Analysis of context-dependent errors for Illumina sequencing

Irina Abnizova, Steven Leonard, Tom Skelly, Andy Brown, David Jackson, Marina Gourtovaia, Guoying Qi, Nadeem Faruque, Kevin Lewis, Tony Cox, Rene te Boekhorst

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


The new generation of short-read sequencing technologies requires reliable measures of data quality. Such measures are especially important for variant calling. However, in the particular case of SNP calling, a great number of false-positive SNPs may be obtained. One needs to distinguish putative SNPs from sequencing or other errors. We found that not only the probability of sequencing errors (i.e. the quality value) is important to distinguish an FP-SNP but also the conditional probability of \correcting" this error (the \second best call" probability,
conditional on that of the first call). Surprisingly, around 80% of mismatches can be corrected" with this second call. Another way to reduce the rate of FP-SNPs is to retrieve DNA motifs that seem to be prone to sequencing errors, and to attach a corresponding conditional quality value to these motifs. We have developed several measures to distinguish between sequence errors and candidate SNPs, based on a base call’s nucleotide context and its mismatch type. In addition, we suggested a simple method to correct the majority of mismatches,based on conditional probability of their \second" best intensity call. We attach a corresponding second call confidence (quality value) of being corrected to each mismatch.
Original languageEnglish
Article number1241005
Number of pages20
JournalJournal of Bioinformatics and Computational Biology
Issue number2
Publication statusPublished - 3 Apr 2012


  • Next-generation sequencing
  • statistical measures
  • error probability
  • quality value


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