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0%; work impairment, 37.1% vs. 23.3%; overall activity impairment, 39.8% vs. 25.3%; all p  less then  0.05). CONCLUSION HRQoL and work productivity were significantly impacted in TIRs versus TRs in this real-world analysis of patients with migraine acutely treated with triptans, highlighting the need for more effective treatments for patients with an insufficient triptan response. Further research is needed to establish causal relationships between insufficient response and these outcomes.BACKGROUND The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains wia proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone.BACKGROUND Circular RNAs (circRNAs) are a newly appreciated class of non-coding RNA molecules. Numerous tools have been developed for the detection of circRNAs, however computational tools to perform downstream functional analysis of circRNAs are scarce. RESULTS We present circRNAprofiler, an R-based computational framework that runs after circRNAs have been identified. It allows to combine circRNAs detected by multiple publicly available annotation-based circRNA detection tools and to analyze their expression, genomic context, evolutionary conservation, biogenesis and putative functions. CONCLUSIONS Overall, the circRNA analysis workflow implemented by circRNAprofiler is highly automated and customizable, and the results of the analyses can be used as starting point for further investigation in the role of specific circRNAs in any physiological or pathological condition.BACKGROUND The rnpB gene encodes for an essential catalytic RNA (RNase P). Like other essential RNAs, RNase P's sequence is highly variable. However, unlike other essential RNAs (i.e. tRNA, 16 S, 6 S,...) its structure is also variable with at least 5 distinct structure types observed in prokaryotes. This structural variability makes it labor intensive and challenging to create and maintain covariance models for the detection of RNase P RNA in genomic and metagenomic sequences. The lack of a facile and rapid annotation algorithm has led to the rnpB gene being the most grossly under annotated essential gene in completed prokaryotic genomes with only a 24% annotation rate. Here we describe the coupling of the largest RNase P RNA database with the local alignment scoring algorithm to create the most sensitive and rapid prokaryote rnpB gene identification and annotation algorithm to date. RESULTS Of the 2772 completed microbial genomes downloaded from GenBank only 665 genomes had an annotated rnpB gene. We applieree available for download at https//github.com/JChristopherEllis/P-Finder as well as a small sample RNase P RNA file for testing.BACKGROUND Motility in bacteria forms the basis for taxis and is in some pathogenic bacteria important for virulence. Video tracking of motile bacteria allows the monitoring of bacterial swimming behaviour and taxis on the level of individual cells, which is a prerequisite to study the underlying molecular mechanisms. RESULTS The open-source python program YSMR (Your Software for Motility Recognition) was designed to simultaneously track a large number of bacterial cells on standard computers from video files in various formats. In order to cope with the high number of tracked objects, we use a simple detection and tracking approach based on grey-value and position, followed by stringent selection against suspicious data points. The generated data can be used for statistical analyses either directly with YSMR or with external programs. Aminoguanidine hydrochloride research buy CONCLUSION In contrast to existing video tracking software, which either requires expensive computer hardware or only tracks a limited number of bacteria for a few seconds, YSMR is an open-source program which allows the 2-D tracking of several hundred objects over at least 5 minutes on standard computer hardware. The code is freely available at https//github.com/schwanbeck/YSMR.BACKGROUND Network motifs are connectivity structures that occur with significantly higher frequency than chance, and are thought to play important roles in complex biological networks, for example in gene regulation, interactomes, and metabolomes. Network motifs may also become pivotal in the rational design and engineering of complex biological systems underpinning the field of synthetic biology. Distinguishing true motifs from arbitrary substructures, however, remains a challenge. RESULTS Here we demonstrate both theoretically and empirically that implicit assumptions present in mainstream methods for motif identification do not necessarily hold, with the ramification that motif studies using these mainstream methods are less able to effectively differentiate between spurious results and events of true statistical significance than is often presented. We show that these difficulties cannot be overcome without revising the methods of statistical analysis used to identify motifs. CONCLUSIONS Present-day methods for the discovery of network motifs, and, indeed, even the methods for defining what they are, are critically reliant on a set of incorrect assumptions, casting a doubt on the scientific validity of motif-driven discoveries. The implications of these findings are therefore far-reaching across diverse areas of biology.