Abstract
BackgroundSolexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology.ResultsWe propose a novel base calling algorithm using model-based clustering and probability theory to identify ambiguous bases and code them with IUPAC symbols. We also select optimal sub-tags using a score based on information content to remove uncertain bases towards the ends of the reads.ConclusionWe show that the method improves genome coverage and number of usable tags as compared with Solexa's data processing pipeline by an average of 15%. An R package is provided which allows fast and accurate base calling of Solexa's fluorescence intensity files and the production of informative diagnostic plots.
Highlights
Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags by parallel sequencing-by-synthesis of DNA colonies
Synthesis efficiency is limited and within each colony, some DNA strands incorporate a non-complementary base or are de-synchronized because they failed to incorporate a nucleotide at a previous step
Cokus et al.[12] use Solexa's pre-treated data (_sig2 files) and apply a very similar EM procedure to fit a Gaussian mixture model for probabilistic base calling. They do not use information based metrics to reduce the probabilities to IUPAC codes, but rather construct position-weight matrices with which they scan the reference genome, which is computationally expensive and not directly applicable for de-novo sequencing
Summary
Solexa/Illumina short-read ultra-high throughput DNA sequencing technology produces millions of short tags (up to 36 bases) by parallel sequencing-by-synthesis of DNA colonies. The processing and statistical analysis of such high-throughput data poses new challenges; currently a fair proportion of the tags are routinely discarded due to an inability to match them to a reference sequence, thereby reducing the effective throughput of the technology. Ultra-high-throughput sequencing is having a growing impact on biological research by providing a fast and high resolution access to genome-scale information. While the sample processing is relatively streamlined, innovations in data management and information processing are necessary to exploit the full potential of the technology. Developing new algorithms to extract more information from available images and reduce the number of sequencing runs per project will prove extremely (page number not for citation purposes)
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