MOTIVATION: The exponential reduction in cost of genome sequencing has resulted in a rapid growth of genomic data. Most of the entropy of short read data lies not in the sequence of read bases themselves but in their Quality Scores-the confidence measurement that each base has been sequenced correctly. Lossless compression methods are now close to their theoretical limits and hence there is a need for lossy methods that further reduce the complexity of these data without impacting downstream analyses. RESULTS: We here propose GeneCodeq, a Bayesian method inspired by coding theory for adjusting quality scores to improve the compressibility of quality scores without adversely impacting genotyping accuracy. Our model leverages a corpus of k-mers to reduce the entropy of the quality scores and thereby the compressibility of these data (in FASTQ or SAM/BAM/CRAM files), resulting in compression ratios that significantly exceeds those of other methods. Our approach can also be combined with existing lossy compression schemes to further reduce entropy and allows the user to specify a reference panel of expected sequence variations to improve the model accuracy. In addition to extensive empirical evaluation, we also derive novel theoretical insights that explain the empirical performance and pitfalls of corpus-based quality score compression schemes in general. Finally, we show that as a positive side effect of compression, the model can lead to improved genotyping accuracy. AVAILABILITY AND IMPLEMENTATION: GeneCodeq is available at: github.com/genecodeq/eval CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

ThinSight is a novel optical sensing system, fully integrated into a thin form factor display, capable of detecting multi-ple fingers placed on or near the display surface. We describe this new hardware in detail, and demonstrate how it can be embedded behind a regular LCD, allowing sensing without degradation of display capability. With our approach, fingertips and hands are clearly identifiable through the display. The approach of optical sensing also opens up the exciting possibility for detecting other physical objects and visual markers through the display, and some initial experiments are described. We also discuss other novel capabilities of our system: interaction at a distance using IR pointing devices, and IR-based communication with other electronic devices through the display. A major advantage of ThinSight over existing camera and projector based optical systems is its compact, thin form-factor making such systems even more deployable. We therefore envisage using ThinSight to capture rich sensor data through the display which can be processed using computer vision techniques to enable both multi-touch and tangible interaction.