Darya Chudova

Circadian clock genes contribute to the regulation of hair follicle cycling.

Hair follicles undergo recurrent cycling of controlled growth (anagen), regression (catagen), and relative quiescence (telogen) with a defined periodicity. Taking a genomics approach to study gene expression during synchronized mouse hair follicle cycling, we discovered that, in addition to circadian fluctuation, CLOCK–regulated genes are also modulated in phase with the hair growth cycle. During telogen and early anagen, circadian clock genes are prominently expressed in the secondary hair germ, which contains precursor cells for the growing follicle. Analysis of Clock and Bmal1 mutant mice reveals a delay in anagen progression, and the secondary hair germ cells show decreased levels of phosphorylated Rb and lack mitotic cells, suggesting that circadian clock genes regulate anagen progression via their effect on the cell cycle. Consistent with a block at the G1 phase of the cell cycle, we show a significant upregulation of p21 in Bmal1 mutant skin. While circadian clock mechanisms have been implicated in a variety of diurnal biological processes, our findings indicate that circadian clock genes may be utilized to modulate the progression of non-diurnal cyclic processes.

Translation-invariant mixture models for curve clustering

In this paper we present a family of algorithms that can simultaneously align and cluster sets of multidimensional curves defined on a discrete time grid. Our approach uses the Expectation-Maximization (EM) algorithm to recover both the mean curve shapes for each cluster, and the most likely shifts, offsets, and cluster memberships for each curve. We demonstrate how Bayesian estimation methods can improve the results for small sample sizes by enforcing smoothness in the cluster mean curves. We evaluate the methodology on two real-world data sets, time-course gene expression data and storm trajectory data. Experimental results show that models that incorporate curve alignment systematically provide improvements in predictive power and within-cluster variance on test data sets. The proposed approach provides a non-parametric, computationally efficient, and robust methodology for clustering broad classes of curve data.

Bayesian detection of non-sinusoidal periodic patterns in circadian expression data

MOTIVATION Cyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns. RESULTS We present an analysis of variance (ANOVA) periodicity detector and its Bayesian extension that can be used to discover periodic transcripts of arbitrary shapes from replicated gene expression profiles. The models are applicable when the profiles are collected at comparable time points for at least two cycles. We provide an empirical Bayes procedure for estimating parameters of the prior distributions and derive closed-form expressions for the posterior probability of periodicity, enabling efficient computation. The model is applied to two datasets profiling circadian regulation in murine liver and skeletal muscle, revealing a substantial number of previously undetected non-sinusoidal periodic transcripts in each. We also apply quantitative real-time PCR to several highly ranked non-sinusoidal transcripts in liver tissue found by the model, providing independent evidence of circadian regulation of these genes. AVAILABILITY Matlab software for estimating prior distributions and performing inference is available for download from http://www.datalab.uci.edu/resources/periodicity/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Analysis of Pattern Discovery in Sequences Using a Bayes Error Framework

In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences. An important real-world problem of this nature is motif discovery in DNA sequences. There are a number of fundamental aspects of this data mining problem that can make discovery “easy” or “hard”—we characterize the difficulty of this problem using an analysis based on the Bayes error rate under a Markov assumption. The Bayes error framework demonstrates why certain patterns are much harder to discover than others. It also explains the role of different parameters such as pattern length and pattern frequency in sequential discovery. We demonstrate how the Bayes error can be used to calibrate existing discovery algorithms, providing a lower bound on achievable performance. We discuss a number of fundamental issues that characterize sequential pattern discovery in this context, present a variety of empirical results to complement and verify the theoretical analysis, and apply our methodology to real-world motif-discovery problems in computational biology.