Efficient representation of uncertainty in multiple sequence alignments using directed acyclic graphs

Background A standard procedure in many areas of bioinformatics is to use a single multiple sequence alignment (MSA) as the basis for various types of analysis. However, downstream results may be highly sensitive to the alignment used, and neglecting the uncertainty in the alignment can lead to significant bias in the resulting inference. In recent years, a number of approaches have been developed for probabilistic sampling of alignments, rather than simply generating a single optimum. However, this type of probabilistic information is currently not widely used in the context of downstream inference, since most existing algorithms are set up to make use of a single alignment. Results In this work we present a framework for representing a set of sampled alignments as a directed acyclic graph (DAG) whose nodes are alignment columns; each path through this DAG then represents a valid alignment. Since the probabilities of individual columns can be estimated from empirical frequencies, this approach enables sample-based estimation of posterior alignment probabilities. Moreover, due to conditional independencies between columns, the graph structure encodes a much larger set of alignments than the original set of sampled MSAs, such that the effective sample size is greatly increased. Conclusions The alignment DAG provides a natural way to represent a distribution in the space of MSAs, and allows for existing algorithms to be efficiently scaled up to operate on large sets of alignments. As an example, we show how this can be used to compute marginal probabilities for tree topologies, averaging over a very large number of MSAs. This framework can also be used to generate a statistically meaningful summary alignment; example applications show that this summary alignment is consistently more accurate than the majority of the alignment samples, leading to improvements in downstream tree inference. Implementations of the methods described in this article are available at http://statalign.github.io/WeaveAlign webcite

Epilepsy and driving

Epilepsy is a disease with high prevalence, which interferes driving and may lead to car accident; This case-control study has been done on 100 epileptic patients and 100 persons as control group, who had history of driving. We gathered our patients with face to face interview and registering their information in special forms which were prepared for this study. There were three times more accidents among epileptic cases comparing with control group and this difference was more considerable in men and in patients under 35 years old. The cause of accident were not seizure attack in more than 60% of the patients and these ordinary accidents were also more in case group. Epileptic patients with history of car accidents during driving had poor drug compliance comparing with the epileptics without history of an accident so drug compliance may be valuable in predicting accident in these patients. We have also found poor drug compliance in whom seizure attacks caused accident for them. 58% of the epileptics had not consulted their physician about driving. 43.3% of seizures during driving were of generalized type and none of the patients had inform police about their disease during getting driving license