Aging is associated with a systemic length-associated transcriptome imbalance

[1]  M. Levine,et al.  A complex systems approach to aging biology , 2022, Nature Aging.

[2]  Jianzhi Zhang,et al.  Synonymous mutations in representative yeast genes are mostly strongly non-neutral , 2022, Nature.

[3]  S. Horvath,et al.  Biological age is increased by stress and restored upon recovery , 2022, bioRxiv.

[4]  A. Stewart,et al.  Heat shock induces premature transcript termination and reconfigures the human transcriptome , 2022, Molecular cell.

[5]  Handan Melike Dönertaş,et al.  Inter-tissue convergence of gene expression during ageing suggests age-related loss of tissue and cellular identity , 2022, bioRxiv.

[6]  R. Bernards,et al.  The Cancer SENESCopedia: A delineation of cancer cell senescence , 2021, Cell reports.

[7]  Martin Jinye Zhang,et al.  Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures , 2021, eLife.

[8]  J. de Magalhães,et al.  Gene Size Matters: An Analysis of Gene Length in the Human Genome , 2021, Frontiers in Genetics.

[9]  Margarita V. Meer,et al.  Reprogramming to recover youthful epigenetic information and restore vision , 2020, Nature.

[10]  Daniel A. Skelly,et al.  Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney , 2020, bioRxiv.

[11]  Irving L. Weissman,et al.  A single-cell transcriptomic atlas characterizes ageing tissues in the mouse , 2020, Nature.

[12]  James T. Webber,et al.  Aging hallmarks exhibit organ-specific temporal signatures , 2020, Nature.

[13]  B. Kennedy,et al.  The quest to slow ageing through drug discovery , 2020, Nature Reviews Drug Discovery.

[14]  Ryan L. Collins,et al.  The mutational constraint spectrum quantified from variation in 141,456 humans , 2020, Nature.

[15]  Christina A. Sanford,et al.  Whole transcriptome analysis and validation of metabolic pathways in subcutaneous adipose tissues during FGF21-induced weight loss in non-human primates , 2020, Scientific Reports.

[16]  Daniel-Cosmin Marcu,et al.  Trade-off between somatic and germline repair in a vertebrate supports the expensive germ line hypothesis , 2020, Proceedings of the National Academy of Sciences.

[17]  Jessica M. Hoffman,et al.  Transcriptomic and metabolomic profiling of long-lived growth hormone releasing hormone knock-out mice: evidence for altered mitochondrial function and amino acid metabolism , 2020, Aging.

[18]  Sven Nahnsen,et al.  The nf-core framework for community-curated bioinformatics pipelines , 2020, Nature Biotechnology.

[19]  Martin Jinye Zhang,et al.  Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures Revision 1 , 2019, bioRxiv.

[20]  David R. Kelley,et al.  Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging , 2019, Genome research.

[21]  Eric C. Griffith,et al.  MeCP2 Represses the Rate of Transcriptional Initiation of Highly Methylated Long Genes. , 2019, Molecular cell.

[22]  J. Sznajder,et al.  Impaired phagocytic function in CX3CR1+ tissue-resident skeletal muscle macrophages prevents muscle recovery after influenza A virus-induced pneumonia in aged mice , 2019, bioRxiv.

[23]  David A. Knowles,et al.  Genetic regulation of gene expression and splicing during a 10-year period of human aging , 2019, Genome Biology.

[24]  D. Glass,et al.  Age-Related Gene Expression Signature in Rats Demonstrate Early, Late, and Linear Transcriptional Changes from Multiple Tissues. , 2019, Cell reports.

[25]  P. Khaitovich,et al.  Identification and Application of Gene Expression Signatures Associated with Lifespan Extension. , 2019, Cell metabolism.

[26]  B. Waclaw,et al.  Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic , 2019, Proceedings of the National Academy of Sciences.

[27]  M. Hande,et al.  Genome-Wide Transcriptome Analysis Reveals Intermittent Fasting-Induced Metabolic Rewiring in the Liver , 2019, Dose-response : a publication of International Hormesis Society.

[28]  Adam Freund.  Untangling Aging Using Dynamic, Organism-Level Phenotypic Networks. , 2019, Cell systems.

[29]  Fedor A. Kolpakov,et al.  GTRD: a database on gene transcription regulation—2019 update , 2018, Nucleic Acids Res..

[30]  Luís A. Nunes Amaral,et al.  Large-scale investigation of the reasons why potentially important genes are ignored , 2018, PLoS biology.

[31]  P. Verstreken,et al.  A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain , 2018, Cell.

[32]  Param Priya Singh,et al.  Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses , 2018, bioRxiv.

[33]  T. Abe,et al.  Loss of Sfpq Causes Long-Gene Transcriptopathy in the Brain. , 2018, Cell reports.

[34]  Dominique T. Alessi,et al.  Interpretation of biological experiments changes with evolution of the Gene Ontology and its annotations , 2018, Scientific Reports.

[35]  F. Hartl,et al.  Pathways of cellular proteostasis in aging and disease , 2018, The Journal of cell biology.

[36]  Nuno A. Fonseca,et al.  Expression Atlas: gene and protein expression across multiple studies and organisms , 2017, Nucleic Acids Res..

[37]  João Pedro de Magalhães,et al.  Human Ageing Genomic Resources: new and updated databases , 2017, Nucleic Acids Res..

[38]  A. Cellerino,et al.  What have we learned on aging from omics studies? , 2017, Seminars in cell & developmental biology.

[39]  A. Shilatifard,et al.  Monocyte-derived alveolar macrophages drive lung fibrosis and persist in the lung over the life span , 2017, The Journal of experimental medicine.

[40]  Vikki M. Weake,et al.  Transcriptional Signatures of Aging. , 2017, Journal of molecular biology.

[41]  W. Kaelin,et al.  Common pitfalls in preclinical cancer target validation , 2017, Nature Reviews Cancer.

[42]  Vanessa Sochat,et al.  Singularity: Scientific containers for mobility of compute , 2017, PloS one.

[43]  Paolo Di Tommaso,et al.  Nextflow enables reproducible computational workflows , 2017, Nature Biotechnology.

[44]  M. Mann,et al.  Widespread Proteome Remodeling and Aggregation in Aging C. elegans , 2017, Cell.

[45]  Sol Katzman,et al.  Modulation of nonsense mediated decay by rapamycin , 2016, Nucleic acids research.

[46]  C. Molony,et al.  Genomic Characterization of Metformin Hepatic Response , 2016, PLoS genetics.

[47]  Ashish Rajput,et al.  Systematic analysis of the gerontome reveals links between aging and age-related diseases , 2016, Human molecular genetics.

[48]  J. Pennings,et al.  Diet restriction delays accelerated aging and genomic stress in DNA repair deficient mice , 2016, Nature.

[49]  D. Chalopin,et al.  Insights into Sex Chromosome Evolution and Aging from the Genome of a Short-Lived Fish , 2015, Cell.

[50]  R. Morimoto,et al.  The biology of proteostasis in aging and disease. , 2015, Annual review of biochemistry.

[51]  G. Barton,et al.  How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? , 2015, RNA.

[52]  Dmitri D. Pervouchine,et al.  The human transcriptome across tissues and individuals , 2015, Science.

[53]  T. Serre,et al.  Reduced Expression of MYC Increases Longevity and Enhances Healthspan , 2015, Cell.

[54]  Marc Vidal,et al.  A chaperome subnetwork safeguards proteostasis in aging and neurodegenerative disease. , 2014, Cell reports.

[55]  E. Schadt,et al.  Geroscience: Linking Aging to Chronic Disease , 2014, Cell.

[56]  Xiaowei Wang,et al.  miRDB: an online resource for microRNA target prediction and functional annotations , 2014, Nucleic Acids Res..

[57]  I. Yanai,et al.  Gene length and expression level shape genomic novelties , 2014, Genome research.

[58]  Paul Theodor Pyl,et al.  HTSeq—a Python framework to work with high-throughput sequencing data , 2014, bioRxiv.

[59]  Lei Li,et al.  Transcriptome profiling of CTLs regulated by rapamycin using RNA-Seq , 2014, Immunogenetics.

[60]  R. Cabo,et al.  The Search for Antiaging Interventions: From Elixirs to Fasting Regimens , 2014, Cell.

[61]  Y. Gilad,et al.  RNA-seq: impact of RNA degradation on transcript quantification , 2014, BMC Biology.

[62]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[63]  M. Mattson,et al.  Communication breakdown: The impact of ageing on synapse structure , 2014, Ageing Research Reviews.

[64]  Lucas Pelkmans,et al.  Predicting functional gene interactions with the hierarchical interaction score , 2013, Nature Methods.

[65]  B. Kennedy,et al.  Late‐life rapamycin treatment reverses age‐related heart dysfunction , 2013, Aging cell.

[66]  L. Partridge,et al.  The Hallmarks of Aging , 2013, Cell.

[67]  Wei Shi,et al.  featureCounts: an efficient general purpose program for assigning sequence reads to genomic features , 2013, Bioinform..

[68]  Cole Trapnell,et al.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.

[69]  J. Epstein,et al.  Postnatal development- and age-related changes in DNA-methylation patterns in the human genome , 2012, Nucleic acids research.

[70]  K. Hansen,et al.  Removing technical variability in RNA-seq data using conditional quantile normalization , 2012, Biostatistics.

[71]  F. Slack,et al.  MicroRNAs and their roles in aging , 2012, Journal of Cell Science.

[72]  Sandrine Dudoit,et al.  GC-Content Normalization for RNA-Seq Data , 2011, BMC Bioinformatics.

[73]  X. Cui,et al.  Length bias correction for RNA-seq data in gene set analyses , 2011, Bioinform..

[74]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[75]  Matthew D. Young,et al.  Gene ontology analysis for RNA-seq: accounting for selection bias , 2010, Genome Biology.

[76]  Davis J. McCarthy,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[77]  D. Weiskopf,et al.  The aging of the immune system , 2009, Transplant international : official journal of the European Society for Organ Transplantation.

[78]  Janet M. Thornton,et al.  Ribosomal Protein S6 Kinase 1 Signaling Regulates Mammalian Life Span , 2009, Science.

[79]  Gonçalo R. Abecasis,et al.  The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..

[80]  A. Oshlack,et al.  Transcript length bias in RNA-seq data confounds systems biology , 2009, Biology Direct.

[81]  G. Church,et al.  Meta-analysis of age-related gene expression profiles identifies common signatures of aging , 2009, Bioinform..

[82]  Kenneth L. Hensley,et al.  An aging Interventions Testing Program: study design and interim report , 2007, Aging cell.

[83]  Xiaohua Hu,et al.  Average gene length is highly conserved in prokaryotes and eukaryotes and diverges only between the two kingdoms. , 2006, Molecular biology and evolution.

[84]  João Pedro de Magalhães,et al.  HAGR: the Human Ageing Genomic Resources , 2004, Nucleic Acids Res..

[85]  Kazuo Yagi,et al.  Gene expression profile of long‐lived Ames dwarf mice and Little mice , 2004, Aging cell.

[86]  Daryi Wang,et al.  A general tendency for conservation of protein length across eukaryotic kingdoms. , 2004, Molecular biology and evolution.

[87]  B. Chatterjee,et al.  Impacts of transcriptional regulation on aging and senescence , 2002, Ageing Research Reviews.

[88]  A. Richardson,et al.  The transcription of various types of ribonucleic acid by hepatocytes isolated from rats of various ages. , 1982, Journal of gerontology.

[89]  E. Uemura.  Age-related changes in neuronal RNA content in rhesus monkeys (Macaca mulatta) , 1980, Brain Research Bulletin.

[90]  H. Quastler,et al.  An hypothesis concerning RNA metabolism and aging. , 1962, Proceedings of the National Academy of Sciences of the United States of America.

[91]  D. G. Beech,et al.  The Advanced Theory of Statistics. Volume 2: Inference and Relationship. , 1962 .

[92]  V. J. Wulff,et al.  Age-related reduction of the RNA content of rat cardiac muscle and cerebellum. , 1961, Archives of biochemistry and biophysics.

[93]  T. K. Doktor,et al.  Next generation sequencing of RNA reveals novel targets of resveratrol with possible implications for Canavan disease. , 2019, Molecular genetics and metabolism.

[94]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[95]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[96]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[97]  Frederick R. Forst,et al.  On robust estimation of the location parameter , 1980 .

[98]  R. Fisher.  014: On the "Probable Error" of a Coefficient of Correlation Deduced from a Small Sample. , 1921 .