Detection of rare variants in massively parallel sequencing data and base quality recalibration

Presented by: Mr Rick Tankard

Rick Tankard, who is a research assistant in the Bahlo Lab at WEHI, gave a seminar discussing the quality scores produced by the Illumina sequencing machines. He also discussed the analysis pipeline he has built, in conjunction with other members of the Bahlo lab, for the detection of rare variants in MPS experiments

The quality scores produced off the sequencer are meant to give an indication of the quality of the base-call (for SOLiD data the qualities measure the quality of the colour- or dinucleotide-call). Rick only spoke about Illumina HiSeq data but what follows is generally relevant regardless of the sequencing platform. The quality scores are Phred-encoded probabilities, e.g. a base quality of 30 corresponds to a 1/1000 chance that the base-call is incorrect. These quality scores are commonly used in the downstream analysis of aligned data, such as when calling variants. However it is well known that these quality scores frequently overestimate the quality of the base-calls.

Illumina quality scores decrease roughly exponentially (on the Phred scale) by base position so that the initial bases are generally much higher quality than the last bases of each read. Illumina recently re-jigged their base-quality algorithm to down-weight the first 10 or so nucleotides of every read in light of evidence that it was consistently over-estimating the quality of these positions. This has improved things somewhat, but there still seems to be a general overconfidence in the quality of the base-calls.

Roughly speaking, base-quality recalibration tries to adjust the sequencer-produced base qualities by comparing them to the observed mismatch frequencies in the aligned data. GATK and the Novoalign aligner both perform base-quality recalibration, though in slightly different ways and at different points in the analysis pipeline. GATK is more transparent in how it actually does the recalibration.

Rick also discussed aligning reads against an ambiguous reference genome. An ambiguous reference genome uses the IUPAC DNA codes at positions of known variation (such as SNPs) so that reads containing the non-reference allele at these sites are not unduly penalised. Novoalign is able to align against an ambiguous reference but I don’t believe many aligners are capable of this (e.g. BWA or Bowtie).

Rick studied a single sample to assess the effect of base-quality recalibration, using Novoalign and/or GATK, and the effect of aligning against an ambiguous reference (where possible) on the number of reads aligned and the number of variants called when using Novoalign or BWA. The overall conclusions were:

  • Use an ambiguous reference with Novoalign whenever possible
  • Use GATK recalibration with BWA
  • Avoid GATK recalibration when using the default options in Novoalign
  • Use the -o FULLNW option in Novoalign to prevent Novoalign artificially inflating the quality of bases at the 3’ end of reads
  • Don’t yet know if performing Novoalign quality-score recalibration followed by GATK quality score recalibration is a good idea

In the Bahlo lab we currently use Novoalign’s recalibration when aligning with Novoalign and GATK’s recalibration when aligning with BWA. Whilst the number of variants called by each of the “best” Novoalign and BWA analyses were similar in Rick’s study, I’d like to know which variants are unique to each analysis and why. Rick is pursuing this question.