Stony Brook University

About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.

TransRate: reference-free quality assessment of de novo transcriptome assemblies

Abstract: TransRate is a tool for reference-free quality assessment of de novo transcriptome assemblies Using only the sequenced reads and the assembly as input, we show that multiple common artifacts of de novo transcriptome assembly can be readily detected These include chimeras, structural errors, incomplete assembly, and base errors TransRate evaluates these errors to produce a diagnostic quality score for each contig, and these contig scores are integrated to evaluate whole assemblies Thus, TransRate can be used for de novo assembly filtering and optimization as well as comparison of assemblies generated using different methods from the same input reads Applying the method to a data set of 155 published de novo transcriptome assemblies, we deconstruct the contribution that assembly method, read length, read quantity, and read quality make to the accuracy of de novo transcriptome assemblies and reveal that variance in the quality of the input data explains 43% of the variance in the quality of published de novo transcriptome assemblies Because TransRate is reference-free, it is suitable for assessment of assemblies of all types of RNA, including assemblies of long noncoding RNA, rRNA, mRNA, and mixed RNA samples

Spatial autocorrelation in biology: 2. Some biological implications and four applications of evolutionary and ecological interest

Abstract: Spatial autocorrelation analysis tests whether the observed value of a variable at one locality is significantly dependent on values of the variable at neighbouring localities. The method was extended by us in an earlier paper to include the computation of correlograms for spatial autocorrelation. These show the autocorrelation coefficient as a function of distance between pairs of localities, and summarize the patterns of geographic variation exhibited by the response surface of any given variable. Identical variation patterns lead to identical correlograms, but different patterns may or may not yield different correlograms. Similarity in the correlograms of different variation patterns suggests similarity in the generating mechanism of the pattern. The inferences that can be drawn from correlograms are discussed and illustrated. Examination and analysis of variation patterns of several characters or gene frequencies for one population, or of several populations in different places or at different times, permit some conclusions about the nature of the populational processes generating the observed patterns. Autocorrelation analysis is applied to four biological situations differing in the nature of the data (interval or nominal), in the type of grid connecting the localities (regular or irregular), and the field of application (evolution or ecology). The examples comprise genotypes of individual mice, blood group frequencies in humans, gene frequency variation in a perennial herb, and the distribution of species of trees. The implications of our findings are discussed.

CCS expressions finite state processes, and three problems of equivalence

Abstract: We examine the computational complexity of testing finite state processes for equivalence in Milner's Calculus of Communicating Systems (CCS). The equivalence problems in CCS are presented as refinements of the familiar problem of testing whether two nondeterministic finite automata (NFA) are equivalent, i.e., accept the same language. Three notions of equivalence proposed for CCS are investigated, namely, observational equivalence, strong observational equivalence, and failure equivalence. We show that observational equivalence can be tested in polynomial time. As defined in CCS, observational equivalence is the limit of a sequence of successively finer equivalence relations, ≈k, where ≈1 is nondeterministic finite automaton equivalence. We prove that, for each fixed k, deciding ≈k is PSPACE-complete. We show that strong observational equivalence can be decided in polynomial time by reducing it to generalized partitioning, a new combinatorial problem of independent interest. Finally, we demonstrate that testing for failure equivalence is PSPACE-complete, even for a very restricted type of process.

Strategies for analyzing ecological momentary assessment data

Abstract: Studies incorporating repeated observations of momentary phenomena are becoming more common in behavioral and medical science. Analysis of such data requires the use of statistical techniques that are unfamiliar to many investigators. Some common ways of analyzing momentary data are reviewed--aggregation strategies, repeated measures analysis of variance, pooled within-person regression, and two-stage estimation procedures for multilevel models--and are found to be usually suboptimal, possibly leading to incorrect inferences. A broad class of statistical models for multilevel data that can address many research questions typically asked of momentary data are then described. Analytic issues that merit careful consideration include the scaling of momentary variables, allowance for serial autocorrelation of residuals, and the treatment of coefficients that vary across individuals as fixed versus random effects.