5601540
A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences
MOTIVATION
Transcription factors (TFs) regulate gene expression by recognizing and binding to specific regulatory regions on the genome, which in higher eukaryotes can occur far away from the regulated genes. Recently, Affymetrix developed the high-density oligonucleotide arrays that tile all the non-repetitive sequences of the human genome at 35 bp resolution. This new array platform allows for the unbiased mapping of in vivo TF binding sequences (TFBSs) using Chromatin ImmunoPrecipitation followed by microarray experiments (ChIP-chip). The massive dataset generated from these experiments pose great challenges for data analysis.


RESULTS
We developed a fast, scalable and sensitive method to extract TFBSs from ChIP-chip experiments on genome tiling arrays. Our method takes advantage of tiling array data from many experiments to normalize and model the behavior of each individual probe, and identifies TFBSs using a hidden Markov model (HMM). When applied to the data of p53 ChIP-chip experiments from an earlier study, our method discovered many new high confidence p53 targets including all the regions verified by quantitative PCR. Using a de novo motif finding algorithm MDscan, we also recovered the p53 motif from our HMM identified p53 target regions. Furthermore, we found substantial p53 motif enrichment in these regions comparing with both genomic background and the TFBSs identified earlier. Several of the newly identified p53 TFBSs are in the promoter region of known genes or associated with previously characterized p53-responsive genes.


SUPPLEMENTARY INFORMATION
Available at the following URL http://genome.dfci.harvard.edu/~xsliu/HMMTiling/index.html.
7555324
BioProspector: Discovering Conserved DNA Motifs in Upstream Regulatory Regions of Co-Expressed Genes
The development of genome sequencing and DNA microarray analysis of gene expression gives rise to the demand for data-mining tools. BioProspector, a C program using a Gibbs sampling strategy, examines the upstream region of genes in the same gene expression pattern group and looks for regulatory sequence motifs. BioProspector uses zero to third-order Markov background models whose parameters are either given by the user or estimated from a specified sequence file. The significance of each motif found is judged based on a motif score distribution estimated by a Monte Carlo method. In addition, BioProspector modifies the motif model used in the earlier Gibbs samplers to allow for the modeling of gapped motifs and motifs with palindromic patterns. All these modifications greatly improve the performance of the program. Although testing and development are still in progress, the program has shown preliminary success in finding the binding motifs for Saccharomyces cerevisiae RAP1, Bacillus subtilis RNA polymerase, and Escherichia coli CRP. We are currently working on combining BioProspector with a clustering program to explore gene expression networks and regulatory mechanisms.
16397904
A suite of web-based programs to search for transcriptional regulatory motifs
The identification of regulatory motifs is important for the study of gene expression. Here we present a suite of programs that we have developed to search for regulatory sequence motifs: (i) BioProspector, a Gibbs-sampling-based program for predicting regulatory motifs from co-regulated genes in prokaryotes or lower eukaryotes; (ii) CompareProspector, an extension to BioProspector which incorporates comparative genomics features to be used for higher eukaryotes; (iii) MDscan, a program for finding protein-DNA interaction sites from ChIP-on-chip targets. All three programs examine a group of sequences that may share common regulatory motifs and output a list of putative motifs as position-specific probability matrices, the individual sites used to construct the motifs and the location of each site on the input sequences. The web servers and executables can be accessed at http://seqmotifs.stanford.edu.
13969434
Genre based navigation on the Web
We report on our ongoing study of using the genre of Web pages to facilitate information exploration. By genre, we mean socially recognized regularities of form and purpose in documents (e.g., a letter, a memo, a research paper). Our study had three phases. First, through a user study, we identified genres which most/least frequently meet searchers' information needs. We found that certain genres are better suited for certain types of needs. We identified five major groups of document genres that might be used in an interactive search tool that would allow genre based navigation. We tried to balance the following dual objectives: i) each group should be recognizable by a computer algorithm as easily as possible; ii) each group has a better chance of satisfying particular types of information needs. Finally, we developed a novel user interface for Web searching that allows genre based navigation through three major functionalities: 1) limiting search to specified genres, 2) visualizing the hierarchy of genres discovered in the search results and 3) accepting user feedback on the relevancy of the specified genres.
12770105
A tractable probabilistic model for Affymetrix probe-level analysis across multiple chips
MOTIVATION
Affymetrix GeneChip arrays are currently the most widely used microarray technology. Many summarization methods have been developed to provide gene expression levels from Affymetrix probe-level data. Most of the currently popular methods do not provide a measure of uncertainty for the expression level of each gene. The use of probabilistic models can overcome this limitation. A full hierarchical Bayesian approach requires the use of computationally intensive MCMC methods that are impractical for large datasets. An alternative computationally efficient probabilistic model, mgMOS, uses Gamma distributions to model specific and non-specific binding with a latent variable to capture variations in probe affinity. Although promising, the main limitations of this model are that it does not use information from multiple chips and does not account for specific binding to the mismatch (MM) probes.


RESULTS
We extend mgMOS to model the binding affinity of probe-pairs across multiple chips and to capture the effect of specific binding to MM probes. The new model, multi-mgMOS, provides improved accuracy, as demonstrated on some bench-mark datasets and a real time-course dataset, and is much more computationally efficient than a competing hierarchical Bayesian approach that requires MCMC sampling. We demonstrate how the probabilistic model can be used to estimate credibility intervals for expression levels and their log-ratios between conditions.


AVAILABILITY
Both mgMOS and the new model multi-mgMOS have been implemented in an R package, which is available at http://www.bioinf.man.ac.uk/resources/puma.
8835059
Computing the maximum similarity bi-clusters of gene expression data
MOTIVATIONS
Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions.


RESULTS
We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters.


AVAILABILITY
The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html.
848425
Location awareness in unstructured peer-to-peer systems
Peer-to-peer (P2P) computing has emerged as a popular model aiming at further utilizing Internet information and resources. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch between the P2P overlay network and the physical underlying network. The topology mismatch problem brings great stress in the Internet infrastructure. It greatly limits the performance gain from various search or routing techniques. Meanwhile, due to the inefficient overlay topology, the flooding-based search mechanisms cause a large volume of unnecessary traffic. Aiming at alleviating the mismatching problem and reducing the unnecessary traffic, we propose a location-aware topology matching (LTM) technique. LTM builds an efficient overlay by disconnecting slow connections and choosing physically closer nodes as logical neighbors while still retaining the search scope and reducing response time for queries. LTM is scalable and completely distributed in the sense that it does not require any global knowledge of the whole overlay network. The effectiveness of LTM is demonstrated through simulation studies.
17082553
HyPE: hybrid power estimation for IP-based systems-on-chip
In this paper, we present a novel power estimation scheme for programmable systems consisting of predesigned datapath and memory components. The proposed hybrid methodology yields highly accurate estimates within short runtimes by combining high-level simulation with analytical macromodeling of circuit characteristics. The kernel of our methodology is a simulation-free power estimation scheme for memoryless datapaths comprising several IP blocks connected in fixed topologies. The outer shell of our hybrid scheme is a functional simulation, which is performed only on the interfaces between memoryless components and memory blocks. This simulation accurately captures the control signals that affect the flow of data and, consequently, the utilization and power dissipation of hardware. Experimental results validate the accuracy and efficiency of our methodology. We applied our static power estimation kernel to signal processing and data encryption datapaths. For designs of up to 576 IP blocks, the average error of our power estimates is 7.3% in comparison with switch-level simulation results. We implemented our hybrid scheme into a power estimation tool, called HYPE, and used it to explore various architectural alternatives in the design of a 256-state Viterbi decoder and a Rijndael encryptor. For designs with about 1 million transistors, our estimator terminates within seconds. Compared with commercial state-of-the-art gate-level power estimators, our proposed methodology is up to 1000 times faster with 5.4% deviation on average.