Reference-Free Validation of Short Read Data
AuthorSchroeder, J; Bailey, J; Conway, T; Zobel, J
Source TitlePLoS One
PublisherPUBLIC LIBRARY SCIENCE
AffiliationComputer Science and Software Engineering
Document TypeJournal Article
CitationsSchroeder, J., Bailey, J., Conway, T. & Zobel, J. (2010). Reference-Free Validation of Short Read Data. PLOS ONE, 5 (9), https://doi.org/10.1371/journal.pone.0012681.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943903
BACKGROUND: High-throughput DNA sequencing techniques offer the ability to rapidly and cheaply sequence material such as whole genomes. However, the short-read data produced by these techniques can be biased or compromised at several stages in the sequencing process; the sources and properties of some of these biases are not always known. Accurate assessment of bias is required for experimental quality control, genome assembly, and interpretation of coverage results. An additional challenge is that, for new genomes or material from an unidentified source, there may be no reference available against which the reads can be checked. RESULTS: We propose analytical methods for identifying biases in a collection of short reads, without recourse to a reference. These, in conjunction with existing approaches, comprise a methodology that can be used to quantify the quality of a set of reads. Our methods involve use of three different measures: analysis of base calls; analysis of k-mers; and analysis of distributions of k-mers. We apply our methodology to wide range of short read data and show that, surprisingly, strong biases appear to be present. These include gross overrepresentation of some poly-base sequences, per-position biases towards some bases, and apparent preferences for some starting positions over others. CONCLUSIONS: The existence of biases in short read data is known, but they appear to be greater and more diverse than identified in previous literature. Statistical analysis of a set of short reads can help identify issues prior to assembly or resequencing, and should help guide chemical or statistical methods for bias rectification.
KeywordsArtificial Intelligence and Image Processing
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