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Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study

  • 1.

    Schuster, S. C. Next-generation sequencing transforms today’s biology. Nat. Methods 5, 16–18 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 2.

    Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 3.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Mardis, E. R. The impact of next-generation sequencing technology on genetics. Trends Genet. 24, 133–141 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 5.

    MacLean, D., Jones, J. D. & Studholme, D. J. Application of ‘next-generation’ sequencing technologies to microbial genetics. Nature Rev. Microbiol. 7, 96–97 (2009).

    Google Scholar 

  • 6.

    Glenn, T. C. Field guide to next-generation DNA sequencers. Mol. Ecol. Resour. 11, 759–769 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 7.

    Aziz, N. et al. College of American Pathologists’ laboratory standards for next-generation sequencing clinical tests. Arch. Pathol. Lab. Med. 139, 481–493 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 8.

    Schlaberg, R. et al. Validation of metagenomic next-generation sequencing tests for universal pathogen detection. Arch. Pathol. Lab. Med. 141, 776–786 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 9.

    Zhou, J. et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 5, 1303–1313 (2011).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 10.

    Mellmann, A. et al. High interlaboratory reproducibility and accuracy of next-generation-sequencing-based bacterial genotyping in a ring trial. J. Clin. Microbiol. 55, 908–913 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 11.

    Quail, M. A. et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13, 341 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 12.

    Shi, L. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).

    CAS 

    Google Scholar 

  • 13.

    Shi, L. et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol. 28, 827–838 (2010).

    CAS 

    Google Scholar 

  • 14.

    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).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 15.

    Su, Z. et al. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903–914 (2014).

    CAS 

    Google Scholar 

  • 16.

    Wang, C. et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32, 926–932 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 17.

    Li, S. et al. Detecting and correcting systematic variation in large-scale RNA sequencing data. Nat. Biotechnol. 32, 888–895 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 18.

    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).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 19.

    Merker, J. D. et al. Proficiency testing of standardized samples shows very high interlaboratory agreement for clinical next-generation sequencing–based oncology assays. Arch. Pathol. Lab. Med. 143, 463–471 (2019).

    CAS 

    Google Scholar 

  • 20.

    Mahamdallie, S. et al. The ICR639 CPG NGS validation series: a resource to assess analytical sensitivity of cancer predisposition gene testing. Wellcome Open Res. 3, 68 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 21.

    Zhong, Q. et al. Multi-laboratory proficiency testing of clinical cancer genomic profiling by next-generation sequencing. Pathol. Res. Pract. 214, 957–963 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 22.

    Zook, J. M. et al. An open resource for accurately benchmarking small variant and reference calls. Nat. Biotechnol. 37, 561–566 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 23.

    Krusche, P. et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37, 555–560 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 24.

    Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 25.

    Zook, J. M. et al. A robust benchmark for detection of germline large deletions and insertions. Nat. Biotechnol. 38, 1347–1355 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 26.

    Ball, M. P. et al. A public resource facilitating clinical use of genomes. Proc. Natl Acad. Sci. USA 109, 11920–11927 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 27.

    Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 28.

    Wagner, J. et al. Benchmarking challenging small variants with linked and long reads. Preprint at bioRxiv https://doi.org/10.1101/2020.07.24.212712 (2020).

  • 29.

    Landrum, M. J. & Kattman, B. L. ClinVar at five years: delivering on the promise. Hum. Mutat. 39, 1623–1630 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 30.

    Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F. & Hamosh, A. OMIM.org: Online Mendelian Inheritance in Man (OMIM), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43, D789–D798 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 31.

    Jeffares, D. C. et al. Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat. Commun. 8, 14061 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 32.

    Mahmoud, M. et al. Structural variant calling: the long and the short of it. Genome Biol. 20, 246 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 33.

    Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15, 461–468 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 34.

    Olson, N. D. et al. precisionFDA Truth Challenge V2: calling variants from short-and long-reads in difficult-to-map regions. Preprint at bioRxiv https://doi.org/10.1101/2020.11.13.380741 (2020).

  • 35.

    Freed, D. N., Aldana, R., Weber, J. A. & Edwards, J. S. The Sentieon Genomics Tools – A fast and accurate solution to variant calling from next-generation sequence data. Preprint at bioRxiv 115717 (2017).

  • 36.

    McIntyre, A. B. et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol. 18, 182 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 37.

    Sogin, M. L. in PCR Protocols: A Guide to Methods and Applications (eds Innis, M. et al.) (Elsevier, 2012).

  • 38.

    Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 39.

    Pedersen, B. S. & Quinlan, A. R. Mosdepth: quick coverage calculation for genomes and exomes. Bioinformatics 34, 867–868 (2018).

    CAS 

    Google Scholar 

  • 40.

    Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).

  • 41.

    Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 42.

    Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 43.

    Luo, R. et al. Exploring the limit of using a deep neural network on pileup data for germline variant calling. Nat. Mach. Intell. 2, 220–227 (2020).

    Google Scholar 

  • 44.

    Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 45.

    Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 46.

    Chen, X. et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 32, 1220–1222 (2016).

    CAS 

    Google Scholar 

  • 47.

    Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 48.

    Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 49.

    Toptaş, B. Ç., Rakocevic, G., Kómár, P. & Kural, D. Comparing complex variants in family trios. Bioinformatics 34, 4241–4247 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

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