MOTIVATION: Non-canonical (or non-B) DNA are genomic regions whose three-dimensional conformation deviates from the canonical double helix. Non-B DNA play an important role in basic cellular processes and are associated with genomic instability, gene regulation, and oncogenesis. Experimental methods are low-throughput and can detect only a limited set of non-B DNA structures, while computational methods rely on non-B DNA base motifs, which are necessary but not sufficient indicators of non-B structures. Oxford Nanopore sequencing is an efficient and low-cost platform, but it is currently unknown whether nanopore reads can be used for identifying non-B structures. RESULTS: We build the first computational pipeline to predict non-B DNA structures from nanopore sequencing. We formalize non-B detection as a novelty detection problem and develop the GoFAE-DND, an autoencoder that uses goodness-of-fit (GoF) tests as a regularizer. A discriminative loss encourages non-B DNA to be poorly reconstructed and optimizing Gaussian GoF tests allows for the computation of P-values that indicate non-B structures. Based on whole genome nanopore sequencing of NA12878, we show that there exist significant differences between the timing of DNA translocation for non-B DNA bases compared with B-DNA. We demonstrate the efficacy of our approach through comparisons with novelty detection methods using experimental data and data synthesized from a new translocation time simulator. Experimental validations suggest that reliable detection of non-B DNA from nanopore sequencing is achievable. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/bayesomicslab/ONT-nonb-GoFAE-DND.

The detection of patterned spiking activity is important in the study of neural coding. A pattern filtering approach is developed for pattern detection under the framework of point processes, which offers flexibility in combining temporal details and firing rates. The detection combines multiple steps of filtering in a coarse-to-fine manner. Under some conditional Poisson assumptions on the spiking activity, each filtering step is equivalent to classifying by likelihood ratios all the data segments as targets or as background sequences. Unlike previous studies, where global surrogate data were used to evaluate the statistical significance of the detected patterns, a localized p-test procedure is developed, which better accounts for firing modulation and nonstationarity in spiking activity. Common temporal structures of patterned activity are learned using an entropy-based alignment procedure, without relying on metrics or pair-wise alignment. Applications of pattern filtering to single, presumptive interneurons recorded in the nucleus HVc of zebra finch are illustrated. These demonstrate a match between the auditory-evoked response to playback of the individual bird's own song and spontaneous activity during sleep. Small temporal compression or expansion, or both, is required for optimal matching of spontaneous patterns to stimulus-evoked activity.

Let X and Y be two independent stationary processes on general metric spaces, with distributions P and Q, respectively. The first-order asymptotic of the waiting time W/sub n/(D) between X and Y, allowing distortion, is established in the presence of one-sided /spl psi/-mixing conditions for Y. With probability one, n/sup -1/log W/sub n/(D) has the same limit as -n/sup -1/logQ(B(X/sub 1//sup n/, D)), where Q(B(X/sub 1//sup n/, D)) is the Q-measure of the D-ball around (X/sub 1/,...,X/sub n/), with respect to a given distortion measure. Large deviations techniques are used to get the convergence of -n/sup -1/log Q(B(X/sub 1//sup n/, D)). First, a sequence of functions R/sub n/ in terms of the marginal distributions of X/sub 1//sup n/ and Y/sub 1//sup n/ as well as D are constructed and demonstrated to converge to a function R(P, Q, D). The functions R/sub n/ and R(P, Q, D) are different from rate distortion functions. Then -n/sup -1/logQ(B(X/sub 1//sup n/, D)) is shown to converge to R(P, Q, D) with probability one.

Log-linear models provide a statistically sound framework for Stochastic "Unification-Based" Grammars (SUBGs) and stochastic versions of other kinds of grammars. We describe two computationally-tractable ways of estimating the parameters of such grammars from a training corpus of syntactic analyses, and apply these to estimate a stochastic version of Lexical-Functional Grammar.

Nonparametric estimation is usually inconsistent without some form of regularization. One way to impose regularity is through a prior measure. Barron and Cover (1991) have shown that complexity based prior measures can insure consistency, at least when restricted to countable dense subsets of the infinite-dimensional parameter (i.e., function) space. Strangely, however, these results are independent of the actual complexity assignment: the same results hold under an arbitrary permutation of the match-up of complexities to functions. We show that this phenomenon is related to the weakness of the convergence measures used. Stronger convergence can only be achieved through complexity measures that relate to the actual behavior of the functions.