Measuring Global Credibility with Application to Local Sequence Alignment

Computational biology is replete with high-dimensional (high-D) discrete prediction and inference problems, including sequence alignment, RNA structure prediction, phylogenetic inference, motif finding, prediction of pathways, and model selection problems in statistical genetics. Even though prediction and inference in these settings are uncertain, little attention has been focused on the development of global measures of uncertainty. Regardless of the procedure employed to produce a prediction, when a procedure delivers a single answer, that answer is a point estimate selected from the solution ensemble, the set of all possible solutions. For high-D discrete space, these ensembles are immense, and thus there is considerable uncertainty. We recommend the use of Bayesian credibility limits to describe this uncertainty, where a (1−α)%, 0≤α≤1, credibility limit is the minimum Hamming distance radius of a hyper-sphere containing (1−α)% of the posterior distribution. Because sequence alignment is arguably the most extensively used procedure in computational biology, we employ it here to make these general concepts more concrete. The maximum similarity estimator (i.e., the alignment that maximizes the likelihood) and the centroid estimator (i.e., the alignment that minimizes the mean Hamming distance from the posterior weighted ensemble of alignments) are used to demonstrate the application of Bayesian credibility limits to alignment estimators. Application of Bayesian credibility limits to the alignment of 20 human/rodent orthologous sequence pairs and 125 orthologous sequence pairs from six Shewanella species shows that credibility limits of the alignments of promoter sequences of these species vary widely, and that centroid alignments dependably have tighter credibility limits than traditional maximum similarity alignments

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

Deubiquitinase-based analysis of ubiquitin chain architecture using Ubiquitin Chain Restriction (UbiCRest)

Protein ubiquitination is a versatile protein modification that regulates virtually all cellular processes. This versatility originates from polyubiquitin chains, which can be linked in eight distinct ways. The combinatorial complexity of eight linkage types in homotypic (one chain type per polymer) and heterotypic (multiple linkage types per polymer) chains poses significant problems for biochemical analysis. Here we describe UbiCRest, in which substrates (ubiquitinated proteins or polyubiquitin chains) are treated with a panel of linkage-specific deubiquitinating enzymes (DUBs) in parallel reactions, followed by gel-based analysis. UbiCRest can be used to show that a protein is ubiquitinated, to identify which linkage type(s) are present on polyubiquitinated proteins and to assess the architecture of heterotypic polyubiquitin chains. DUBs used in UbiCRest can be obtained commercially; however, we include details for generating a toolkit of purified DUBs and for profiling their linkage preferences in vitro. UbiCRest is a qualitative method that yields insights into ubiquitin chain linkage types and architecture within hours, and it can be performed on western blotting quantities of endogenously ubiquitinated proteins

The pilocarpine model of epilepsy: what have we learned?

The systemic administration of a potent muscarinic agonist pilocarpine in rats promotes sequential behavioral and electrographic changes that can be divided into 3 distinct periods: (a) an acute period that built up progressively into a limbic status epilepticus and that lasts 24 h, (b) a silent period with a progressive normalization of EEG and behavior which varies from 4 to 44 days, and (c) a chronic period with spontaneous recurrent seizures (SRSs). The main features of the SRSs observed during the long-term period resemble those of human complex partial seizures and recurs 2-3 times per week per animal. Therefore, the pilocarpine model of epilepsy is a valuable tool not only to study the pathogenesis of temporal lobe epilepsy in human condition, but also to evaluate potential antiepileptogenic drugs. This review concentrates on data from pilocarpine model of epilepsy.<br>A administração sistêmica do potente agonista muscarínico pilocarpina em ratos promove alterações comportamentais e eletrográficas que podem ser divididas em três períodos distintos: (a) período agudo o animal evolui progressivamente para o status epilepticus, que perdura por até 24h; (b) período silencioso, caracterizado pela normalização progressiva do comportamento e do EEG e pode ter uma duração de 4 a 44 dias; período crônico, aparecimento de crises epilépticas espontâneas e recorrentes (SRSs). As características das SRSs observadas nos animas durante o período crônico são semelhantes às crises parciais complexas dos seres humanos e recorrem de 2-3 vezes por semana/animal. Além disso, o modelo de epilepsia induzido pela pilocarpina é válido não somente para se estudar a patogênese da epilepsia do lobo temporal em humanos como também para se testar a viabilidade de drogas antiepilépticas. Esse artigo de revisão aborda diversos aspectos do modelo de epilepsia induzido pela pilocarpina