A comprehensive database for integrated analysis of omics data in autoimmune diseases

[1]  J. Dopazo,et al.  Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models , 2019, bioRxiv.

[2]  Panuwat Trairatphisan,et al.  From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL , 2019, npj Systems Biology and Applications.

[3]  Daniel Toro-Domínguez,et al.  ImaGEO: integrative gene expression meta-analysis from GEO database , 2018, Bioinform..

[4]  Alexander Lachmann,et al.  Mining data and metadata from the gene expression omnibus , 2018, Biophysical Reviews.

[5]  L. Rönnblom,et al.  An update on the role of type I interferons in systemic lupus erythematosus and Sjögren's syndrome , 2018, Current opinion in rheumatology.

[6]  Marta R. Hidalgo,et al.  Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. , 2018, Cancer research.

[7]  J. Sáez-Rodríguez,et al.  Benchmark and integration of resources for the estimation of human transcription factor activities , 2018, bioRxiv.

[8]  N. Pavlos,et al.  Rheumatoid arthritis: pathological mechanisms and modern pharmacologic therapies , 2018, Bone Research.

[9]  Jongho Kim,et al.  An integrated clinical and genomic information system for cancer precision medicine , 2018, BMC Medical Genomics.

[10]  M. Alarcón‐Riquelme,et al.  Moving towards a molecular taxonomy of autoimmune rheumatic diseases , 2018, Nature Reviews Rheumatology.

[11]  P. López,et al.  Heterogeneity of the Type I Interferon Signature in Rheumatoid Arthritis: A Potential Limitation for Its Use As a Clinical Biomarker , 2018, Front. Immunol..

[12]  J. Sáez-Rodríguez,et al.  Perturbation-response genes reveal signaling footprints in cancer gene expression , 2016, Nature Communications.

[13]  Kathleen M Jagodnik,et al.  Massive mining of publicly available RNA-seq data from human and mouse , 2017, Nature Communications.

[14]  Tom Heskes,et al.  RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets , 2017, Bioinform..

[15]  M. Alarcón‐Riquelme,et al.  Omics studies: their use in diagnosis and reclassification of SLE and other systemic autoimmune diseases. , 2016, Rheumatology.

[16]  Jinwei Chen,et al.  Application of omics in predicting anti-TNF efficacy in rheumatoid arthritis , 2017, Clinical Rheumatology.

[17]  Francisco Salavert,et al.  High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes , 2016, bioRxiv.

[18]  A. El-Osta,et al.  Gene name errors are widespread in the scientific literature , 2016, Genome Biology.

[19]  M. Kleinewietfeld,et al.  Environmental factors in autoimmune diseases and their role in multiple sclerosis , 2016, Cellular and Molecular Life Sciences.

[20]  Virginia Pascual,et al.  Personalized Immunomonitoring Uncovers Molecular Networks that Stratify Lupus Patients , 2016, Cell.

[21]  K. Kalunian,et al.  Sifalimumab, an anti-interferon-α monoclonal antibody, in moderate to severe systemic lupus erythematosus: a randomised, double-blind, placebo-controlled study , 2016, Annals of the rheumatic diseases.

[22]  Francisco Salavert,et al.  Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity , 2015, Scientific Reports.

[23]  A. Conesa,et al.  Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package , 2015, Nucleic acids research.

[24]  M. Peinado,et al.  Wanderer, an interactive viewer to explore DNA methylation and gene expression data in human cancer , 2015, Epigenetics & Chromatin.

[25]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[26]  Robert Petryszak,et al.  ArrayExpress update—simplifying data submissions , 2014, Nucleic Acids Res..

[27]  Daniel Toro-Domínguez,et al.  Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjögren’s syndrome uncovered through gene expression meta-analysis , 2014, Arthritis Research & Therapy.

[28]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[29]  M. Crow Type I Interferon in the Pathogenesis of Lupus , 2014, The Journal of Immunology.

[30]  Rafael A. Irizarry,et al.  Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays , 2014, Bioinform..

[31]  Hae-Rim Kim,et al.  Advances in Systems Biology Approaches for Autoimmune Diseases , 2014, Immune network.

[32]  Oliver S. Burren,et al.  A Type I Interferon Transcriptional Signature Precedes Autoimmunity in Children Genetically at Risk for Type 1 Diabetes , 2014, Diabetes.

[33]  C. Mohan,et al.  Systemic lupus erythematosus diagnostics in the 'omics' era. , 2013, International journal of clinical rheumatology.

[34]  K. Morris,et al.  Interferon-γ and systemic autoimmunity. , 2013, Discovery medicine.

[35]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[36]  Ruth Pidsley,et al.  A data-driven approach to preprocessing Illumina 450K methylation array data , 2013, BMC Genomics.

[37]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[38]  A. Peck,et al.  The Interferon-Signature of Sjögren’s Syndrome: How Unique Biomarkers Can Identify Underlying Inflammatory and Immunopathological Mechanisms of Specific Diseases , 2013, Front. Immunol..

[39]  L. Rönnblom,et al.  The interferon signature in autoimmune diseases , 2013, Current opinion in rheumatology.

[40]  R. Weksberg,et al.  Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray , 2013, Epigenetics.

[41]  Simon Yu,et al.  INTERFEROME v2.0: an updated database of annotated interferon-regulated genes , 2012, Nucleic Acids Res..

[42]  Francesco Marabita,et al.  A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data , 2012, Bioinform..

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

[44]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[45]  Colin N. Dewey,et al.  RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.

[46]  Wei Shi,et al.  Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips , 2010, Nucleic acids research.

[47]  M. Robinson,et al.  A scaling normalization method for differential expression analysis of RNA-seq data , 2010, Genome Biology.

[48]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[49]  E. Birney,et al.  Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt , 2009, Nature Protocols.

[50]  Virginia Pascual,et al.  A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. , 2008, Immunity.

[51]  Pan Du,et al.  lumi: a pipeline for processing Illumina microarray , 2008, Bioinform..

[52]  A. Bird,et al.  DNA methylation landscapes: provocative insights from epigenomics , 2008, Nature Reviews Genetics.

[53]  Sean R. Davis,et al.  GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor , 2007, Bioinform..

[54]  Bart De Moor,et al.  BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis , 2005, Bioinform..

[55]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[56]  G. Cooper,et al.  The epidemiology of autoimmune diseases. , 2003, Autoimmunity reviews.

[57]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[58]  M. R. Salaman A Two-step Hypothesis for the Appearance of Autoimmune Disease , 2003, Autoimmunity.

[59]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[60]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[61]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..