title: Improvement in detection of minor alleles in next generation sequencing by base quality recalibration.
abstract: BACKGROUND
  Minor allele detection in very high coverage sequence data (>1000X) has many applications such as detecting mtDNA heteroplasmy, somatic mutations in cancer or tumors, SNP calling in pool sequencing, etc., where reads with low frequency are not necessarily sequence error but may instead convey biological information. However, the suitability of common base quality recalibration tools for such applications has not been investigated in detail.
  
  
  RESULTS
  We show that the widely used tool GATK BaseRecalibration has several limitations in minor allele detection. First, GATK IndelRealignment fails to work if the sequence coverage is above a certain level since it then becomes computationally infeasible. Second, the accuracy of the base quality largely depends on the database of known SNPs as the control, which limits the ability of de novo minor allele detection. Third, GATK reduces the base quality of sequence errors at the cost of reducing scores for true minor alleles. To overcome these limitations, we present a novel approach called SEGREG, which applies segmented regression to control sequences (e.g. phiX174 DNA) spiked into a sequencing run. Based on simulations SEGREG improves both the accuracy of base quality scores and the detection of minor alleles. We further investigate sequence error and recalibration parameters by applying a Logarithm Likelihood Ratio (LLR) approach to SEGREG recalibrated base quality scores for phiX174 DNA sequenced to very high coverage, and for mtDNA genome sequences previously analyzed for heteroplasmic variants.
  
  
  CONCLUSIONS
  Our results suggest that SEGREG improves base recalibration without suffering the limitations discussed above, and the LLR approach benefits from SEGREG in identifying more true minor alleles, while avoiding false positives from sequencing error.
authors.affiliation: Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, D04103, Germany. shengyu_ni@eva.mpg.de.
authors.forename: Shengyu
authors.initials: S
authors.lastname: Ni
authors.affiliation: Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, D04103, Germany. stonekg@eva.mpg.de.
authors.forename: Mark
authors.initials: M
authors.lastname: Stoneking
mesh_terms: D000483:Alleles; D002138:Calibration; D003198:Computer Simulation; D004272:DNA, Mitochondrial / Q000235:genetics; D005787:Gene Frequency; D054629:Genome, Mitochondrial; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*; D014764:Viral Proteins / Q000235:genetics
chemical_list: D004272:DNA, Mitochondrial; C029555:E protein, bacteriophage X174; D014764:Viral Proteins