Reference standards for next-generation sequencing

[1]  T. Greiner,et al.  Opportunities and Challenges Associated with Clinical Diagnostic Genome Sequencing , 2019 .

[2]  Cory Y. McLean,et al.  Creating a universal SNP and small indel variant caller with deep neural networks , 2016, bioRxiv.

[3]  Rob Patro,et al.  Salmon provides fast and bias-aware quantification of transcript expression , 2017, Nature Methods.

[4]  Pingfang Liu,et al.  DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification , 2017, Science.

[5]  Valentine Svensson,et al.  Power Analysis of Single Cell RNA-Sequencing Experiments , 2016, Nature Methods.

[6]  John D Pfeifer,et al.  In Silico Proficiency Testing for Clinical Next-Generation Sequencing. , 2017, The Journal of molecular diagnostics : JMD.

[7]  Jefferey Chen,et al.  Sanger Confirmation Is Required to Achieve Optimal Sensitivity and Specificity in Next-Generation Sequencing Panel Testing. , 2016, The Journal of molecular diagnostics : JMD.

[8]  Birgit H Funke,et al.  Multiplexed Reference Materials as Controls for Diagnostic Next-Generation Sequencing: A Pilot Investigating Applications for Hypertrophic Cardiomyopathy. , 2016, The Journal of molecular diagnostics : JMD.

[9]  J. Korlach,et al.  De novo assembly and phasing of a Korean human genome , 2016, Nature.

[10]  M. Ante,et al.  SIRVs: Spike-In RNA Variants as External Isoform Controls in RNA-Sequencing , 2016, bioRxiv.

[11]  Ira W. Deveson,et al.  Spliced synthetic genes as internal controls in RNA sequencing experiments , 2016, Nature Methods.

[12]  Ted Wong,et al.  Representing genetic variation with synthetic DNA standards , 2016, Nature Methods.

[13]  Lisa Kalman,et al.  Assuring the Quality of Next-Generation Sequencing in Clinical Microbiology and Public Health Laboratories , 2016, Journal of Clinical Microbiology.

[14]  D. Posada,et al.  A comparison of tools for the simulation of genomic next-generation sequencing data , 2016, Nature Reviews Genetics.

[15]  Naomi S. Altman,et al.  Points of Significance: Classification evaluation , 2016, Nature Methods.

[16]  John D Pfeifer,et al.  A Model Study of In Silico Proficiency Testing for Clinical Next-Generation Sequencing. , 2016, Archives of pathology & laboratory medicine.

[17]  Dahui Qin,et al.  Multi-Institutional FASTQ File Exchange as a Means of Proficiency Testing for Next-Generation Sequencing Bioinformatics and Variant Interpretation. , 2016, The Journal of molecular diagnostics : JMD.

[18]  Levi C. T. Pierce,et al.  Deep sequencing of 10,000 human genomes , 2016, Proceedings of the National Academy of Sciences.

[19]  Rainer Spang,et al.  Adjusting microbiome profiles for differences in microbial load by spike-in bacteria , 2016, Microbiome.

[20]  G. McVean,et al.  A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree , 2016, bioRxiv.

[21]  Robin D Harrington,et al.  Plasmid-Based Materials as Multiplex Quality Controls and Calibrators for Clinical Next-Generation Sequencing Assays. , 2016, The Journal of molecular diagnostics : JMD.

[22]  J. Fuhrman,et al.  Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. , 2016, Environmental microbiology.

[23]  Lior Pachter,et al.  Near-optimal probabilistic RNA-seq quantification , 2016, Nature Biotechnology.

[24]  G. Friedlander,et al.  Using Synthetic Mouse Spike-In Transcripts to Evaluate RNA-Seq Analysis Tools , 2016, PloS one.

[25]  Russ B. Altman,et al.  A research roadmap for next-generation sequencing informatics , 2016, Science Translational Medicine.

[26]  Steven J. M. Jones,et al.  A somatic reference standard for cancer genome sequencing , 2016, Scientific Reports.

[27]  J. Mullikin,et al.  Systematic Evaluation of Sanger Validation of Next-Generation Sequencing Variants. , 2016, Clinical chemistry.

[28]  J. Carpten,et al.  Translating RNA sequencing into clinical diagnostics: opportunities and challenges , 2016, Nature Reviews Genetics.

[29]  Euan A. Ashley,et al.  Medical implications of technical accuracy in genome sequencing , 2016, Genome Medicine.

[30]  Asaf Levy,et al.  High-resolution phylogenetic microbial community profiling , 2016, The ISME Journal.

[31]  Ken W. Y. Cho,et al.  Measuring Absolute RNA Copy Numbers at High Temporal Resolution Reveals Transcriptome Kinetics in Development. , 2016, Cell reports.

[32]  Daniel J. Gaffney,et al.  A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.

[33]  Wolfgang Losert,et al.  svclassify: a method to establish benchmark structural variant calls , 2015, BMC Genomics.

[34]  Hanlee P. Ji,et al.  Haplotyping germline and cancer genomes using high-throughput linked-read sequencing , 2015, Nature Biotechnology.

[35]  Gert Matthijs,et al.  Guidelines for diagnostic next-generation sequencing , 2015, European Journal of Human Genetics.

[36]  Ulrich Broeckel,et al.  Characterization of 137 Genomic DNA Reference Materials for 28 Pharmacogenetic Genes: A GeT-RM Collaborative Project. , 2016, The Journal of molecular diagnostics : JMD.

[37]  Christina K. Yung,et al.  A cancer cell-line titration series for evaluating somatic classification , 2015, BMC Research Notes.

[38]  A. Sanchez-Flores,et al.  The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics , 2015, Front. Genet..

[39]  C. Huttenhower,et al.  The microbiome quality control project: baseline study design and future directions , 2015, Genome Biology.

[40]  Niaz Banaei,et al.  Next-Generation Sequencing for Infectious Disease Diagnosis and Management: A Report of the Association for Molecular Pathology. , 2015, The Journal of molecular diagnostics : JMD.

[41]  Naomi S. Altman,et al.  Points of Significance: Simple linear regression , 2015, Nature Methods.

[42]  J. Zook,et al.  Advancing Benchmarks for Genome Sequencing. , 2015, Cell systems.

[43]  Alexa B. R. McIntyre,et al.  Extensive sequencing of seven human genomes to characterize benchmark reference materials , 2015, Scientific Data.

[44]  Obi L. Griffith,et al.  Optimizing cancer genome sequencing and analysis. , 2015, Cell systems.

[45]  Brian C. Thomas,et al.  Unusual biology across a group comprising more than 15% of domain Bacteria , 2015, Nature.

[46]  Marc L. Salit,et al.  Best practices for evaluating single nucleotide variant calling methods for microbial genomics , 2015, Front. Genet..

[47]  Jian Wang,et al.  De novo assembly of a haplotype-resolved human genome , 2015, Nature Biotechnology.

[48]  Joshua M. Stuart,et al.  Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection , 2015, Nature Methods.

[49]  Bjarni V. Halldórsson,et al.  Large-scale whole-genome sequencing of the Icelandic population , 2015, Nature Genetics.

[50]  Mateusz Kuzak,et al.  Improving small RNA-seq by using a synthetic spike-in set for size-range quality control together with a set for data normalization , 2015, Nucleic acids research.

[51]  Birgit Funke,et al.  College of American Pathologists' laboratory standards for next-generation sequencing clinical tests. , 2015, Archives of pathology & laboratory medicine.

[52]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[53]  S. Teichmann,et al.  Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.

[54]  Richard Durbin,et al.  Extending reference assembly models , 2015, Genome Biology.

[55]  Jakob Grove,et al.  Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios , 2015, Nature Communications.

[56]  Paolo Rocco,et al.  Good laboratory practice for clinical next-generation sequencing informatics pipelines , 2015 .

[57]  Winnie S. Liang,et al.  Open-access synthetic spike-in mRNA-seq data for cancer gene fusions , 2014, BMC Genomics.

[58]  S. Dudoit,et al.  Normalization of RNA-seq data using factor analysis of control genes or samples , 2014, Nature Biotechnology.

[59]  Sheng Li,et al.  Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study , 2014, Nature Biotechnology.

[60]  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.

[61]  David P. Kreil,et al.  Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures , 2014, Nature Communications.

[62]  A. Molven,et al.  U-251 revisited: genetic drift and phenotypic consequences of long-term cultures of glioblastoma cells , 2014, Cancer medicine.

[63]  C. Amos,et al.  Routine use of the Ion Torrent AmpliSeq™ Cancer Hotspot Panel for identification of clinically actionable somatic mutations , 2014, Clinical chemistry and laboratory medicine.

[64]  Carolyn Sue Richards,et al.  Methods-based proficiency testing in molecular genetic pathology. , 2014, The Journal of molecular diagnostics : JMD.

[65]  Eric E Schadt,et al.  Analytical validation of whole exome and whole genome sequencing for clinical applications , 2014, BMC Medical Genomics.

[66]  C. Thermes,et al.  Library preparation methods for next-generation sequencing: tone down the bias. , 2014, Experimental cell research.

[67]  S. Bale,et al.  Development of a genomic DNA reference material panel for Rett syndrome (MECP2-related disorders) genetic testing. , 2014, The Journal of molecular diagnostics : JMD.

[68]  J. Zook,et al.  Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls , 2013, Nature Biotechnology.

[69]  C. Sheridan Milestone approval lifts Illumina's NGS from research into clinic , 2014, Nature Biotechnology.

[70]  C. Ponting,et al.  Sequencing depth and coverage: key considerations in genomic analyses , 2014, Nature Reviews Genetics.

[71]  Anne Marsden,et al.  International Organization for Standardization , 2014 .

[72]  Elaine Lyon,et al.  Three-year experience of a CAP/ACMG methods-based external proficiency testing program for laboratories offering DNA sequencing for rare inherited disorders , 2013, Genetics in Medicine.

[73]  Steven Leonard,et al.  SASI-Seq: sample assurance Spike-Ins, and highly differentiating 384 barcoding for Illumina sequencing , 2014, BMC Genomics.

[74]  R. Daber,et al.  Understanding the limitations of next generation sequencing informatics, an approach to clinical pipeline validation using artificial data sets. , 2013, Cancer genetics.

[75]  J. Harrow,et al.  Systematic evaluation of spliced alignment programs for RNA-seq data , 2013, Nature Methods.

[76]  Aleksandra A. Kolodziejczyk,et al.  Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.

[77]  Jeroen F. J. Laros,et al.  Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories , 2013, Nature Biotechnology.

[78]  Magalie S Leduc,et al.  Clinical whole-exome sequencing for the diagnosis of mendelian disorders. , 2013, The New England journal of medicine.

[79]  Rashmi Kanagal-Shamanna,et al.  Clinical validation of a next-generation sequencing screen for mutational hotspots in 46 cancer-related genes. , 2013, The Journal of molecular diagnostics : JMD.

[80]  Joshua L. Deignan,et al.  ACMG clinical laboratory standards for next-generation sequencing , 2013, Genetics in Medicine.

[81]  Nick L Hjelm,et al.  Development of a genomic DNA reference material panel for myotonic dystrophy type 1 (DM1) genetic testing. , 2013, The Journal of molecular diagnostics : JMD.

[82]  A. Sivachenko,et al.  Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.

[83]  N. Lennon,et al.  Characterizing and measuring bias in sequence data , 2013, Genome Biology.

[84]  H. Hakonarson,et al.  Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing , 2013, Genome Medicine.

[85]  Tina Hambuch,et al.  Opportunities and challenges associated with clinical diagnostic genome sequencing: a report of the Association for Molecular Pathology. , 2012, The Journal of molecular diagnostics : JMD.

[86]  Shashikant Kulkarni,et al.  Assuring the quality of next-generation sequencing in clinical laboratory practice , 2012, Nature Biotechnology.

[87]  David A. Orlando,et al.  Revisiting Global Gene Expression Analysis , 2012, Cell.

[88]  Charles Y. Lin,et al.  Transcriptional Amplification in Tumor Cells with Elevated c-Myc , 2012, Cell.

[89]  Marc Salit,et al.  Synthetic Spike-in Standards Improve Run-Specific Systematic Error Analysis for DNA and RNA Sequencing , 2012, PloS one.

[90]  Euan A Ashley,et al.  A public resource facilitating clinical use of genomes , 2012, Proceedings of the National Academy of Sciences.

[91]  Lynn K. Carmichael,et al.  Evaluation of 16S rDNA-Based Community Profiling for Human Microbiome Research , 2012, PloS one.

[92]  Katherine H. Huang,et al.  A framework for human microbiome research , 2012, Nature.

[93]  Zhiyuan Hu,et al.  Quality assurance of RNA expression profiling in clinical laboratories. , 2012, The Journal of molecular diagnostics : JMD.

[94]  P. D. Rijk,et al.  Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing , 2011, Nature Biotechnology.

[95]  Euan A Ashley,et al.  Performance comparison of whole-genome sequencing platforms , 2011, Nature Biotechnology.

[96]  M. Salit,et al.  Synthetic Spike-in Standards for Rna-seq Experiments Material Supplemental Open Access License Commons Creative , 2022 .

[97]  David M Bunk,et al.  Roadmap for harmonization of clinical laboratory measurement procedures. , 2011, Clinical chemistry.

[98]  Joshua S. Paul,et al.  Genotype and SNP calling from next-generation sequencing data , 2011, Nature Reviews Genetics.

[99]  Tengyu Ma,et al.  Quality assurance for Duchenne and Becker muscular dystrophy genetic testing: development of a genomic DNA reference material panel. , 2011, The Journal of molecular diagnostics : JMD.

[100]  Barbara Zehnbauer,et al.  Characterization of 107 genomic DNA reference materials for CYP2D6, CYP2C19, CYP2C9, VKORC1, and UGT1A1: a GeT-RM and Association for Molecular Pathology collaborative project. , 2010, The Journal of molecular diagnostics : JMD.

[101]  Paul Metcalfe,et al.  Establishment of the first World Health Organization International Genetic Reference Panel for quantitation of BCR-ABL mRNA. , 2010, Blood.

[102]  Tom Royce,et al.  A comprehensive catalogue of somatic mutations from a human cancer genome , 2010, Nature.

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

[104]  Bin Chen,et al.  Good laboratory practices for molecular genetic testing for heritable diseases and conditions. , 2009 .

[105]  S. Barker,et al.  Development of genomic reference materials for cystic fibrosis genetic testing. , 2009, The Journal of molecular diagnostics : JMD.

[106]  D. Armbruster,et al.  Limit of blank, limit of detection and limit of quantitation. , 2008, The Clinical biochemist. Reviews.

[107]  Amit Phansalkar,et al.  Consensus characterization of 16 FMR1 reference materials: a consortium study. , 2008, The Journal of molecular diagnostics : JMD.

[108]  Hubert W Vesper,et al.  Reference materials and commutability. , 2007, The Clinical biochemist. Reviews.

[109]  D. Bunk Reference materials and reference measurement procedures: an overview from a national metrology institute. , 2007, The Clinical biochemist. Reviews.

[110]  C. Richards,et al.  Development of genomic reference materials for Huntington disease genetic testing , 2007, Genetics in Medicine.

[111]  W Greg Miller,et al.  Why commutability matters. , 2006, Clinical chemistry.

[112]  L. Reid,et al.  Proposed methods for testing and selecting the ERCC external RNA controls , 2005, BMC Genomics.

[113]  Arlene Buller,et al.  Technical validation of a multiplex platform to detect thirty mutations in eight genetic diseases prevalent in individuals of Ashkenazi Jewish descent , 2005, Genetics in Medicine.

[114]  Elaine Lyon,et al.  Developing a Sustainable Process to Provide Quality Control Materials for Genetic Testing , 2005, Genetics in Medicine.

[115]  Kathleen F. Kerr,et al.  The External RNA Controls Consortium: a progress report , 2005, Nature Methods.

[116]  W. Grody,et al.  A novel method for creating artificial mutant samples for performance evaluation and quality control in clinical molecular genetics. , 2005, The Journal of molecular diagnostics : JMD.

[117]  The External Rna Controls Consortium The External RNA Controls Consortium: a progress report , 2005 .

[118]  John Quackenbush,et al.  Universal RNA reference materials for gene expression. , 2004, Clinical chemistry.

[119]  G. White,et al.  Uncertainty of measurement in quantitative medical testing: a laboratory implementation guide. , 2004, The Clinical biochemist. Reviews.

[120]  David Botstein,et al.  BMC Genomics BioMed Central Methodology article Universal Reference RNA as a standard for microarray experiments , 2004 .

[121]  C Sue Richards,et al.  Alternative approaches to proficiency testing in molecular genetics. , 2003, Clinical Chemistry.

[122]  G Lepschy,et al.  What is the Standard , 2002 .

[123]  C Franzini,et al.  Impact of reference materials on accuracy in clinical chemistry. , 1998, Clinical biochemistry.

[124]  C. Franzini Commutability of reference materials in clinical chemistry. , 1993, Journal of the International Federation of Clinical Chemistry.