title: Statistical method to compare massive parallel sequencing pipelines.
abstract: BACKGROUND
  Today, sequencing is frequently carried out by Massive Parallel Sequencing (MPS) that cuts drastically sequencing time and expenses. Nevertheless, Sanger sequencing remains the main validation method to confirm the presence of variants. The analysis of MPS data involves the development of several bioinformatic tools, academic or commercial. We present here a statistical method to compare MPS pipelines and test it in a comparison between an academic (BWA-GATK) and a commercial pipeline (TMAP-NextGENe®), with and without reference to a gold standard (here, Sanger sequencing), on a panel of 41 genes in 43 epileptic patients. This method used the number of variants to fit log-linear models for pairwise agreements between pipelines. To assess the heterogeneity of the margins and the odds ratios of agreement, four log-linear models were used: a full model, a homogeneous-margin model, a model with single odds ratio for all patients, and a model with single intercept. Then a log-linear mixed model was fitted considering the biological variability as a random effect.
  
  
  RESULTS
  Among the 390,339 base-pairs sequenced, TMAP-NextGENe® and BWA-GATK found, on average, 2253.49 and 1857.14 variants (single nucleotide variants and indels), respectively. Against the gold standard, the pipelines had similar sensitivities (63.47% vs. 63.42%) and close but significantly different specificities (99.57% vs. 99.65%; p < 0.001). Same-trend results were obtained when only single nucleotide variants were considered (99.98% specificity and 76.81% sensitivity for both pipelines).
  
  
  CONCLUSIONS
  The method allows thus pipeline comparison and selection. It is generalizable to all types of MPS data and all pipelines.
authors.affiliation: Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France. mad-helenie.elsensohn@chu-lyon.fr.|Université de Lyon, Lyon, France. mad-helenie.elsensohn@chu-lyon.fr.|Université Lyon 1, Villeurbanne, France. mad-helenie.elsensohn@chu-lyon.fr.|CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France. mad-helenie.elsensohn@chu-lyon.fr.
authors.forename: M H
authors.initials: MH
authors.lastname: Elsensohn
authors.affiliation: Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France.|Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France.
authors.forename: N
authors.initials: N
authors.lastname: Leblay
authors.affiliation: Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|Service de Génétique, Hospices Civils de Lyon, Lyon, France.|Centre de Recherche en Neurosciences de Lyon, CNRS UMR 5292, INSERM U1028, Lyon, France.
authors.forename: S
authors.initials: S
authors.lastname: Dimassi
authors.affiliation: Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|Service de Génétique, Hospices Civils de Lyon, Lyon, France.|Centre de Recherche en Neurosciences de Lyon, CNRS UMR 5292, INSERM U1028, Lyon, France.
authors.forename: A
authors.initials: A
authors.lastname: Campan-Fournier
authors.affiliation: Service de Génétique, Hospices Civils de Lyon, Lyon, France.
authors.forename: A
authors.initials: A
authors.lastname: Labalme
authors.affiliation: Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France.|Service de Génétique, Hospices Civils de Lyon, Lyon, France.
authors.forename: F
authors.initials: F
authors.lastname: Roucher-Boulez
authors.affiliation: Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|Service de Génétique, Hospices Civils de Lyon, Lyon, France.|Centre de Recherche en Neurosciences de Lyon, CNRS UMR 5292, INSERM U1028, Lyon, France.
authors.forename: D
authors.initials: D
authors.lastname: Sanlaville
authors.affiliation: Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|Service de Génétique, Hospices Civils de Lyon, Lyon, France.|Centre de Recherche en Neurosciences de Lyon, CNRS UMR 5292, INSERM U1028, Lyon, France.
authors.forename: G
authors.initials: G
authors.lastname: Lesca
authors.affiliation: Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France.|Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France.
authors.forename: C
authors.initials: C
authors.lastname: Bardel
authors.affiliation: Service de Biostatistique-Bioinformatique, Hospices Civils de Lyon, 162 avenue Lacassagne, F-69003, Lyon, France.|Université de Lyon, Lyon, France.|Université Lyon 1, Villeurbanne, France.|CNRS UMR5558, Laboratoire de Biométrie et Biologie Evolutive, Equipe Biostatistique Santé, Villeurbanne, France.
authors.forename: P
authors.initials: P
authors.lastname: Roy
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004827:Epilepsy / Q000235:genetics / Q000473:pathology; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D054643:INDEL Mutation; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA
keywords: Massive parallel sequencing; Next-generation sequencing; Pipeline comparison; Sensitivity; Specificity; Statistical methods