title: Validation and assessment of variant calling pipelines for next-generation sequencing.
abstract: BACKGROUND
  The processing and analysis of the large scale data generated by next-generation sequencing (NGS) experiments is challenging and is a burgeoning area of new methods development. Several new bioinformatics tools have been developed for calling sequence variants from NGS data. Here, we validate the variant calling of these tools and compare their relative accuracy to determine which data processing pipeline is optimal.
  
  
  RESULTS
  We developed a unified pipeline for processing NGS data that encompasses four modules: mapping, filtering, realignment and recalibration, and variant calling. We processed 130 subjects from an ongoing whole exome sequencing study through this pipeline. To evaluate the accuracy of each module, we conducted a series of comparisons between the single nucleotide variant (SNV) calls from the NGS data and either gold-standard Sanger sequencing on a total of 700 variants or array genotyping data on a total of 9,935 single-nucleotide polymorphisms. A head to head comparison showed that Genome Analysis Toolkit (GATK) provided more accurate calls than SAMtools (positive predictive value of 92.55% vs. 80.35%, respectively). Realignment of mapped reads and recalibration of base quality scores before SNV calling proved to be crucial to accurate variant calling. GATK HaplotypeCaller algorithm for variant calling outperformed the UnifiedGenotype algorithm. We also showed a relationship between mapping quality, read depth and allele balance, and SNV call accuracy. However, if best practices are used in data processing, then additional filtering based on these metrics provides little gains and accuracies of >99% are achievable.
  
  
  CONCLUSIONS
  Our findings will help to determine the best approach for processing NGS data to confidently call variants for downstream analyses. To enable others to implement and replicate our results, all of our codes are freely available at http://metamoodics.org/wes.
authors: Mehdi
authors: M
authors: Pirooznia
authors: Melissa
authors: M
authors: Kramer
authors: Jennifer
authors: J
authors: Parla
authors: Fernando S
authors: FS
authors: Goes
authors: James B
authors: JB
authors: Potash
authors: W Richard
authors: WR
authors: McCombie
authors: Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD 21205, USA. pzandi@jhsph.edu.
authors: Peter P
authors: PP
authors: Zandi
mesh_terms: D001714:Bipolar Disorder / Q000235:genetics; D003627:Data Interpretation, Statistical; D059472:Exome; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA; D012984:Software