Sheila M. Reynolds

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Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal(More)
The fundamental building block of chromatin, the nucleosome, occupies 150 bp of DNA in a spaced arrangement that is a primary determinant in regulation of the genome. The nucleosomal organization of some regions of the human genome has been described, but mapping of these regions has been limited to a few kilobases. We have explored two independent and(More)
We present a part-of-speech tagger which introduces two new concepts: virtual evidence in the form of an " observed child " node, and negative training data to learn the conditional probabilities for the observed child. Associated with each word is a flexible feature-set which can include binary flags, neighboring words, etc. The conditional probability of(More)
MOTIVATION Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their fragmentation patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation.(More)
DNA periodicity and its relationship to the formation of nu-cleosomes has been investigated extensively using autocorrelation and Fourier transform methods. We provide a precise treatment of the mathematical foundation for this type of analysis, and we apply the resulting method to quantify dinucleotide periodicity in several datasets. We begin by(More)
We describe a probabilistic model, implemented as a dynamic Bayesian network, that can be used to predict nucleosome positioning along a chromosome based on one or more genomic input tracks containing position-specific information (evidence). Previous models have either made predictions based on primary DNA sequence alone, or have been used to infer(More)
Motivation: Tandem mass spectrometry (MS/MS) is an indispensable technology for identification of proteins from complex mixtures. Proteins are digested to peptides that are then identified by their frag-mention patterns in the mass spectrometer. Thus, at its core, MS/MS protein identification relies on the relative predictability of peptide fragmentation.(More)
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