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.
1069327
DWE: Discriminating Word Enumerator
MOTIVATION
Tissue-specific transcription factor binding sites give insight into tissue-specific transcription regulation.


RESULTS
We describe a word-counting-based tool for de novo tissue-specific transcription factor binding site discovery using expression information in addition to sequence information. We incorporate tissue-specific gene expression through gene classification to positive expression and repressed expression. We present a direct statistical approach to find overrepresented transcription factor binding sites in a foreground promoter sequence set against a background promoter sequence set. Our approach naturally extends to synergistic transcription factor binding site search. We find putative transcription factor binding sites that are overrepresented in the proximal promoters of liver-specific genes relative to proximal promoters of liver-independent genes. Our results indicate that binding sites for hepatocyte nuclear factors (especially HNF-1 and HNF-4) and CCAAT/enhancer-binding protein (C/EBPbeta) are the most overrepresented in proximal promoters of liver-specific genes. Our results suggest that HNF-4 has strong synergistic relationships with HNF-1, HNF-4 and HNF-3beta and with C/EBPbeta.


AVAILABILITY
Programs are available for use over the Web at http://rulai.cshl.edu/tools/dwe.
11611563
Sequentiality and prefetching in database systems
Sequentiality of access is an inherent characteristic of many database systems. We use this observation to develop an algorithm which selectively prefetches data blocks ahead of the point of reference. The number of blocks prefetched is chosen by using the empirical run length distribution and conditioning on the observed number of sequential block references immediately preceding reference to the current block. The optimal number of blocks to prefetch is estimated as a function of a number of “costs,” including the cost of accessing a block not resident in the buffer (a miss), the cost of fetching additional data blocks at fault times, and the cost of fetching blocks that are never referenced. We estimate this latter cost, described as memory pollution, in two ways. We consider the treatment (in the replacement algorithm) of prefetched blocks, whether they are treated as referenced or not, and find that it makes very little difference. Trace data taken from an operational IMS database system is analyzed and the results are presented. We show how to determine optimal block sizes. We find that anticipatory fetching of data can lead to significant improvements in system operation.
1725701
Cache performance for multimedia applications
The caching behavior of multimedia applications has been described as having high instruction reference locality within small loops, very large working sets, and poor data cache performance due to non-locality of data references. Despite this, there is no published research deriving or measuring these qualities. Utilizing the previously developed Berkeley Multimedia Workload, we present the results of execution driven cache simulations with the goal of aiding future media processing architecture design. Our analysis examines the differences between multimedia and traditional applications in cache behavior. We find that multimedia applications actually exhibit lower instruction miss ratios and comparable data miss ratios when contrasted with other widely studied workloads. In addition, we find that longer data cache line sizes than are currently used would benefit multimedia processing.
36108373
Machine Characterization Based on an Abstract High-Level Language Machine
Measurements are presented for a large number of machines ranging from small workstations to supercomputers. The authors combine these measurements into groups of parameters which relate to specific aspects of the machine implementation, and use these groups to provide overall machine characterizations. The authors also define the concept of pershapes, which represent the level of performance of a machine for different types of computation. A metric based on pershapes is introduced that provides a quantitative way of measuring how similar two machines are in terms of their performance distributions. The metric is related to the extent to which pairs of machines have varying relative performance levels depending on which benchmark is used. >
18681371
Reducing and Manipulating Complex Trace Data
In performance analysis of computer systems, trace-driven simulation techniques have the important advantage of credibility and accuracy. Unfortunately, traces are usually difficult to obtain, and little work has been done to provide efficient tools to help in the process of reducing and manipulating them. In this paper, we present a tool we have developed to help in the data reduction and data analysis phases of trace-driven simulation studies. Our tool has three main advantages: it accepts a variety of common trace formats; it provides a programmable user interface in which many actions can be directly specified; and it is easy to extend. This tool is particularly helpful for reducing and analyzing complex trace data, such as traces of file system or database activity. In this paper, we present our design principles, our implementation techniques, and a few concrete examples of the use of this tool.
34073205
Genetic Optimization Using A Penalty Function