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ItemReference-Free Validation of Short Read Data(PUBLIC LIBRARY SCIENCE, 2010-09-22)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.
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ItemMethodologies for evaluation of note-based music-retrieval systems(INFORMS, 2006-01-01)There have been many proposed music-retrieval systems, based on a variety of principles. How the effectiveness of these systems compares is not clear. The evaluation of some systems has been informal, without the rigor applied in other areas of information retrieval, and comparison of systems is difficult because of the lack of a common data set, queries, or relevance judgments. In this paper we explain how we collected artificial and expert music queries and name-based relevance judgments, and describe software we developed for collection of manual relevance judgments. Together with a collection of downloaded musical instrument digital interface (MIDI) files, these sets of queries and relevance judgments provide valuable tools for measuring music-retrieval systems. As an example of the value of these tools, we use them to compare the effect of using the expert queries and manual judgments to that of the artificial queries and manual judgments used in our earlier experiments.