Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq
暂无分享,去创建一个
James D. Johnson | W. Kraus | P. Kapranov | C. Wahlestedt | J. Timmons | A. Pitsillides | I. Gallagher | S. Phillips | C. Volmar | H. Cen | Tanner Stokes
[1] Jonathan M. Mudge,et al. Standardized annotation of translated open reading frames , 2022, Nature Biotechnology.
[2] C. N. Thawng,et al. A transcriptome software comparison for the analyses of treatments expected to give subtle gene expression responses , 2022, BMC Genomics.
[3] A. Brunner,et al. Unbiased spatial proteomics with single-cell resolution in tissues. , 2022, Molecular cell.
[4] R. Moots,et al. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial , 2022, Nature Medicine.
[5] N. Samani,et al. Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure , 2022, European journal of heart failure.
[6] C. Ponting,et al. Genome-Wide Analysis of Human Long Noncoding RNAs: A Provocative Review. , 2022, Annual review of genomics and human genetics.
[7] Hagen U. Tilgner,et al. Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore Technologies reveals platform-specific error patterns , 2022, Genome research.
[8] J. Li,et al. Exaggerated false positives by popular differential expression methods when analyzing human population samples , 2022, Genome Biology.
[9] Hagen U. Tilgner,et al. Single-nuclei isoform RNA sequencing unlocks barcoded exon connectivity in frozen brain tissue , 2022, Nature Biotechnology.
[10] M. Guttman,et al. Xist spatially amplifies SHARP/SPEN recruitment to balance chromosome-wide silencing and specificity to the X chromosome , 2022, Nature Structural & Molecular Biology.
[11] M. Ziemann,et al. Urgent need for consistent standards in functional enrichment analysis , 2022, PLoS Comput. Biol..
[12] Huanming Yang,et al. Distinct biological ages of organs and systems identified from a multi-omics study. , 2022, Cell reports.
[13] L. Pachter,et al. RNA velocity unraveled , 2022, bioRxiv.
[14] Rick B. Vega,et al. Skeletal muscle transcriptome response to a bout of endurance exercise in physically active and sedentary older adults. , 2022, American journal of physiology. Endocrinology and metabolism.
[15] J. Li,et al. Statistics or biology: the zero-inflation controversy about scRNA-seq data , 2022, Genome Biology.
[16] Francisca Rojas Ringeling,et al. Partitioning RNAs by length improves transcriptome reconstruction from short-read RNA-seq data , 2022, Nature Biotechnology.
[17] A. Wingo,et al. Large-scale deep multi-layer analysis of Alzheimer’s disease brain reveals strong proteomic disease-related changes not observed at the RNA level , 2021, Nature Neuroscience.
[18] L. Pachter,et al. Museum of spatial transcriptomics , 2020, Nature Methods.
[19] OUP accepted manuscript , 2022, Nucleic Acids Research.
[20] S. Mukherjee,et al. The accuracy of absolute differential abundance analysis from relative count data , 2021, bioRxiv.
[21] L. Pachter,et al. The specious art of single-cell genomics , 2021, bioRxiv.
[22] Lin Gao,et al. Evaluation and comparison of multi-omics data integration methods for cancer subtyping , 2021, PLoS Comput. Biol..
[23] Yvonne A. Evrard,et al. TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository , 2021, Journal of Translational Medicine.
[24] Jonathan L. Robinson,et al. BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq , 2021, Genome biology.
[25] Neha J. Pagidipati,et al. Metabolomic profiling identifies complex lipid species and amino acid analogues associated with response to weight loss interventions , 2021, PloS one.
[26] Daniel Hebenstreit,et al. Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision , 2021, Briefings Bioinform..
[27] Shaurya Jauhari,et al. Popularity and performance of bioinformatics software: the case of gene set analysis , 2021, BMC Bioinform..
[28] Christopher D. Brown,et al. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease , 2021, Cell.
[29] Matthew E. Ritchie,et al. The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools , 2021, NAR genomics and bioinformatics.
[30] L. Ferrucci,et al. Skeletal muscle transcriptome in healthy aging , 2021, Nature Communications.
[31] J. Fitzgibbon,et al. Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs , 2021, Nature Communications.
[32] P. Kapranov,et al. Hovlinc is a recently evolved class of ribozyme found in human lncRNA , 2021, Nature Chemical Biology.
[33] K. Garg,et al. The Role of Innate and Adaptive Immune Cells in Skeletal Muscle Regeneration , 2021, International journal of molecular sciences.
[34] L. Elo,et al. Exon-level estimates improve the detection of differentially expressed genes in RNA-seq studies , 2021, RNA biology.
[35] K. Kadota,et al. Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data , 2020, BMC Bioinform..
[36] Ryan Gosselin,et al. Current RNA-seq methodology reporting limits reproducibility , 2019, Briefings Bioinform..
[37] OUP accepted manuscript , 2021, Briefings In Bioinformatics.
[38] OUP accepted manuscript , 2021, Nucleic Acids Research.
[39] Nathan A. Bihlmeyer,et al. Novel plasma biomarkers improve discrimination of metabolic health independent of weight , 2020, Scientific Reports.
[40] Esti Yeger-Lotem,et al. Dosage-sensitive molecular mechanisms are associated with the tissue-specificity of traits and diseases , 2020, Computational and structural biotechnology journal.
[41] May D. Wang,et al. Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction , 2020, Scientific Reports.
[42] J. Mar,et al. Metformin alters skeletal muscle transcriptome adaptations to resistance training in older adults , 2020, Aging.
[43] Kayla A Johnson,et al. Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data , 2020, Genome Biology.
[44] C. Wahlestedt,et al. Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States , 2020, Cell reports.
[45] Wenxian Yang,et al. Performance evaluation of lossy quality compression algorithms for RNA-seq data , 2020, BMC Bioinformatics.
[46] Eva Friedel,et al. Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies , 2020, BMC Bioinformatics.
[47] Anthony J. Payne,et al. A Multi-omic Integrative Scheme Characterizes Tissues of Action at Loci Associated with Type 2 Diabetes , 2020, bioRxiv.
[48] Yongmei Zhao,et al. Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples. , 2020, Journal of visualized experiments : JoVE.
[49] R. Sandberg,et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3 , 2020, Nature Biotechnology.
[50] Zhan Ye,et al. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols , 2020, RNA.
[51] Monika S. Kowalczyk,et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods , 2020, Nature Biotechnology.
[52] Arfa Mehmood,et al. Systematic evaluation of differential splicing tools for RNA-seq studies , 2019, Briefings Bioinform..
[53] Christopher D. Brown,et al. A Quantitative Proteome Map of the Human Body , 2019, Cell.
[54] L. S. Churchman,et al. Splicing Kinetics and Coordination Revealed by Direct Nascent RNA Sequencing through Nanopores. , 2019, Molecular cell.
[55] P. Boutros,et al. A transcriptome-based signature of pathological angiogenesis predicts breast cancer patient survival , 2019, PLoS genetics.
[56] M. Shahjaman,et al. Robust identification of differentially expressed genes from RNA-seq data. , 2019, Genomics.
[57] J. Li,et al. AIDE: annotation-assisted isoform discovery with high precision , 2019, Genome research.
[58] O. Elroy-Stein,et al. Recurrent functional misinterpretation of RNA-seq data caused by sample-specific gene length bias , 2019, PLoS biology.
[59] Ian McQuillan,et al. Size matters: how sample size affects the reproducibility and specificity of gene set analysis , 2019, Human Genomics.
[60] S. Subramaniam,et al. Skeletal muscle: A review of molecular structure and function, in health and disease , 2019, Wiley interdisciplinary reviews. Systems biology and medicine.
[61] W. Kraus,et al. A statistical and biological response to an informatics appraisal of healthy aging gene signatures , 2019, Genome Biology.
[62] S. Kummerfeld,et al. Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes , 2019, Cell reports.
[63] J. Hadfield,et al. RNA sequencing: the teenage years , 2019, Nature Reviews Genetics.
[64] J. Kirk,et al. Systematic evaluation of RNA-Seq preparation protocol performance , 2019, BMC Genomics.
[65] I. Voutsadakis,et al. The Oncotype Dx Assay in ER-Positive, HER2-Negative Breast Cancer Patients: A Real Life Experience from a Single Cancer Center. , 2019, European journal of breast health.
[66] W. Kraus,et al. Longevity‐related molecular pathways are subject to midlife “switch” in humans , 2019, Aging cell.
[67] Robert Olaso,et al. Systematic analysis of TruSeq, SMARTer and SMARTer Ultra-Low RNA-seq kits for standard, low and ultra-low quantity samples , 2019, Scientific Reports.
[68] J. Vilo,et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) , 2019, Nucleic Acids Res..
[69] M. Budoff,et al. The Clinical Utility of a Precision Medicine Blood Test Incorporating Age, Sex, and Gene Expression for Evaluating Women with Stable Symptoms Suggestive of Obstructive Coronary Artery Disease: Analysis from the PRESET Registry , 2019, Journal of women's health.
[70] Benjamin A. Logsdon,et al. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age , 2019, Nature Communications.
[71] Alireza Hadj Khodabakhshi,et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets , 2019, Nature Communications.
[72] M. Laakso,et al. Nine Amino Acids Are Associated With Decreased Insulin Secretion and Elevated Glucose Levels in a 7.4-Year Follow-up Study of 5,181 Finnish Men , 2019, Diabetes.
[73] T. Derrien,et al. Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules , 2019, Scientific Reports.
[74] P. Khaitovich,et al. LINC00116 codes for a mitochondrial peptide linking respiration and lipid metabolism , 2019, Proceedings of the National Academy of Sciences.
[75] Terence P. Speed,et al. Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements , 2018, Bioinform..
[76] M. Pellegrini,et al. A comparison between whole transcript and 3’ RNA sequencing methods using Kapa and Lexogen library preparation methods , 2019, BMC Genomics.
[77] Nicholas J. Lyons,et al. Drug and disease signature integration identifies synergistic combinations in glioblastoma , 2018, Nature Communications.
[78] R. Irizarry,et al. Missing data and technical variability in single‐cell RNA‐sequencing experiments , 2018, Biostatistics.
[79] I. Mohr,et al. Direct RNA sequencing on nanopore arrays redefines the transcriptional complexity of a viral pathogen , 2018, bioRxiv.
[80] Claes Wahlestedt,et al. A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease , 2018, Nucleic acids research.
[81] C. Wilke,et al. Limitations of alignment-free tools in total RNA-seq quantification , 2018, BMC Genomics.
[82] Z. Shkedy,et al. The Usage of Exon-Exon Splice Junctions for the Detection of Alternative Splicing using the REIDS model , 2018, Scientific Reports.
[83] Christian G Bien,et al. Systematic evaluation of RNA quality, microarray data reliability and pathway analysis in fresh, fresh frozen and formalin-fixed paraffin-embedded tissue samples , 2018, Scientific Reports.
[84] Martin Jaeger,et al. Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses , 2018, Nature Immunology.
[85] J. Eberwine,et al. The successes and future prospects of the linear antisense RNA amplification methodology , 2018, Nature Protocols.
[86] D. Warton. Why you cannot transform your way out of trouble for small counts , 2018, Biometrics.
[87] J. Andersen,et al. Rapid switch‐off of the human myosin heavy chain IIX gene after heavy load muscle contractions is sustained for at least four days , 2018, Scandinavian journal of medicine & science in sports.
[88] J. Mar,et al. Metformin regulates metabolic and nonmetabolic pathways in skeletal muscle and subcutaneous adipose tissues of older adults , 2018, Aging cell.
[89] Hans-Ulrich Klein,et al. Descriptor : A multi-omic atlas of the human frontal cortex for aging and Alzheimer ’ s disease research , 2018 .
[90] Z. Weng,et al. Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers , 2018, BMC Genomics.
[91] Ravi Iyengar,et al. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. , 2017, Cell systems.
[92] Nuno A. Fonseca,et al. Expression Atlas: gene and protein expression across multiple studies and organisms , 2017, Nucleic Acids Res..
[93] Johanna Hardin,et al. Selecting between‐sample RNA‐Seq normalization methods from the perspective of their assumptions , 2016, Briefings Bioinform..
[94] D. Voora,et al. Peripheral blood gene expression signatures which reflect smoking and aspirin exposure are associated with cardiovascular events , 2018, BMC Medical Genomics.
[95] Heiner Koch,et al. The target landscape of clinical kinase drugs , 2017, Science.
[96] J. Shendure,et al. DNA sequencing at 40: past, present and future , 2017, Nature.
[97] Nicola J. Rinaldi,et al. Enhancing GTEx by bridging the gaps between genotype, gene expression, and disease , 2017, Nature Genetics.
[98] M. Trotter,et al. Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm , 2017, bioRxiv.
[99] Lior Pachter,et al. Gene-level differential analysis at transcript-level resolution , 2017, Genome Biology.
[100] C. Lindskog,et al. A pathology atlas of the human cancer transcriptome , 2017, Science.
[101] Judy H. Cho,et al. Transcriptional Risk Scores link GWAS to eQTL and Predict Complications in Crohn's Disease , 2017, Nature Genetics.
[102] Ira W. Deveson,et al. The Dimensions, Dynamics, and Relevance of the Mammalian Noncoding Transcriptome. , 2017, Trends in genetics : TIG.
[103] G. Dittmar,et al. RNA sequencing and transcriptome arrays analyses show opposing results for alternative splicing in patient derived samples , 2017, BMC Genomics.
[104] D. Bajorin,et al. Global Cancer Transcriptome Quantifies Repeat Element Polarization between Immunotherapy Responsive and T Cell Suppressive Classes , 2017, bioRxiv.
[105] Jacob K. Asiedu,et al. The Drug Repurposing Hub: a next-generation drug library and information resource , 2017, Nature Medicine.
[106] M. Biggin,et al. Quantitating translational control: mRNA abundance-dependent and independent contributions and the mRNA sequences that specify them , 2017, bioRxiv.
[107] Rickey E Carter,et al. Enhanced Protein Translation Underlies Improved Metabolic and Physical Adaptations to Different Exercise Training Modes in Young and Old Humans. , 2017, Cell metabolism.
[108] Geet Duggal,et al. Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference , 2017, Nature Methods.
[109] S. Heath,et al. A Comparison of RNA-Seq Results from Paired Formalin-Fixed Paraffin-Embedded and Fresh-Frozen Glioblastoma Tissue Samples , 2017, PloS one.
[110] A. Strömberg,et al. A reverse genetics cell-based evaluation of genes linked to healthy human tissue age , 2016, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.
[111] Lior Pachter,et al. Differential analysis of RNA-seq incorporating quantification uncertainty , 2016, Nature Methods.
[112] J. Timmons. Molecular Diagnostics of Ageing and Tackling Age-related Disease. , 2017, Trends in pharmacological sciences.
[113] Charles C. Kim,et al. Empirical assessment of analysis workflows for differential expression analysis of human samples using RNA-Seq , 2017, BMC Bioinformatics.
[114] Natalia Castro,et al. Human Pancreatic β Cell lncRNAs Control Cell-Specific Regulatory Networks , 2016, bioRxiv.
[115] N. Mizushima,et al. An Autophagic Flux Probe that Releases an Internal Control. , 2016, Molecular cell.
[116] Andrew D. Johnson,et al. Peripheral Blood Transcriptomic Signatures of Fasting Glucose and Insulin Concentrations , 2016, Diabetes.
[117] J. Timmons,et al. Biomarkers of browning of white adipose tissue and their regulation during exercise- and diet-induced weight loss12 , 2016, The American journal of clinical nutrition.
[118] Laura J. Scott,et al. The genetic regulatory signature of type 2 diabetes in human skeletal muscle , 2016, Nature Communications.
[119] Á. Rubio,et al. EventPointer: an effective identification of alternative splicing events using junction arrays , 2016, BMC Genomics.
[120] J. Timmons,et al. Molecular studies of exercise, skeletal muscle, and ageing , 2016, F1000Research.
[121] Sara M. Willems,et al. Genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease , 2016 .
[122] H. Wathieu,et al. DrugGenEx-Net: a novel computational platform for systems pharmacology and gene expression-based drug repurposing , 2016, BMC Bioinformatics.
[123] I. Gottesman,et al. Epigenetic assimilation in the aging human brain , 2016, Genome Biology.
[124] Krzysztof J. Szkop,et al. iGEMS: an integrated model for identification of alternative exon usage events , 2016, Nucleic acids research.
[125] Lior Pachter,et al. Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.
[126] J. Carpten,et al. Translating RNA sequencing into clinical diagnostics: opportunities and challenges , 2016, Nature Reviews Genetics.
[127] Weida Tong,et al. Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine , 2016, Pharmaceutics.
[128] Daniel J. Gaffney,et al. A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.
[129] G. Davey Smith. Commentary: Known knowns and known unknowns in medical research: James Mackenzie meets Donald Rumsfeld. , 2016, International journal of epidemiology.
[130] Mark D. Robinson,et al. Isoform prefiltering improves performance of count-based methods for analysis of differential transcript usage , 2016, Genome Biology.
[131] Bin Zhang,et al. Multiscale Embedded Gene Co-expression Network Analysis , 2015, PLoS Comput. Biol..
[132] H. Shon,et al. Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data , 2015, BMC Bioinformatics.
[133] Krzysztof J. Szkop,et al. Multiple sources of bias confound functional enrichment analysis of global -omics data , 2015, Genome Biology.
[134] W. Kraus,et al. A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status , 2015, Genome Biology.
[135] Mick Watson,et al. Errors in RNA-Seq quantification affect genes of relevance to human disease , 2015, Genome Biology.
[136] J. Sarkaria,et al. The Bromodomain protein BRD4 controls HOTAIR, a long noncoding RNA essential for glioblastoma proliferation , 2015, Proceedings of the National Academy of Sciences.
[137] R. Minghim,et al. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams , 2015, BMC Bioinformatics.
[138] Dmitri D. Pervouchine,et al. The human transcriptome across tissues and individuals , 2015, Science.
[139] Mark D. Biggin,et al. Statistics requantitates the central dogma , 2015, Science.
[140] Shanrong Zhao,et al. A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification , 2015, BMC Genomics.
[141] Laura L. Elo,et al. Comparison of software packages for detecting differential expression in RNA-seq studies , 2013, Briefings Bioinform..
[142] M. Mann,et al. Cell-type-resolved quantitative proteomics of murine liver. , 2014, Cell metabolism.
[143] Jeffrey T. Leek,et al. Preserving biological heterogeneity with a permuted surrogate variable analysis for genomics batch correction , 2014, Bioinform..
[144] David P. Kreil,et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance , 2014, Nature Biotechnology.
[145] Sheng Li,et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study , 2014, Nature Biotechnology.
[146] David P. Kreil,et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium , 2014, Nature Biotechnology.
[147] Philip Beineke,et al. Biological and Analytical Stability of a Peripheral Blood Gene Expression Score for Obstructive Coronary Artery Disease in the PREDICT and COMPASS Studies , 2014, Journal of Cardiovascular Translational Research.
[148] H. Lehrach,et al. Influence of RNA extraction methods and library selection schemes on RNA-seq data , 2014, BMC Genomics.
[149] Michael B. Black,et al. IVT-seq reveals extreme bias in RNA sequencing , 2014, Genome Biology.
[150] Donald Sharon,et al. Defining a personal, allele-specific, and single-molecule long-read transcriptome , 2014, Proceedings of the National Academy of Sciences.
[151] Robert E. W. Hancock,et al. NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration , 2014, Nucleic Acids Res..
[152] J. Bergh,et al. Multi‐level gene expression signatures, but not binary, outperform Ki67 for the long term prognostication of breast cancer patients , 2014, Molecular oncology.
[153] C. Thermes,et al. Library preparation methods for next-generation sequencing: tone down the bias. , 2014, Experimental cell research.
[154] C. Schaaf. Nicotinic acetylcholine receptors in human genetic disease , 2014, Genetics in Medicine.
[155] S. P. Fodor,et al. Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations , 2014, Proceedings of the National Academy of Sciences.
[156] John D McPherson,et al. Robust global microRNA expression profiling using next-generation sequencing technologies , 2014, Laboratory Investigation.
[157] Andreas Krämer,et al. Causal analysis approaches in Ingenuity Pathway Analysis , 2013, Bioinform..
[158] Pierre Baldi,et al. Muscle insulin sensitivity and glucose metabolism are controlled by the intrinsic muscle clock , 2013, Molecular metabolism.
[159] D. Koller,et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals , 2013, Genome research.
[160] P. Bickel,et al. System wide analyses have underestimated protein abundances and the importance of transcription in mammals , 2012, PeerJ.
[161] N. Cox,et al. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines , 2014, Genome Biology.
[162] J. Harrow,et al. Assessment of transcript reconstruction methods for RNA-seq , 2013, Nature Methods.
[163] Donald Sharon,et al. A single-molecule long-read survey of the human transcriptome , 2013, Nature Biotechnology.
[164] James R. Bain,et al. Metabolomics Reveals Unexpected Responses to Oral Glucose , 2013, Diabetes.
[165] Hugues Bersini,et al. Batch effect removal methods for microarray gene expression data integration: a survey , 2013, Briefings Bioinform..
[166] C. Bouchard,et al. Molecular Networks of Human Muscle Adaptation to Exercise and Age , 2013, PLoS genetics.
[167] David G Hendrickson,et al. Differential analysis of gene regulation at transcript resolution with RNA-seq , 2012, Nature Biotechnology.
[168] Damian Szklarczyk,et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..
[169] Anders Berglund,et al. Iterative rank-order normalization of gene expression microarray data , 2013, BMC Bioinformatics.
[170] M. McCarthy,et al. Human β cell transcriptome analysis uncovers lncRNAs that are tissue-specific, dynamically regulated, and abnormally expressed in type 2 diabetes. , 2012, Cell metabolism.
[171] Yan Lin,et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection , 2012, Bioinform..
[172] T. Hashimshony,et al. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.
[173] Peter K. Davidsen,et al. Is irisin a human exercise gene? , 2012, Nature.
[174] Y. Benjamini,et al. Summarizing and correcting the GC content bias in high-throughput sequencing , 2012, Nucleic acids research.
[175] K. Hansen,et al. Removing technical variability in RNA-seq data using conditional quantile normalization , 2012, Biostatistics.
[176] Phillip E. C. Compeau,et al. Why are de Bruijn graphs useful for genome assembly? , 2011, Nature Biotechnology.
[177] Matko Bosnjak,et al. REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms , 2011, PloS one.
[178] Paulo P. Amaral,et al. The Reality of Pervasive Transcription , 2011, PLoS biology.
[179] H. Steven Wiley,et al. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling , 2011, Bioinform..
[180] Kees-Jan Françoijs,et al. Linear amplification for deep sequencing , 2011, Nature Protocols.
[181] K. Hansen,et al. Sequencing technology does not eliminate biological variability , 2011, Nature Biotechnology.
[182] Lee T. Sam,et al. A Comparison of Single Molecule and Amplification Based Sequencing of Cancer Transcriptomes , 2011, PloS one.
[183] John D. Storey,et al. Human transcriptome array for high-throughput clinical studies , 2011, Proceedings of the National Academy of Sciences.
[184] Claude Bouchard,et al. A transcriptional map of the impact of endurance exercise training on skeletal muscle phenotype. , 2011, Journal of applied physiology.
[185] A. Vaag,et al. Insulin resistance induced by physical inactivity is associated with multiple transcriptional changes in skeletal muscle in young men. , 2010, American journal of physiology. Endocrinology and metabolism.
[186] E. Wang,et al. Analysis and design of RNA sequencing experiments for identifying isoform regulation , 2010, Nature Methods.
[187] Claude Bouchard,et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. , 2010, Journal of applied physiology.
[188] Andrew Williams,et al. Cross-platform analysis of global microRNA expression technologies , 2010, BMC Genomics.
[189] Cole Trapnell,et al. Role of Rodent Secondary Motor Cortex in Value-based Action Selection Nih Public Access Author Manuscript , 2006 .
[190] Colin N. Dewey,et al. RNA-Seq gene expression estimation with read mapping uncertainty , 2009, Bioinform..
[191] M. Metzker. Sequencing technologies — the next generation , 2010, Nature Reviews Genetics.
[192] Claes Wahlestedt,et al. Integration of microRNA changes in vivo identifies novel molecular features of muscle insulin resistance in type 2 diabetes , 2010, Genome Medicine.
[193] Lars Lundell,et al. Using transcriptomics to identify and validate novel biomarkers of human skeletal muscle cancer cachexia , 2010, Genome Medicine.
[194] Dorothy D. Sears,et al. Mechanisms of human insulin resistance and thiazolidinedione-mediated insulin sensitization , 2009, Proceedings of the National Academy of Sciences.
[195] A. Oshlack,et al. Transcript length bias in RNA-seq data confounds systems biology , 2009, Biology Direct.
[196] D. Stephan,et al. Genetic control of human brain transcript expression in Alzheimer disease. , 2009, American journal of human genetics.
[197] J. Bergh,et al. Stromal signature identifies basal breast cancers , 2009, Nature Network Boston.
[198] M. Gerstein,et al. RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.
[199] Mark D. Robinson,et al. Differential splicing using whole-transcript microarrays , 2009, BMC Bioinformatics.
[200] J. Timmons,et al. Dysregulation of Mitochondrial Dynamics and the Muscle Transcriptome in ICU Patients Suffering from Sepsis Induced Multiple Organ Failure , 2008, PloS one.
[201] B. Frey,et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing , 2008, Nature Genetics.
[202] Nancy F. Hansen,et al. Accurate Whole Human Genome Sequencing using Reversible Terminator Chemistry , 2008, Nature.
[203] Heidi Ledford,et al. The death of microarrays? , 2008, Nature.
[204] Eric T. Wang,et al. Alternative Isoform Regulation in Human Tissue Transcriptomes , 2008, Nature.
[205] M. Stephens,et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. , 2008, Genome research.
[206] Marcel H. Schulz,et al. A Global View of Gene Activity and Alternative Splicing by Deep Sequencing of the Human Transcriptome , 2008, Science.
[207] Mark D. Robinson,et al. FIRMA: a method for detection of alternative splicing from exon array data , 2008, Bioinform..
[208] Andrew B. Nobel,et al. Merging two gene-expression studies via cross-platform normalization , 2008, Bioinform..
[209] P. Stadler,et al. RNA Maps Reveal New RNA Classes and a Possible Function for Pervasive Transcription , 2007, Science.
[210] Peter Bühlmann,et al. Analyzing gene expression data in terms of gene sets: methodological issues , 2007, Bioinform..
[211] J. Davis. Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2007 .
[212] Robert Clarke,et al. Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data , 2006, Bioinform..
[213] C. Wahlestedt,et al. Expression profiling following local muscle inactivity in humans provides new perspective on diabetes-related genes. , 2006, Genomics.
[214] R. Myers,et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data , 2005, Nucleic acids research.
[215] Carl Johan Sundberg,et al. Modulation of extracellular matrix genes reflects the magnitude of physiological adaptation to aerobic exercise training in humans , 2005, BMC Biology.
[216] E Jansson,et al. VEGF-A splice variants and related receptor expression in human skeletal muscle following submaximal exercise. , 2005, Journal of applied physiology.
[217] Claes Wahlestedt,et al. Considerations when using the significance analysis of microarrays (SAM) algorithm , 2005, BMC Bioinformatics.
[218] G. Helt,et al. Transcriptional Maps of 10 Human Chromosomes at 5-Nucleotide Resolution , 2005, Science.
[219] Claes Wahlestedt,et al. Human muscle gene expression responses to endurance training provide a novel perspective on Duchenne muscular dystrophy , 2005, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.
[220] Benjamin M. Bolstad,et al. affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..
[221] Philipp Kapranov,et al. Beyond expression profiling: next generation uses of high density oligonucleotide arrays. , 2003, Briefings in functional genomics & proteomics.
[222] T. Speed,et al. Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.
[223] Terence P. Speed,et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..
[224] S. P. Fodor,et al. Large-Scale Transcriptional Activity in Chromosomes 21 and 22 , 2002, Science.
[225] T. Poggio,et al. Multiclass cancer diagnosis using tumor gene expression signatures , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[226] Thomas D. Schmittgen,et al. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.
[227] R. Tibshirani,et al. Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[228] James L. Winkler,et al. Accessing Genetic Information with High-Density DNA Arrays , 1996, Science.
[229] D. Lockhart,et al. Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.
[230] S. P. Fodor,et al. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. , 1994, Proceedings of the National Academy of Sciences of the United States of America.
[231] J. Eberwine,et al. Analysis of gene expression in single live neurons. , 1992, Proceedings of the National Academy of Sciences of the United States of America.
[232] L. Hood,et al. Large-scale and automated DNA sequence determination. , 1991, Science.
[233] L. M. Smith,et al. High speed DNA sequencing by capillary electrophoresis. , 1990, Nucleic acids research.
[234] J. Eberwine,et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA. , 1990, Proceedings of the National Academy of Sciences of the United States of America.
[235] Lloyd M. Smith,et al. Fluorescence detection in automated DNA sequence analysis , 1986, Nature.
[236] F. Sanger,et al. DNA sequencing with chain-terminating inhibitors. , 1977, Proceedings of the National Academy of Sciences of the United States of America.
[237] J. T. Madison,et al. Structure of a Ribonucleic Acid , 1965, Science.