12035419
A Transition Probability Model for Amino Acid Substitutions from Blocks
Substitution matrices have been useful for sequence alignment and protein sequence comparisons. The BLOSUM series of matrices, which had been derived from a database of alignments of protein blocks, improved the accuracy of alignments previously obtained from the PAM-type matrices estimated from only closely related sequences. Although BLOSUM matrices are scoring matrices now widely used for protein sequence alignments, they do not describe an evolutionary model. BLOSUM matrices do not permit the estimation of the actual number of amino acid substitutions between sequences by correcting for multiple hits. The method presented here uses the Blocks database of protein alignments, along with the additivity of evolutionary distances, to approximate the amino acid substitution probabilities as a function of actual evolutionary distance. The PMB (Probability Matrix from Blocks) defines a new evolutionary model for protein evolution that can be used for evolutionary analyses of protein sequences. Our model is directly derived from, and thus compatible with, the BLOSUM matrices. The model has the additional advantage of being easily implemented.
5737475
Toward Optimal Motif Enumeration
We present algorithms that reduce the time and space needed to solve problems of finding all motifs common to a set of sequences. In particular, we give algorithms that (1) require time and space linear in the size of the input, (2) succinctly encode the output so that the time and space requirements depend on the number of motifs, not directly on motif length, and (3) efficiently parallelize the enumeration.
4331453
Computational prediction of novel components of lung transcriptional networks
MOTIVATION
Little is known regarding the transcriptional mechanisms involved in forming and maintaining epithelial cell lineages of the mammalian respiratory tract.


RESULTS
Herein, a motif discovery approach was used to identify novel transcriptional regulators in the lung using genes previously found to be regulated by Foxa2 or Wnt signaling pathways. A human-mouse comparison of both novel and known motifs was also performed. Some of the factors and families identified here were previously shown to be involved epithelial cell differentiation (ETS family, HES-1 and MEIS-1), and ciliogenesis (RFX family), but have never been characterized in lung epithelia. Other unidentified over-represented motifs suggest the existence of novel mammalian lung transcription factors. Of the fraction of motifs examined we describe 25 transcription factor family predictions for lung. Fifteen novel factors were shown here to be expressed in mouse lung, and/or human bronchial or distal lung epithelial tissues or lung epithelial cell lineages.


AVAILABILITY
DME: http://rulai.cshl.edu/dme. MATCOMPARE: http://rulai.cshl.edu/MatCompare. MOTIFCLASS is available from the authors.
19034554
SIMPROT: Using an empirically determined indel distribution in simulations of protein evolution
BackgroundGeneral protein evolution models help determine the baseline expectations for the evolution of sequences, and they have been extensively useful in sequence analysis and for the computer simulation of artificial sequence data sets.ResultsWe have developed a new method of simulating protein sequence evolution, including insertion and deletion (indel) events in addition to amino-acid substitutions. The simulation generates both the simulated sequence family and a true sequence alignment that captures the evolutionary relationships between amino acids from different sequences. Our statistical model for indel evolution is based on the empirical indel distribution determined by Qian and Goldstein. We have parameterized this distribution so that it applies to sequences diverged by varying evolutionary times and generalized it to provide flexibility in simulation conditions. Our method uses a Monte-Carlo simulation strategy, and has been implemented in a C++ program named Simprot.ConclusionSimprot will be useful for testing methods of analysis of protein sequence families particularly alignment methods, phylogenetic tree building, detection of recombination and horizontal gene transfer, and homology detection, where knowing the true course of sequence evolution is essential.
15197279
On the complexity of finding common approximate substrings
Problems associated with finding strings that are within a specified Hamming distance of a given set of strings occur in several disciplines. In this paper, we use techniques from parameterized complexity to assess non-polynomial time algorithmic options and complexity for the COMMON APPROXIMATE SUBSTRING (CAS) problem. Our analyses indicate under which parameter restrictions useful algorithms are possible, and include both class membership and parameterized reductions to prove class hardness. In order to achieve fixed-parameter tractability, either a fixed string length or both a fixed size alphabet and fixed substring length are sufficient. Fixing either the string length or the alphabet size and Hamming distance is shown to be necessary, unless W[1] = FPT. An assortment of parameterized class membership and hardness results cover all other parameterized variants, showing in particular the effect of fixing the number of strings.
7234685
Mining ChIP-chip data for transcription factor and cofactor binding sites
MOTIVATION
Identification of single motifs and motif pairs that can be used to predict transcription factor localization in ChIP-chip data, and gene expression in tissue-specific microarray data.


RESULTS
We describe methodology to identify de novo individual and interacting pairs of binding site motifs from ChIP-chip data, using an algorithm that integrates localization data directly into the motif discovery process. We combine matrix-enumeration based motif discovery with multivariate regression to evaluate candidate motifs and identify motif interactions. When applied to the HNF localization data in liver and pancreatic islets, our methods produce motifs that are either novel or improved known motifs. All motif pairs identified to predict localization are further evaluated according to how well they predict expression in liver and islets and according to how conserved are the relative positions of their occurrences. We find that interaction models of HNF1 and CDP motifs provide excellent prediction of both HNF1 localization and gene expression in liver. Our results demonstrate that ChIP-chip data can be used to identify interacting binding site motifs.


AVAILABILITY
Motif discovery programs and analysis tools are available on request from the authors.
25357010
An Analytic and Experimental Study of Multiple Channel Controllers
A multiple channel controller (MCC) is a controller which switches a given number of channels among a larger number of input/output devices and permits simultaneous access to as many devices as there are channels available. A simple queuing model for multiple channel controllers is created, and an approximate solution for this model is generated. The approximate solution is found, using simulation, to be very close for those cases examined to the actual behavior of the model. Further simulations indicate that the approximate solution of the model appears to be robust with respect to changes in some of the assumptions used in making the approximation. Trace data taken from a real system are analyzed and they confirm the predicted utility of MCC's. The problem of optimal scheduling of MCC's is briefly discussed. Alternative system configurations are compared with the objective of minimizing queuing delays.
59837401
An Analytical Approach to Generation and Exploration of Reconfigurable Architectures
17069073
On the effectiveness of buffered and multiple arm disks
Access patterns to input/output files are usually sequential and local; a small number of files are open for each job and the file is generally read or written sequentially. This implies that if files are allocated in contiguous disk storage, the disk arms will rarely be required to move. Disk seeks can be almost entirely eliminated by using multiple arms, whereby if there are several open files on a given spindle, there will be an arm for each. Even better results can be obtained by using electronic storage such as magnetic bubbles, CCD's or electron beam memories to buffer portions of the disk. Such buffers not only eliminate seek time but reduce rotational latency to negligible amounts and decrease transfer time. We study, using trace driven simulation, the effectiveness of both buffers and multiple arms. For the data used, our experiments show that a minimum total buffer capacity of 20 tracks is required in a buffered disk. Best hit ratio results are obtained with individual buffer sizes upwards of one quarter cylinder. Three cylinder size buffers give very high hit ratios, on the order of 96%, and this appears to be a good choice for buffer size and number. LRU buffer scheduling is found to work well; comparisons with optimal, look-ahead buffer scheduling are made. Some possible models for the frequency of seeks are briefly considered.
10020032
Estimating a State-Space Model from Point Process Observations
A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.
9459767
Fault diagnosis and logic debugging using Boolean satisfiability
Recent advances in Boolean satisfiability have made it an attractive engine for solving many digital very-large-scale-integration design problems. Although useful in many stages of the design cycle, fault diagnosis and logic debugging have not been addressed within a satisfiability-based framework. This work proposes a novel Boolean satisfiability-based method for multiple-fault diagnosis and multiple-design-error diagnosis in combinational and sequential circuits. A number of heuristics are presented that keep the method memory and run-time efficient. An extensive suite of experiments on large circuits corrupted with different types of faults and errors confirm its robustness and practicality. They also suggest that satisfiability captures significant characteristics of the problem of diagnosis and encourage novel research in satisfiability-based diagnosis as a complementary process to design verification.