2057857
Gene-finding via tandem mass spectrometry
We propose a new gene-finding methodology that combines high performance liquid chromatograph (HPLC)-tandem mass spectrometry experiments with a fast computer algorithm to locate coding regions and introns. Proteins are first extracted from cells and digested by enzymes, and then the resulting peptides are separated and analyzed by HPLC-tandem mass spectrometry. We designed an algorithm to find DNA coding sequences, corresponding to open reading frames (ORF), in the genome such that their translated amino acid sequences are optimally correlated with these tandem mass spectra. In this algorithm, we also allow one gap, corresponding to an intron, between two DNA coding sequences, such that their concatenation becomes one coding sequence. Finally, the algorithm assembles these candidate coding sequences and introns into gene structures. Our algorithm was implemented to predict genes on 4 contigs with a total of 123 kbps using two sets of simulated digestion- HPLC-tandem mass spectrometry data of 2523 Caenorhabditis elegans Chromosome IV proteins, digested by trypsin and Asp-N respectively. Among 15 annotated genes in the forward strand, all 98 exons are hit by the predicted no-gap coding sequences, and 60 out of 83 introns are correctly predicted. We also tested gene structure prediction in a contig containing 3 genes. Combining splicing site predictions with predicted coding sequences and introns, we found all 3 gene structures.
10712046
Algorithms for identifying protein cross-links via tandem mass spectrometry
Cross-linking technology combined with tandem mass spectrometry (MS-MS) is a powerful method that provides a rapid solution to the discovery of protein-protein interactions and protein structures. We studied the problem of detecting cross-linked peptides and cross-linked amino acids from tandem mass spectral data. Our method consists of two steps: the first step finds two protein subsequences whose mass sum equals a given mass measured from the mass spectrometry; and the second step finds the best cross-linked amino acids in these two peptide sequences that are optimally correlated to a given tandem mass spectrum. We designed fast and space-efficient algorithms for these two steps, and implemented and tested them on experimental data of cross-linked Hemoglobin proteins.
31567164
Prediction of protein function using protein-protein interaction data
Assigning functions to novel proteins is one of the most important problems in the post-genomic era. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database, using the protein-protein interaction data from the Munich Information Center for Protein Sequences. We show that our approach outperforms other available methods for function prediction based on protein interaction data.
2822926
Sorting with Fixed-length Reversals
Abstract A popular puzzle TOP-SPIN consists of a permutation of 20 numbered disks on an oval track, with a turnstile capable of reversing a string of 4 consecutive disks. The goal is to sort the disks into identity permutation using reversals. We consider the more general case of sorting n element permutations with a turnstile of size k. The problem of computing the reversal distances is of considerable importance in reconstructing the evolutionary history of the genome. The minimum reversal sequence sorting the one genome to another corresponds to the most likely evolutionary path between them. We give a complete solution for all n and k, of the number of equivalence classes of n-permutations under k-reversal, for both permutations and circular permutations. We also prove the upper and lower bounds on the reversal distances between two permutations.
1201195
Prediction of Protein Function Using Protein-Protein Interaction Data
Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database (YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.
8315237
Local Lazy Regression: Making Use of the Neighborhood to Improve QSAR Predictions
Traditional quantitative structure-activity relationship (QSAR) models aim to capture global structure-activity trends present in a data set. In many situations, there may be groups of molecules which exhibit a specific set of features which relate to their activity or inactivity. Such a group of features can be said to represent a local structure-activity relationship. Traditional QSAR models may not recognize such local relationships. In this work, we investigate the use of local lazy regression (LLR), which obtains a prediction for a query molecule using its local neighborhood, rather than considering the whole data set. This modeling approach is especially useful for very large data sets because no a priori model need be built. We applied the technique to three biological data sets. In the first case, the root-mean-square error (RMSE) for an external prediction set was 0.94 log units versus 0.92 log units for the global model. However, LLR was able to characterize a specific group of anomalous molecules with much better accuracy (0.64 log units versus 0.70 log units for the global model). For the second data set, the LLR technique resulted in a decrease in RMSE from 0.36 log units to 0.31 log units for the external prediction set. In the third case, we obtained an RMSE of 2.01 log units versus 2.16 log units for the global model. In all cases, LLR led to a few observations being poorly predicted compared to the global model. We present an analysis of why this was observed and possible improvements to the local regression approach.
16174340
A new encryption algorithm for image cryptosystems
Abstract There are two major differences of the characteristics of the text data and image data. One difference is that the size of image data is usually much larger than that of text data. The other is that plain data rarely permit loss when a compression technique is used, but image data do. In this paper, we design an efficient cryptosystem for images. Our method is based on vector quantization, which is one of the popular image compression techniques. Our method can achieve the following two goals. One goal is to design a high security image cryptosystem. The other goal is to reduce computational complexity of the encryption and decryption algorithms.
46178429
Task Migration in All-Port Wormhole-Routed 2D Mesh Multicomputer
In a mesh multicomputer, submeshes are scheduled to perform jobs according to some processor allocation schemes, each job assigned to occupy processors of one submesh with an appropriate size. In order to assign the region for the incoming jobs, a task compaction is needed to produce a larger contiguous free region. The overhead of task compaction is primarily relied on designing an efficient task migration scheme. In this paper, we aim at the 2D mesh multicomputers with supporting wormhole routing in all-port communication model as the target machine. First, a certain and a relieved constraints between two submeshes for task migration are proposed. We next propose two task migration schemes following one of the constraints in 2D mesh multicomputers. We also analyze and discuss the routing phases for task migration. Finally, we compare the proposed task migration schemes via performance analysis.
418775
Progressive image watermarking
Progressive transmission of images is very useful in many applications, especially in image transmission over the Internet. To view an image, people would want to see part of the image while the image transmission is in progress, rather than having to wait until the end of the image transmission. On the other hand, the ease of transmission and copying of images creates the need to use digital watermarking to embed the copyright information seamlessly into the media. We propose a progressive image watermarking scheme. In this scheme, the watermark is embedded in such a way that we can retrieve part of it even when the watermarked image is still being transmitted. As transmission progresses, the retrieved watermark has a decreasing bit error rate. Our proposed methods not only transmit the watermarked image progressively, but also intelligently select watermark embedding locations robust to various attacks.
5876317
Framework of performance evaluation for mobile process based on mobile ambient
Formal methodology for distributed and concurrent system, especially computation system with mobility, is increasingly important both in the theory and the practice. Based on the calculus of mobile ambient (MA), a widely studied formal mechanism for mobile computation, this paper focuses on the quantitative analysis of mobile computation system and provides a framework of performance evaluation for it. In details, this paper introduces an enhanced labelled transition system as the system model, and assigns rate to each label through so-called cost function. Based on it, the labelled transition system can be mapped to continuous time Markov chains and thus performance evaluation can be carried out by standard numerical techniques and tools. In some sense the main work of this paper can be regarded as integrating behavior and performance analysis in a compositional formal framework, which provides some basis of unified formal methodology for the development of mobile system.
26835658
On the test allocations for the best lower bound performance of partition testing
A partition testing strategy divides the program's input domain into subdomains according to a partitioning scheme, and selects test cases from each subdomain according to a test allocation scheme. Previous studies have shown that partition testing strategies can be very effective or very ineffective in detecting faults, depending on both the partitioning scheme and the test allocation scheme. Given a partitioning scheme, the maximin criterion chooses a test allocation scheme that will maximally improve the lower bound performance of the partition testing strategy. In an earlier work, we have proved that the Basic Maximin Algorithm can generate such a test allocation scheme. In this paper, we derive further properties of the Basic Maximin Algorithm and present the Complete Maximin Algorithm that generates all possible test allocation schemes that satisfy the maximin criterion.