Document Retrieval on Repetitive Collections

Document retrieval aims at finding the most important documents where a pattern appears in a collection of strings. Traditional pattern-matching techniques yield brute-force document retrieval solutions, which has motivated the research on tailored indexes that offer near-optimal performance. However, an experimental study establishing which alternatives are actually better than brute force, and which perform best depending on the collection characteristics, has not been carried out. In this paper we address this shortcoming by exploring the relationship between the nature of the underlying collection and the performance of current methods. Via extensive experiments we show that established solutions are often beaten in practice by brute-force alternatives. We also design new methods that offer superior time/space trade-offs, particularly on repetitive collections.Comment: Accepted to ESA 2014. Implementation and experiments at http://www.cs.helsinki.fi/group/suds/rlcsa

RLZAP: Relative Lempel-Ziv with Adaptive Pointers

Relative Lempel-Ziv (RLZ) is a popular algorithm for compressing databases of genomes from individuals of the same species when fast random access is desired. With Kuruppu et al.'s (SPIRE 2010) original implementation, a reference genome is selected and then the other genomes are greedily parsed into phrases exactly matching substrings of the reference. Deorowicz and Grabowski (Bioinformatics, 2011) pointed out that letting each phrase end with a mismatch character usually gives better compression because many of the differences between individuals' genomes are single-nucleotide substitutions. Ferrada et al. (SPIRE 2014) then pointed out that also using relative pointers and run-length compressing them usually gives even better compression. In this paper we generalize Ferrada et al.'s idea to handle well also short insertions, deletions and multi-character substitutions. We show experimentally that our generalization achieves better compression than Ferrada et al.'s implementation with comparable random-access times

Genome‐wide off‐target analyses of CRISPR/Cas9‐mediated T‐cell receptor engineering in primary human T cells

Objectives Exploiting the forces of human T cells for treatment has led to the current paradigm of emerging immunotherapy strategies. Genetic engineering of the T-cell receptor (TCR) redirects specificity, ablates alloreactivity and brings significant progress and off-the-shelf options to emerging adoptive T-cell transfer (ACT) approaches. Targeted CRISPR/Cas9-mediated double-strand breaks in the DNA enable knockout or knock-in engineering. Methods Here, we perform CRISPR/Cas9-mediated TCR knockout using a therapeutically relevant ribonucleoprotein (RNP) delivery method to assess the safety of genetically engineered T-cell products. Whole-genome sequencing was performed to analyse whether CRISPR/Cas9-mediated DNA double-strand break at the TCR locus is associated with off-target events in human primary T cells. Results TCRα chain and TCRβ chain knockout leads to high on-target InDel frequency and functional knockout. None of the predicted off-target sites could be confirmed experimentally, whereas whole-genome sequencing and manual Integrative Genomics Viewer (IGV) review revealed 9 potential low-frequency off-target events genome-wide. Subsequent amplification and targeted deep sequencing in 7 of 7 evaluable loci did not confirm these low-frequency InDels. Therefore, off-target events are unlikely to be caused by the CRISPR/Cas9 engineering. Conclusion The combinatorial approach of whole-genome sequencing and targeted deep sequencing confirmed highly specific genetic engineering using CRISPR/Cas9-mediated TCR knockout without potentially harmful exonic off-target effects

Cognitive impairment induced by delta9-tetrahydrocannabinol occurs through heteromers between cannabinoid CB1 and serotonin 5-HT2A receptors

Delta-9-tetrahydrocannabinol (THC), the main psychoactive compound of marijuana, induces numerous undesirable effects, including memory impairments, anxiety, and dependence. Conversely, THC also has potentially therapeutic effects, including analgesia, muscle relaxation, and neuroprotection. However, the mechanisms that dissociate these responses are still not known. Using mice lacking the serotonin receptor 5-HT2A, we revealed that the analgesic and amnesic effects of THC are independent of each other: while amnesia induced by THC disappears in the mutant mice, THC can still promote analgesia in these animals. In subsequent molecular studies, we showed that in specific brain regions involved in memory formation, the receptors for THC and the 5-HT2A receptors work together by physically interacting with each other. Experimentally interfering with this interaction prevented the memory deficits induced by THC, but not its analgesic properties. Our results highlight a novel mechanism by which the beneficial analgesic properties of THC can be dissociated from its cognitive side effects

Four Distances between Pairs of Amino Acids Provide a Precise Description of their Interaction

The three-dimensional structures of proteins are stabilized by the interactions between amino acid residues. Here we report a method where four distances are calculated between any two side chains to provide an exact spatial definition of their bonds. The data were binned into a four-dimensional grid and compared to a random model, from which the preference for specific four-distances was calculated. A clear relation between the quality of the experimental data and the tightness of the distance distribution was observed, with crystal structure data providing far tighter distance distributions than NMR data. Since the four-distance data have higher information content than classical bond descriptions, we were able to identify many unique inter-residue features not found previously in proteins. For example, we found that the side chains of Arg, Glu, Val and Leu are not symmetrical in respect to the interactions of their head groups. The described method may be developed into a function, which computationally models accurately protein structures