Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome‐wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short‐read RNA sequencing (RNA‐seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA‐seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA‐seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA‐seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA‐seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long‐read or single‐cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high‐density array data—to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

[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.