A framework for orthology assignment from gene rearrangement data

Abstract. Gene rearrangements have successfully been used in phylogenetic reconstruction and comparative genomics, but usually under the assumption that all genomes have the same gene content and that no gene is duplicated. While these assumptions allow one to work with organellar genomes, they are too restrictive when comparing nuclear genomes. The main challenge is how to deal with gene families, specifically, how to identify orthologs. While searching for orthologies is a common task in computational biology, it is usually done using sequence data. We approach that problem using gene rearrangement data, provide an optimization framework in which to phrase the problem, and present some preliminary theoretical results.

Materializing digital collecting: an extended view of digital materiality

If digital objects are abundant and ubiquitous, why should consumers pay for, much less collect them? The qualities of digital code present numerous challenges for collecting, yet digital collecting can and does occur. We explore the role of companies in constructing digital consumption objects that encourage and support collecting behaviours, identifying material configuration techniques that materialise these objects as elusive and authentic. Such techniques, we argue, may facilitate those pleasures of collecting otherwise absent in the digital realm. We extend theories of collecting by highlighting the role of objects and the companies that construct them in materialising digital collecting. More broadly, we extend theories of digital materiality by highlighting processes of digital material configuration that occur in the pre-objectification phase of materialisation, acknowledging the role of marketing and design in shaping the qualities exhibited by digital consumption objects and consequently related consumption behaviours and experiences

Cabotegravir for HIV Prevention in Cisgender Men and Transgender Women

Background: Safe and effective long-acting injectable agents for preexposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) infection are needed to increase the options for preventing HIV infection. Methods: We conducted a randomized, double-blind, double-dummy, noninferiority trial to compare long-acting injectable cabotegravir (CAB-LA, an integrase strand-transfer inhibitor [INSTI]) at a dose of 600 mg, given intramuscularly every 8 weeks, with daily oral tenofovir disoproxil fumarate-emtricitabine (TDF-FTC) for the prevention of HIV infection in at-risk cisgender men who have sex with men (MSM) and in at-risk transgender women who have sex with men. Participants were randomly assigned (1:1) to receive one of the two regimens and were followed for 153 weeks. HIV testing and safety evaluations were performed. The primary end point was incident HIV infection. Results: The intention-to-treat population included 4566 participants who underwent randomization; 570 (12.5%) identified as transgender women, and the median age was 26 years (interquartile range, 22 to 32). The trial was stopped early for efficacy on review of the results of the first preplanned interim end-point analysis. Among 1698 participants from the United States, 845 (49.8%) identified as Black. Incident HIV infection occurred in 52 participants: 13 in the cabotegravir group (incidence, 0.41 per 100 person-years) and 39 in the TDF-FTC group (incidence, 1.22 per 100 person-years) (hazard ratio, 0.34; 95% confidence interval, 0.18 to 0.62). The effect was consistent across prespecified subgroups. Injection-site reactions were reported in 81.4% of the participants in the cabotegravir group and in 31.3% of those in the TDF-FTC group. In the participants in whom HIV infection was diagnosed after exposure to CAB-LA, INSTI resistance and delays in the detection of HIV infection were noted. No safety concerns were identified. Conclusions: CAB-LA was superior to daily oral TDF-FTC in preventing HIV infection among MSM and transgender women. Strategies are needed to prevent INSTI resistance in cases of CAB-LA PrEP failure

Haemophilia

Haemophilia A and B are rare congenital, recessive X-linked disorders caused by lack or deficiency of clotting factor VIII (FVIII) or IX (FIX), respectively. The severity of the disease depends on the reduction of levels of FVIII or FIX, which are determined by the type of the causative mutation in the genes encoding the factors (F8 and F9, respectively). The hallmark clinical characteristic, especially in untreated severe forms, is bleeding (spontaneous or after trauma) into major joints such as ankles, knees and elbows, which can result in the development of arthropathy. Intracranial bleeds and bleeds into internal organs may be life-threatening. The median life expectancy was ~30 years until the 1960s, but improved understanding of the disorder and development of efficacious therapy based on prophylactic replacement of the missing factor has caused a paradigm shift, and today individuals with haemophilia can look forward to a virtually normal life expectancy and quality of life. Nevertheless, the potential development of inhibitory antibodies to infused factor is still a major hurdle to overcome in a substantial proportion of patients. Finally, gene therapy for both types of haemophilia has progressed remarkably and could soon become a reality

Linear programming for phylogenetic reconstruction based on gene rearrangements

Phylogenetic reconstruction from gene rearrangements has attracted increasing attention from biologists and computer scientists over the last few years. Methods used in reconstruction include distance-based methods, parsimony methods using sequence-based encodings, and direct optimization. The latter, pioneered by Sankoff and extended by us with the software suite GRAPPA, is the most accurate approach, but has been limited to small genomes because the running time of its scoring algorithm grows exponentially with the number of genes in the genome. We report here on a new method to compute a tight lower bound on the score of a given tree, using a set of linear constraints generated through selective applications of the triangle inequality. Our method generates an integer linear program with a carefully limited number of constraints, rapidly solves its relaxed version, and uses the result to provide a tight lower bound. Since this bound is very close to the optimal tree score, it can be used directly as a selection criterion, thereby enabling us to bypass entirely the expensive scoring procedure. We have implemented this method within our GRAPPA software and run several series of experiments on both biological and simulated datasets to assess its accuracy. Our results show that using the bound as a selection criterion yields excellent trees, with error rates below 5 % up to very large evolutionary distances, consistently beating the baseline Neighbor-Joining. Our new method enables us to extend the range of applicability of the direct optimization method to chromosomes of size comparable to those of bacteria, as well as to datasets with complex combinations of evolutionary events.

Quartet-Based Phylogeny Reconstruction from Gene Orders

Phylogenetic reconstruction from gene-rearrangement data is attracting increasing attention from biologists and computer scientists. Methods used in reconstruction include distance-based methods, parsimony methods using sequence encodings, and direct optimization. The latter, pioneered by Sankoff and extended by us with the software suiteGRAPPA, is the most accurate approach; however, its exhaustive approach means that it can be applied only to small datasets of fewer than 15 taxa. While we have successfully scaled it up to 1,000 genomes by integrating it with a disk-covering method (DCM-GRAPPA), the recursive decomposition may need many levels of recursion to handle datasets with 1,000 or more genomes. We thus investigated quartet-based approaches, which directly decompose the datasets into subsets of four taxa each; such approaches have been well studied for sequence data, but not for gene-rearrangement data. We give an optimization algorithm for the NP-hard problem of computing optimal trees for each quartet, present a variation of the dyadic method (using heuristics to choose suitable short quartets), and use both in simulation studies. We find that our quartet-based method can handle more genomes than the base version of GRAPPA, thus enabling us to reduce the number of levels of recursion in DCM-GRAPPA, but is more sensitive to the rate of evolution, with error rates rapidly increasing when saturation is approached