unstructured":"Azzalini \u00a0A\n          . The R package sn: the skew-normal and related distributions such as the skew-t and the SUN (version 2.1.0). Universit\u00e0 degli Studi di Padova; 2022. Home page: http:\/\/azzalini.stat.unipd.it\/SN\/. https:\/\/cran.r-project.org\/package=sn."},{"key":"2026101205465905500_dsag012-B50","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1177\/25424823241307878","article-title":"Blood RNA transcripts show changes in inflammation and lipid metabolism in Alzheimer's disease and mitochondrial function in mild cognitive impairment","volume":"8","author":"Iga","year":"2024","journal-title":"J Alzheimer's Dis Rep"},{"key":"2026101205465905500_dsag012-B51","doi-asserted-by":"publisher","first-page":"R25","DOI":"10.1186\/gb-2010-11-3-r25","article-title":"A scaling normalization method for differential expression analysis of RNA-seq data","volume":"11","author":"Robinson","year":"2010","journal-title":"Genome Biol"},{"key":"2026101205465905500_dsag012-B52","doi-asserted-by":"crossref","unstructured":"Frazee AC, Jaffe AE, Langmead B, Leek JT. Polyester: simulating RNA-seq datasets with differential transcript expression. Bioinformatics. 2015:31:2778\u20132784. 10.1093\/bioinformatics\/btv272","DOI":"10.1093\/bioinformatics\/btv272"},{"key":"2026101205465905500_dsag012-B53","doi-asserted-by":"crossref","unstructured":"Benidt S, Nettleton D. SimSeq: a nonparametric approach to simulation of RNA-sequence datasets. Bioinformatics. 2015:31:2131\u20132140. 10.1093\/bioinformatics\/btv124","DOI":"10.1093\/bioinformatics\/btv124"},{"key":"2026101205465905500_dsag012-B54","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1093\/bioinformatics\/btr671","article-title":"A novel significance score for gene selection and ranking","volume":"30","author":"Xiao","year":"2014","journal-title":"Bioinformatics"},{"key":"2026101205465905500_dsag012-B55","unstructured":"Loh \u00a0WY\n          . GUIDE (version 40.3). 2022. https:\/\/pages.stat.wisc.edu\/\u223cloh\/guide.html"},{"key":"2026101205465905500_dsag012-B56","doi-asserted-by":"publisher","first-page":"1710","DOI":"10.1214\/09-AOAS260","article-title":"Improving the precision of classification trees","volume":"3","author":"Loh","year":"2009","journal-title":"Ann Appl Stat"},{"key":"2026101205465905500_dsag012-B57","doi-asserted-by":"publisher","first-page":"W199","DOI":"10.1093\/nar\/gkz401","article-title":"WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs","volume":"47","author":"Liao","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B58","doi-asserted-by":"publisher","first-page":"W741","DOI":"10.1093\/nar\/gki475","article-title":"WebGestalt: an integrated system for exploring gene sets in various biological contexts","volume":"33","author":"Zhang","year":"2005","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B59","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc Natl Acad Sci U S A."},{"key":"2026101205465905500_dsag012-B60","unstructured":"R Core Team\n          . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; 2022. https:\/\/www.r-project.org\/."},{"key":"2026101205465905500_dsag012-B61","author":"Saurin"},{"key":"2026101205465905500_dsag012-B62","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s00018-015-2052-6","article-title":"The endoplasmic reticulum: structure, function and response to cellular signaling","volume":"73","author":"Schwarz","year":"2016","journal-title":"Cell Mol Life Sci"},{"key":"2026101205465905500_dsag012-B63","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1038\/s41419-022-05153-5","article-title":"ER stress and UPR in Alzheimer\u2019s disease: mechanisms, pathogenesis, treatments","volume":"13","author":"Ajoolabady","year":"2022","journal-title":"Cell Death Dis"},{"key":"2026101205465905500_dsag012-B64","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1126\/science.aar3607","article-title":"Defining the physiological role of SRP in protein-targeting efficiency and specificity","volume":"359","author":"Costa","year":"2018","journal-title":"Science"},{"key":"2026101205465905500_dsag012-B65","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s00216-016-9900-6","article-title":"Extracellular matrix proteomics in schizophrenia and Alzheimer\u2019s disease","volume":"409","author":"Sethi","year":"2017","journal-title":"Anal Bioanal Chem"},{"key":"2026101205465905500_dsag012-B66","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1111\/jnc.16067","article-title":"Perineuronal net deglycosylation associates with tauopathy-induced gliosis and neurodegeneration","volume":"168","author":"Logsdon","year":"2024","journal-title":"J Neurochem"},{"key":"2026101205465905500_dsag012-B67","doi-asserted-by":"publisher","first-page":"65","DOI":"10.2147\/DNND.S19829","article-title":"Cell adhesion molecules in Alzheimer\u2019s disease","volume":"2","author":"Wennstr\u00f6m","year":"2012","journal-title":"Degener Neurol Neuromuscul Dis"},{"key":"2026101205465905500_dsag012-B68","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1186\/1478-811X-12-23","article-title":"Signaling pathway cross talk in Alzheimer\u2019s disease","volume":"12","author":"Godoy","year":"2014","journal-title":"J Cell Commun Signal"},{"key":"2026101205465905500_dsag012-B69","doi-asserted-by":"publisher","first-page":"227","DOI":"10.3389\/fncel.2019.00227","article-title":"Wnt signaling deregulation in the aging and Alzheimer\u2019s brain","volume":"13","author":"Palomer","year":"2019","journal-title":"Front Cell Neurosci"},{"key":"2026101205465905500_dsag012-B70","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1016\/S0140-6736(03)12521-4","article-title":"Alzheimer\u2019s disease and angiogenesis","volume":"361","author":"Vagnucci","year":"2003","journal-title":"Lancet"},{"key":"2026101205465905500_dsag012-B71","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/alzrt230","article-title":"Adjusting the compass: new insights into the role of angiogenesis in Alzheimer\u2019s disease","volume":"5","author":"Jefferies","year":"2013","journal-title":"Alzheimers Res Ther"},{"key":"2026101205465905500_dsag012-B72","first-page":"43","article-title":"A derangement of the brain wound healing process may cause some cases of Alzheimer\u2019s disease","volume":"22","author":"Lehrer","year":"2016","journal-title":"Discov Med"},{"key":"2026101205465905500_dsag012-B73","doi-asserted-by":"publisher","first-page":"E1289","DOI":"10.1073\/pnas.1710329115","article-title":"Tau induces blood vessel abnormalities and angiogenesis-related gene expression in P301L transgenic mice and human Alzheimer\u2019s disease","volume":"115","author":"Bennett","year":"2018","journal-title":"Proc Natl Acad Sci U S A."},{"key":"2026101205465905500_dsag012-B74","doi-asserted-by":"publisher","first-page":"e2204581119","DOI":"10.1073\/pnas.2204581119","article-title":"Functionally linked potassium channel activity in cerebral endothelial and smooth muscle cells is compromised in Alzheimer\u2019s disease","volume":"119","author":"Taylor","year":"2022","journal-title":"Proc Natl Acad Sci U S A."},{"key":"2026101205465905500_dsag012-B75","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1038\/s41586-021-04369-3","article-title":"A human brain vascular atlas reveals diverse mediators of Alzheimer\u2019s risk","volume":"603","author":"Yang","year":"2022","journal-title":"Nature"},{"key":"2026101205465905500_dsag012-B76","doi-asserted-by":"publisher","first-page":"e52","DOI":"10.1371\/journal.pbio.0050052","article-title":"FMRP mediates mGluR5-dependent translation of amyloid precursor protein","volume":"5","author":"Westmark","year":"2007","journal-title":"PLoS Biol"},{"key":"2026101205465905500_dsag012-B77","doi-asserted-by":"publisher","first-page":"2276","DOI":"10.1093\/nar\/29.11.2276","article-title":"The fragile X mental retardation protein inhibits translation via interacting with mRNA","volume":"29","author":"Li","year":"2001","journal-title":"Nucleic Acids Res"},{"key":"2026101205465905500_dsag012-B78","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1038\/s41398-020-00882-7","article-title":"Alzheimer\u2019s disease-related dysregulation of mRNA translation causes key pathological features with ageing","volume":"10","author":"Ghosh","year":"2020","journal-title":"Transl Psychiatry"},{"key":"2026101205465905500_dsag012-B79","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1523\/JNEUROSCI.3029-15.2016","article-title":"Pathological tau promotes neuronal damage by impairing ribosomal function and decreasing protein synthesis","volume":"36","author":"Meier","year":"2016","journal-title":"J Neurosci"},{"key":"2026101205465905500_dsag012-B80","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/S0197-4580(00)00124-X","article-title":"Inflammation and Alzheimer\u2019s disease","volume":"21","author":"Akiyama","year":"2000","journal-title":"Neurobiol Aging"},{"key":"2026101205465905500_dsag012-B81","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.nbd.2009.10.006","article-title":"Inflammation, microglia, and Alzheimer\u2019s disease","volume":"37","author":"Cameron","year":"2010","journal-title":"Neurobiol Dis"},{"key":"2026101205465905500_dsag012-B82","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/S1474-4422(15)70016-5","article-title":"Neuroinflammation in Alzheimer\u2019s disease","volume":"14","author":"Heneka","year":"2015","journal-title":"Lancet Neurol"},{"key":"2026101205465905500_dsag012-B83","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.3389\/fimmu.2017.01905","article-title":"Neuroinflammation: microglia and T cells get ready to tango","volume":"8","author":"Schetters","year":"2018","journal-title":"Front Immunol"},{"key":"2026101205465905500_dsag012-B84","doi-asserted-by":"publisher","first-page":"339","DOI":"10.3389\/fimmu.2018.00339","article-title":"Influence of inflammation in the process of T lymphocyte differentiation: proliferative, metabolic, and oxidative changes","volume":"9","author":"Moro-Garc\u00eda","year":"2018","journal-title":"Front Immunol"},{"key":"2026101205465905500_dsag012-B85","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1016\/j.bbadis.2013.10.015","article-title":"Oxidative stress and mitochondrial dysfunction in Alzheimer\u2019s disease","volume":"1842","author":"Wang","year":"2014","journal-title":"Biochim Biophys Acta"},{"key":"2026101205465905500_dsag012-B86","doi-asserted-by":"publisher","first-page":"1219","DOI":"10.1016\/j.bbadis.2013.09.010","article-title":"The Alzheimer\u2019s disease mitochondrial cascade hypothesis: progress and perspectives","volume":"1842","author":"Swerdlow","year":"2014","journal-title":"Biochim Biophys Acta"},{"key":"2026101205465905500_dsag012-B87","doi-asserted-by":"publisher","first-page":"8433","DOI":"10.18632\/aging.102328","article-title":"Brain pyrimidine nucleotide synthesis and Alzheimer disease","volume":"11","author":"Pesini","year":"2019","journal-title":"Aging (Albany NY)."},{"key":"2026101205465905500_dsag012-B88","doi-asserted-by":"publisher","first-page":"1420","DOI":"10.1111\/febs.16344","article-title":"Lipid metabolism and Alzheimer\u2019s disease: clinical evidence, mechanistic link and therapeutic promise","volume":"290","author":"Yin","year":"2023","journal-title":"FEBS J"}],"container-title":["DNA Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/dnaresearch\/advance-article-pdf\/doi\/10.1093\/dnares\/dsag012\/70335322\/dsag012.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/dnaresearch\/article-pdf\/33\/4\/dsag012\/70335322\/dsag012.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/dnaresearch\/article-pdf\/33\/4\/dsag012\/70335322\/dsag012.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T09:47:09Z","timestamp":1791798429000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/dnaresearch\/article\/doi\/10.1093\/dnares\/dsag012\/8746671"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,27]]},"references-count":88,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7,27]]}},"URL":"https:\/\/doi.org\/10.1093\/dnares\/dsag012","relation":{},"ISSN":["1340-2838","1756-1663"],"issn-type":[{"value":"1340-2838","type":"print"},{"value":"1756-1663","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,8]]},"published":{"date-parts":[[2026,7,27]]},"article-number":"dsag012"}}