Efficient error correction for next-generation sequencing of viral amplicons

Details

• Alternative Title:
  BMC Bioinformatics
• Personal Author:
  Skums, Pavel ; Dimitrova, Zoya ; Campo, David S ; Vaughan, Gilberto ; Rossi, Livia ; Forbi, Joseph C ; Yokosawa, Jonny ; Zelikovsky, Alex ; Khudyakov, Yury
• Description:
  Background
  
  Next-generation sequencing allows the analysis of an unprecedented number of viral sequence variants from infected patients, presenting a novel opportunity for understanding virus evolution, drug resistance and immune escape. However, sequencing in bulk is error prone. Thus, the generated data require error identification and correction. Most error-correction methods to date are not optimized for amplicon analysis and assume that the error rate is randomly distributed. Recent quality assessment of amplicon sequences obtained using 454-sequencing showed that the error rate is strongly linked to the presence and size of homopolymers, position in the sequence and length of the amplicon. All these parameters are strongly sequence specific and should be incorporated into the calibration of error-correction algorithms designed for amplicon sequencing.
  
  Results
  
  In this paper, we present two new efficient error correction algorithms optimized for viral amplicons: (i) k-mer-based error correction (KEC) and (ii) empirical frequency threshold (ET). Both were compared to a previously published clustering algorithm (SHORAH), in order to evaluate their relative performance on 24 experimental datasets obtained by 454-sequencing of amplicons with known sequences. All three algorithms show similar accuracy in finding true haplotypes. However, KEC and ET were significantly more efficient than SHORAH in removing false haplotypes and estimating the frequency of true ones.
  
  Conclusions
  
  Both algorithms, KEC and ET, are highly suitable for rapid recovery of error-free haplotypes obtained by 454-sequencing of amplicons from heterogeneous viruses.
  
  
• Subjects:
  Algorithms Cluster Analysis Computational Biology DNA, Viral Haplotypes Proceedings Sequence Analysis, DNA Viruses
• Source:
  BMC Bioinformatics. 2012; 13(Suppl 10):S6.
• Document Type:
  Journal Article
• Volume:
  13
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:53c8dfdc09e620bf8746f8be07ea2b0e3e19630c2bef06b559478eb67ebb1ff8
• Download URL:
  https://stacks.cdc.gov/view/cdc/10839/cdc_10839_DS1.pdf
• File Type:
  [PDF - 981.99 KB ]