Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require at least quadratic time as a function of the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become
Background Opioid‐induced hyperalgesia is a state of nociceptive sensitisation secondary to opioid administration. The objective of this meta‐analysis was to test the hypothesis that high‐dose intraoperative opioids contribute to increased post‐operative pain and hyperalgesia when compared with a low‐dose regimen in patients under general anaesthesia. Methods We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses statement guidelines and rated the certainty of evidence with the Grading of Recommendations, Assessment, Development and Evaluation system. Only trials investigating pain outcomes and comparing two different dosages of the same intraoperative opioid in patients under general anaesthesia were included. The primary outcome was pain score (analogue scale, 0‐10) at 24 post‐operative hours. Secondary outcomes included pain score and cumulative intravenous morphine equivalents (mg) consumed at 2 post‐operative hours, together with mechanical pain threshold (g·mm−2). Results Twenty‐seven randomised controlled trials, including 1630 patients, were identified. Pain score at rest at 24 post‐operative hours was increased in the high‐dose group (mean difference [95% CI]: −0.2 [−0.4, −0.1]; trial sequential analysis‐adjusted CI: −0.4, −0.02; low certainty of evidence). Similarly, at 2 post‐operative hours, both pain score (mean difference [95% CI]: −0.4 [−0.6, −0.2]; low certainty of evidence) and cumulative intravenous morphine equivalents consumed (mean difference [95% CI]: −1.6 mg [−2.6, −0.7]; low certainty of evidence) were significantly higher in the high‐dose group. Finally, the threshold for mechanical pain was significantly lower in the high‐dose group (mean difference to pressure [95% CI]: 3.8 g·mm−2 [1.8, 5.8]; low certainty of evidence). Conclusions There is low certainty of evidence that high‐dose intraoperative opioid administration increases pain scores in the post‐operative period, when compared with a low‐dose regimen.
Phylogenetic profiling is a computational method to predict genes involved in the same biological process by identifying protein families which tend to be jointly lost or retained across the tree of life. Phylogenetic profiling has customarily been more widely used with prokaryotes than eukaryotes, because the method is thought to require many diverse genomes. There are now many eukaryotic genomes available, but these are considerably larger, and typical phylogenetic profiling methods require quadratic time or worse in the number of genes. We introduce a fast, scalable phylogenetic profiling approach entitled HogProf, which leverages hierarchical orthologous groups for the construction of large profiles and locality-sensitive hashing for efficient retrieval of similar profiles. We show that the approach outperforms Enhanced Phylogenetic Tree, a phylogeny-based method, and use the tool to reconstruct networks and query for interactors of the kinetochore complex as well as conserved proteins involved in sexual reproduction: Hap2, Spo11 and Gex1. HogProf enables large-scale phylogenetic profiling across the three domains of life, and will be useful to predict biological pathways among the hundreds of thousands of eukaryotic species that will become available in the coming few years. HogProf is available at https://github.com/DessimozLab/HogProf .
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