title: Performance comparison of NextSeq and Ion Proton platforms for molecular diagnosis of clinical oncology.
abstract: OBJECTIVE
  Next-generation sequencing is a powerful approach to detect genetic mutations with which cancer diagnosis and treatment can be tailored to the individual patient in the era of personalized and precision medicine. Ion Torrent Systems Ion Proton and Illumina NextSeq are 2 major targeted sequencing platforms; however, not much work has been done to compare these platforms' performance for mutation detection in formalin-fixed paraffin-embedded (FFPE) materials.
  
  
  METHODS
  We benchmarked the performance by using a collection of FFPE samples from 23 patients with different cancers for NextSeq and Ion Proton platforms. We report analysis of sequencing in terms of average coverage depth, read length, and variant detection.
  
  
  RESULTS
  Sequencing results by NextSeq and Ion Proton displayed near perfect coverage behavior (>99%) on target region. We analyzed the ability to call variants from each platform and found that Ion Proton sequencing can identify 89% of single nucleotide variants (SNVs) whose mutant allele frequency (MAF) is greater than or equal to 5% detected by the NextSeq pipeline in common analytical regions. The correlation coefficient of MAF for those common SNVs was 1.0046 (R2 = 0.973) between the 2 platforms. To call lower mutant frequency (5%-10%) mutations for NextSeq sequencing, coverage depth should be improved. The concordance of small insertions and deletions between these 2 pipelines was up to 100%.
  
  
  CONCLUSIONS
  The 2 sequencing pipelines evaluated were able to generate usable sequence and had high concordance. They are proper for mutation detection in clinical application.
authors.affiliation: Department of Gastrointestinal Medical Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin - China.
authors.forename: Fei
authors.initials: F
authors.lastname: Cao
authors.affiliation: Novogene Bioinformatics Institute, Beijing - China.
authors.forename: Lianju
authors.initials: L
authors.lastname: Gao
authors.affiliation: Novogene Bioinformatics Institute, Beijing - China.
authors.forename: Longgang
authors.initials: L
authors.lastname: Wei
authors.affiliation: Novogene Bioinformatics Institute, Beijing - China.
authors.forename: Zhanni
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Novogene Bioinformatics Institute, Beijing - China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Novogene Bioinformatics Institute, Beijing - China.
authors.forename: Xia
authors.initials: X
authors.lastname: Ran
authors.affiliation: Department of Gastrointestinal Medical Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin - China.
authors.forename: Xuehong
authors.initials: X
authors.lastname: Meng
authors.affiliation: Department of Gastrointestinal Medical Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin - China.
authors.forename: Ji
authors.initials: J
authors.lastname: Tao
mesh_terms: D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D054643:INDEL Mutation / Q000235:genetics*; D008495:Medical Oncology; D025202:Molecular Diagnostic Techniques; D009363:Neoplasm Proteins / Q000235:genetics; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D011522:Protons
chemical_list: D009363:Neoplasm Proteins; D011522:Protons