Reference standards for next-generation sequencing (original) (raw)

• Yang, Y. et al. Clinical whole-exome sequencing for the diagnosis of Mendelian disorders. N. Engl. J. Med. 369, 1502–1511 (2013).
  Article CAS PubMed PubMed Central Google Scholar
• Byron, S. A., Van Keuren-Jensen, K. R., Engelthaler, D. M., Carpten, J. D. & Craig, D. W. Translating RNA sequencing into clinical diagnostics: opportunities and challenges. Nat. Rev. Genet. 17, 257–271 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Lefterova, M. I., Suarez, C. J., Banaei, N. & Pinsky, B. A. Next-generation sequencing for infectious disease diagnosis and management. J. Mol. Diagn. 17, 623–634 (2015).
  Article CAS PubMed Google Scholar
• Goldfeder, R. L. et al. Medical implications of technical accuracy in genome sequencing. Genome Med. 8, 24 (2016). This study investigated the location of clinically relevant variants in regions of the human genome that are refractory to reliable genotyping with NGS owing to the presence of extreme GC content or repetitive sequences.
  Article CAS PubMed PubMed Central Google Scholar
• van Dijk, E. L., Jaszczyszyn, Y. & Thermes, C. Library preparation methods for next-generation sequencing: tone down the bias. Exp. Cell Res. 322, 12–20 (2014).
  Article CAS PubMed Google Scholar
• Mu, W., Lu, H.-M., Chen, J., Li, S. & Elliott, A. M. Sanger confirmation is required to achieve optimal sensitivity and specificity in next-generation sequencing panel testing. J. Mol. Diagn. 18, 923–932 (2016).
  Article CAS PubMed Google Scholar
• Beck, T. F., Mullikin, J. C. & Biesecker, L. G. Systematic evaluation of Sanger validation of next-generation sequencing variants. Clin. Chem. 62, 647–654 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Matthijs, G. et al. Guidelines for diagnostic next-generation sequencing. Eur. J. Hum. Genet. 24, 2–5 (2016).
  Article CAS PubMed Google Scholar
• Gargis, A. S., Kalman, L. & Lubin, I. M. Assuring the quality of next-generation sequencing in clinical microbiology and public health laboratories. J. Clin. Microbiol. 54, 2857–2865 (2016).
  Article PubMed PubMed Central Google Scholar
• Gargis, A. S. et al. Good laboratory practice for clinical next-generation sequencing informatics pipelines. Nat. Biotechnol. 33, 689–693 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• Aziz, N. et al. College of American Pathologists' laboratory standards for next-generation sequencing clinical tests. Arch. Pathol. Lab. Med. 139, 481–493 (2015).
  Article PubMed Google Scholar
• Rehm, H. L. et al. ACMG clinical laboratory standards for next-generation sequencing. Genet. Med. 15, 733–747 (2013).
  Article PubMed PubMed Central Google Scholar
• Schrijver, I. et al. Opportunities and challenges associated with clinical diagnostic genome sequencing. J. Mol. Diagn. 14, 525–540 (2012).
  Article CAS PubMed Google Scholar
• Gargis, A. S. et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat. Biotechnol. 30, 1033–1036 (2012). The Nex-StoCT (Next-generation Sequencing: Standardization of Clinical Testing) workgroup developed a set of guidelines to ensure that results from NGS tests are sufficiently reliable for clinical diagnosis, including the recommendation of reference standards for test validation, quality control and proficiency testing.
  Article CAS PubMed Google Scholar
• Centers for Disease Control and Prevention. Good laboratory practices for molecular genetic testing for heritable diseases and conditions. MMWR Recomm. Rep. 58, 1–29 (2009).
• Chen, B. et al. Developing a sustainable process to provide quality control materials for genetic testing. Genet. Med. 7, 534–549 (2005).
  Article PubMed Google Scholar
• Greg Miller, W. et al. Roadmap for harmonization of clinical laboratory measurement procedures. Clin. Chem. 57, 1108–1117 (2011).
  Article CAS PubMed Google Scholar
• Franzini, C. & Ceriotti, F. Impact of reference materials on accuracy in clinical chemistry. Clin. Biochem. 31, 449–457 (1998).
  Article CAS PubMed Google Scholar
• Radin, N. What is a standard? Clin. Chem. 13, 55–76 (1967).
  CAS PubMed Google Scholar
• International Organization for Standardization. ISO Guide 30:2015 — Reference Materials — Selected Terms and Definitions (ISO, 2015).
• Bunk, D. M. Reference materials and reference measurement procedures: an overview from a national metrology institute. Clin. Biochem. Rev. 28, 131–137 (2007).
  PubMed PubMed Central Google Scholar
• Vesper, H. W., Miller, W. G. & Myers, G. L. Reference materials and commutability. Clin. Biochem. Rev. 28, 139–147 (2007).
  PubMed PubMed Central Google Scholar
• Miller, W. G., Myers, G. L. & Rej, R. Why commutability matters. Clin. Chem. 52, 553–554 (2006).
  Article CAS PubMed Google Scholar
• Sims, D., Sudbery, I., Ilott, N. E., Heger, A. & Ponting, C. P. Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15, 121–132 (2014).
  Article CAS PubMed Google Scholar
• Chen, L., Liu, P., Evans, T. C. & Ettwiller, L. M. DNA damage is a pervasive cause of sequencing errors, directly confounding variant identification. Science 355, 752–756 (2017).
  Article CAS PubMed Google Scholar
• Zook, J. M., Samarov, D., McDaniel, J., Sen, S. K. & Salit, M. Synthetic spike-in standards improve run-specific systematic error analysis for DNA and RNA sequencing. PLoS ONE 7, e41356 (2012).
  Article CAS PubMed PubMed Central Google Scholar
• White, G. H. & Farrance, I. Uncertainty of measurement in quantitative medical testing: a laboratory implementation guide. Clin. Biochem. Rev. 25, S1–S24 (2004).
  CAS PubMed PubMed Central Google Scholar
• Ross, M. G. et al. Characterizing and measuring bias in sequence data. Genome Biol. 14, R51 (2013).
  Article PubMed PubMed Central Google Scholar
• O'Rawe, J. et al. Low concordance of multiple variant-calling pipelines: practical implications for exome and genome sequencing. Genome Med. 5, 28 (2013).
  Article CAS PubMed PubMed Central Google Scholar
• Reumers, J. et al. Optimized filtering reduces the error rate in detecting genomic variants by short-read sequencing. Nat. Biotechnol. 30, 61–68 (2012).
  Article CAS Google Scholar
• Lam, H. Y. K. et al. Performance comparison of whole-genome sequencing platforms. Nat. Biotechnol. 30, 78–82 (2012).
  Article CAS Google Scholar
• Church, D. M. et al. Extending reference assembly models. Genome Biol. 16, 13 (2015).
  Article PubMed PubMed Central Google Scholar
• Torsvik, A. et al. U-251 revisited: genetic drift and phenotypic consequences of long-term cultures of glioblastoma cells. Cancer Med. 3, 812–824 (2014).
  Article CAS PubMed PubMed Central Google Scholar
• Zook, J. M. et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32, 246–251 (2014). The Genome in a Bottle Consortium used a range of NGS technologies and analytical tools to characterize the NA12878 genome and to provide a set of high-confidence genotypes that can be used to benchmark germline variant-calling pipelines.
  Article CAS PubMed Google Scholar
• Parikh, H. et al. svclassify: a method to establish benchmark structural variant calls. BMC Genomics 17, 64 (2016).
  Article PubMed PubMed Central Google Scholar
• Eberle, M. A. 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. Genome Res. 27, 157–164 (2017).
  Article CAS PubMed PubMed Central Google Scholar
• Linderman, M. D. et al. Analytical validation of whole exome and whole genome sequencing for clinical applications. BMC Med. Genomics 7, 20 (2014).
  Article CAS PubMed PubMed Central Google Scholar
• Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Ball, M. P. et al. A public resource facilitating clinical use of genomes. Proc. Natl Acad. Sci. USA 109, 11920–11927 (2012).
  Article PubMed PubMed Central Google Scholar
• Seo, J.-S. et al. De novo assembly and phasing of a Korean human genome. Nature 538, 243–247 (2016).
  Article CAS PubMed Google Scholar
• Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).
  Article CAS PubMed Google Scholar
• Cao, H. et al. De novo assembly of a haplotype-resolved human genome. Nat. Biotechnol. 33, 617–622 (2015).
  Article CAS PubMed Google Scholar
• Besenbacher, S. et al. Novel variation and de novo mutation rates in population-wide de novo assembled Danish trios. Nat. Commun. 6, 5969 (2015).
  Article CAS PubMed Google Scholar
• Kalman, L. V. et al. Development of a genomic DNA reference material panel for Rett syndrome (_MECP2_-related disorders) genetic testing. J. Mol. Diagn. 16, 273–279 (2014).
  Article CAS PubMed PubMed Central Google Scholar
• Kalman, L. et al. Development of a genomic DNA reference material panel for myotonic dystrophy type 1 (DM1) genetic testing. J. Mol. Diagn. 15, 518–525 (2013).
  Article CAS PubMed PubMed Central Google Scholar
• Kalman, L. et al. Quality assurance for Duchenne and Becker muscular dystrophy genetic testing. J. Mol. Diagn. 13, 167–174 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Pratt, V. M. et al. Development of genomic reference materials for cystic fibrosis genetic testing. J. Mol. Diagn. 11, 186–193 (2009).
  Article CAS PubMed PubMed Central Google Scholar
• Amos Wilson, J. et al. Consensus characterization of 16 FMR1 reference materials: a consortium study. J. Mol. Diagn. 10, 2–12 (2008).
  Article CAS PubMed PubMed Central Google Scholar
• Kalman, L. et al. Development of genomic reference materials for Huntington disease genetic testing. Genet. Med. 9, 719–723 (2007).
  Article CAS PubMed Google Scholar
• Pratt, V. M. et al. Characterization of 137 genomic DNA reference materials for 28 pharmacogenetic genes. J. Mol. Diagn. 18, 109–123 (2016). This paper illustrates the process undertaken by GeT-RM to develop reference materials for genetic testing, including characterization by multiple laboratories and subsequent consensus verification of genotypes.
  Article CAS PubMed PubMed Central Google Scholar
• Pratt, V. M. 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. J. Mol. Diagn. 12, 835–846 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Tsongalis, G. J. et al. Routine use of the Ion Torrent AmpliSeq™ Cancer Hotspot Panel for identification of clinically actionable somatic mutations. Clin. Chem. Lab. Med. 52, 707 (2014).
  Article CAS PubMed Google Scholar
• Jarvis, M. et al. A novel method for creating artificial mutant samples for performance evaluation and quality control in clinical molecular genetics. J. Mol. Diagn. 7, 247–251 (2005).
  Article CAS PubMed PubMed Central Google Scholar
• Craig, D. W. et al. A somatic reference standard for cancer genome sequencing. Sci. Rep. 6, 24607 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Griffith, M. et al. Optimizing cancer genome sequencing and analysis. Cell Syst. 1, 210–223 (2015). This characterization of matched tumour and normal samples shows the requirement for deep sequencing to reveal the diversity of somatic mutations and subclonal populations, with the resulting data providing a useful resource for the bioinformatic analysis of tumour samples.
  Article CAS PubMed PubMed Central Google Scholar
• Pleasance, E. D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010).
  Article CAS PubMed Google Scholar
• Zook, J. M. & Salit, M. Advancing benchmarks for genome sequencing. Cell Syst. 1, 176–177 (2015).
  Article CAS PubMed Google Scholar
• Denroche, R. E. et al. A cancer cell-line titration series for evaluating somatic classification. BMC Res. Notes 8, 823 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014). This is a comprehensive study of RNA-seq accuracy and reproducibility across multiple sequencing platforms and laboratory sites, using human reference RNA samples spiked with the ERCC controls.
• Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Novoradovskaya, N. et al. Universal Reference RNA as a standard for microarray experiments. BMC Genomics 5, 20 (2004).
  Article PubMed PubMed Central Google Scholar
• 't Hoen, P. A. C. et al. Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories. Nat. Biotechnol. 31, 1015–1022 (2013).
  Article CAS PubMed Google Scholar
• Li, S. et al. Multi-platform assessment of transcriptome profiling using RNA-seq in the ABRF next-generation sequencing study. Nat. Biotechnol. 32, 915–925 (2014).
  Article CAS PubMed PubMed Central Google Scholar
• White, H. E. et al. Establishment of the first World Health Organization International Genetic Reference Panel for quantitation of BCR-ABL mRNA. Blood 116, e111–e117 (2010).
  Article CAS PubMed Google Scholar
• Escobar-Zepeda, A., Vera-Ponce de León, A. & Sanchez-Flores, A. The road to metagenomics: from microbiology to DNA sequencing technologies and bioinformatics. Front. Genet. 6, 348 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain bacteria. Nature 523, 208–211 (2015).
  Article CAS PubMed Google Scholar
• Olson, N. D. et al. Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Front. Genet. 6, 235 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).
  Article CAS PubMed Google Scholar
• The Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012).
• Jumpstart Consortium Human Microbiome Project Data Generation Working Group. Evaluation of 16S rDNA-based community profiling for human microbiome research. PLoS ONE 7, e39315 (2012). The Human Microbiome Project developed a mock community of microbes commonly found on or in the human body, which has been used to benchmark metagenome sequencing and analysis.
• Sinha, R., Abnet, C. C., White, O., Knight, R. & Huttenhower, C. The microbiome quality control project: baseline study design and future directions. Genome Biol. 16, 276 (2015).
  Article PubMed PubMed Central Google Scholar
• Singer, E. et al. High-resolution phylogenetic microbial community profiling. ISME J. 10, 2020–2032 (2016).
  Article PubMed PubMed Central Google Scholar
• The External RNA Controls Consortium. The External RNA Controls Consortium: a progress report. Nat. Methods 2, 731–734 (2005).
• Sims, D. J. et al. Plasmid-based materials as multiplex quality controls and calibrators for clinical next-generation sequencing assays. J. Mol. Diagn. 18, 336–349 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Quail, M. A. et al. SASI-Seq: sample assurance spike-ins, and highly differentiating 384 barcoding for Illumina sequencing. BMC Genomics 15, 110 (2014).
  Article PubMed PubMed Central Google Scholar
• Strom, C. M. et al. Technical validation of a multiplex platform to detect thirty mutations in eight genetic diseases prevalent in individuals of Ashkenazi Jewish descent. Genet. Med. 7, 633–639 (2005).
  Article PubMed Google Scholar
• Deveson, I. W. et al. Representing genetic variation with synthetic DNA standards. Nat. Methods 13, 784–791 (2016). This study presents a set of synthetic spike-in controls representing DNA variants (SNVs, indels and structural variants), which can function as qualitative and quantitative controls for genome sequencing.
  Article CAS PubMed Google Scholar
• Kudalkar, E. M. et al. Multiplexed reference materials as controls for diagnostic next-generation sequencing. J. Mol. Diagn. 18, 882–889 (2016).
  Article CAS PubMed Google Scholar
• The External RNA Controls Consortium. Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 6, 150 (2005).
• Cronin, M. et al. Universal RNA reference materials for gene expression. Clin. Chem. 50, 1464–1471 (2004).
  Article CAS PubMed Google Scholar
• Paul, L. et al. SIRVs: Spike-In RNA Variants as external isoform controls in RNA-sequencing. Preprint at bioRxiv http://dx.doi.org/10.1101/080747 (2016).
  Google Scholar
• Leshkowitz, D. et al. Using synthetic mouse spike-in transcripts to evaluate RNA-seq analysis tools. PLoS ONE 11, e0153782 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Hardwick, S. A. et al. Spliced synthetic genes as internal controls in RNA sequencing experiments. Nat. Methods 13, 792–798 (2016).
  Article CAS PubMed Google Scholar
• Locati, M. D. 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. Nucleic Acids Res. 43, e89 (2015).
  Article PubMed PubMed Central Google Scholar
• Tembe, W. D. et al. Open-access synthetic spike-in mRNA-seq data for cancer gene fusions. BMC Genomics 15, 824 (2014).
  Article CAS PubMed PubMed Central Google Scholar
• Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543–1551 (2011). This study used the ERCC controls to measure the sensitivity, dynamic range, quantitative accuracy and biases of RNA-seq experiments.
  Article CAS PubMed PubMed Central Google Scholar
• Munro, S. A. et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat. Commun. 5, 5125 (2014).
  Article CAS PubMed Google Scholar
• Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
  Article CAS PubMed Google Scholar
• Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).
  Article CAS PubMed Google Scholar
• Owens, N. D. L. et al. Measuring absolute RNA copy numbers at high temporal resolution reveals transcriptome kinetics in development. Cell Rep. 14, 632–647 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Ewing, A. D. et al. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection. Nat. Methods 12, 623–630 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• Daber, R., Sukhadia, S. & Morrissette, J. J. D. Understanding the limitations of next generation sequencing informatics, an approach to clinical pipeline validation using artificial data sets. Cancer Genet. 206, 441–448 (2014).
  Article CAS Google Scholar
• Escalona, M., Rocha, S. & Posada, D. A comparison of tools for the simulation of genomic next-generation sequencing data. Nat. Rev. Genet. 17, 459–469 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Engstrom, P. G. et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat. Methods 10, 1185–1191 (2013).
  Article CAS PubMed PubMed Central Google Scholar
• Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
  Article CAS PubMed PubMed Central Google Scholar
• Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
  Article CAS PubMed Google Scholar
• Sheridan, C. Milestone approval lifts Illumina's NGS from research into clinic. Nat. Biotechnol. 32, 111–112 (2014).
  Article CAS PubMed Google Scholar
• Centers for Medicare and Medicaid Services. US Department of Health and Human Services. Part 493 — Laboratory Requirements: Clinical Laboratory Improvement Amendments of 1988. 42 CFR §493.1443–1495 https://www.cdc.gov/clia/Regulatory/default.aspx
• Richards, C. S. & Grody, W. W. Alternative approaches to proficiency testing in molecular genetics. Clin. Chem. 49, 717–718 (2003).
  Article CAS PubMed Google Scholar
• Schrijver, I. et al. Methods-based proficiency testing in molecular genetic pathology. J. Mol. Diagn. 16, 283–287 (2014).
  Article PubMed Google Scholar
• Richards, C. S., Palomaki, G. E., Lacbawan, F. L., Lyon, E. & Feldman, G. L. Three-year experience of a CAP/ACMG methods-based external proficiency testing program for laboratories offering DNA sequencing for rare inherited disorders. Genet. Med. 16, 25–32 (2014).
  Article PubMed Google Scholar
• Duncavage, E. J. et al. A model study of in silico proficiency testing for clinical next-generation sequencing. Arch. Pathol. Lab. Med. 140, 1085–1091 (2016).
  Article CAS PubMed Google Scholar
• Tang, W., Hu, Z., Muallem, H. & Gulley, M. L. Quality assurance of RNA expression profiling in clinical laboratories. J. Mol. Diagn. 14, 1–11 (2012).
  Article CAS PubMed PubMed Central Google Scholar
• Duncavage, E. J., Abel, H. J. & Pfeifer, J. D. In silico proficiency testing for clinical next-generation sequencing. J. Mol. Diagn. 19, 35–42 (2017).
  Article CAS PubMed Google Scholar
• Davies, K. D. et al. Multi-institutional FASTQ file exchange as a means of proficiency testing for next-generation sequencing bioinformatics and variant interpretation. J. Mol. Diagn. 18, 572–579 (2016).
  Article CAS PubMed Google Scholar
• Altman, R. B. et al. A research roadmap for next-generation sequencing informatics. Sci. Transl Med. 8, 335ps10 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014). These authors developed a normalization strategy for RNA-seq termed RUV (remove unwanted variation), which adjusts for nuisance technical effects between samples by performing factor analysis on suitable sets of control genes (for example, RNA spike-ins).
  Article CAS PubMed PubMed Central Google Scholar
• Poplin, R. et al. Creating a universal SNP and small indel variant caller with deep neural networks. Preprint at bioRxiv http://dx.doi.org/10.1101/092890 (2016).
  Google Scholar
• Zheng, G. X. Y. et al. Haplotyping germline and cancer genomes with high-throughput linked-read sequencing. Nat. Biotechnol. 34, 303–311 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Singh, R. R. et al. Clinical validation of a next-generation sequencing screen for mutational hotspots in 46 cancer-related genes. J. Mol. Diagn. 15, 607–622 (2013).
  Article CAS PubMed Google Scholar
• Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).
  Article CAS PubMed PubMed Central Google Scholar
• Franzini, C. Commutability of reference materials in clinical chemistry. J. Int. Fed. Clin. Chem. 5, 169–173 (1993).
  CAS PubMed Google Scholar
• Lever, J., Krzywinski, M. & Altman, N. Points of significance: classification evaluation. Nat. Methods 13, 603–604 (2016).
  Article CAS Google Scholar
• Telenti, A. et al. Deep sequencing of 10,000 human genomes. Proc. Natl Acad. Sci. USA 113, 11901–11906 (2016).
  Article CAS PubMed PubMed Central Google Scholar
• Nielsen, R., Paul, J. S., Albrechtsen, A. & Song, Y. S. Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 12, 443–451 (2011).
  Article CAS PubMed PubMed Central Google Scholar
• Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).
  Article CAS PubMed PubMed Central Google Scholar
• Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10, e0118432 (2015).
  Article CAS PubMed PubMed Central Google Scholar
• Armbruster, D. A. & Pry, T. Limit of blank, limit of detection and limit of quantitation. Clin. Biochem. Rev. 29, S49–S52 (2008).
  PubMed PubMed Central Google Scholar
• Altman, N. & Krzywinski, M. Points of significance: simple linear regression. Nat. Methods 12, 999–1000 (2015).
  Article CAS PubMed Google Scholar
• Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
  Article CAS PubMed PubMed Central Google Scholar
• Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).
  Article CAS PubMed PubMed Central Google Scholar
• Lovén, J. et al. Revisiting global gene expression analysis. Cell 151, 476–482 (2012).
  Article CAS PubMed PubMed Central Google Scholar
• Stämmler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 28 (2016).
  Article PubMed PubMed Central Google Scholar