35235754
Gene Family Identification Network Design for Protein Sequence Analysis
With the exponential accumulation of sequence data, continued progress in the Human Genome Project will depend increasingly on advanced computational tools to manage and analyze the data. Utilizing information embedded within families of homologous sequences, a gene family identification approach may facilitate the understanding of gene functions. We have developed a GeneFIND (Gene Family Identification Network Design) system for database searching against gene families. It provides rapid and accurate protein family identification by combining global and motif sequence similarities and incorporating ProClass family information. Multi-level filters are used, starting with the MOTIFIND neural networks and BLAST search, followed by SSEARCH alignment, motif pattern match, hidden Markov modeling of motifs and ClustalW motif alignment. GeneFIND has been implemented as a full-scale system for the classification of more than 1200 ProSite and 6000 PIR families. It has been used to identify thousands of new family members and is well suited for genomic sequence analysis. The system is available for on-line family identification from our WWW server ().
19037242
iProClass: an integrated database of protein family, function and structure information
The iProClass database provides comprehensive, value-added descriptions of proteins and serves as a framework for data integration in a distributed networking environment. The protein information in iProClass includes family relationships as well as structural and functional classifications and features. The current version consists of about 830 000 non-redundant PIR-PSD, SWISS-PROT, and TrEMBL proteins organized with more than 36 000 PIR superfamilies, 145 000 families, 4000 domains, 1300 motifs and 550 000 FASTA similarity clusters. It provides rich links to over 50 database of protein sequences, families, functions and pathways, protein-protein interactions, post-translational modifications, protein expressions, structures and structural classifications, genes and genomes, ontologies, literature and taxonomy. Protein and superfamily summary reports present extensive annotation information and include membership statistics and graphical display of domains and motifs. iProClass employs an open and modular architecture for interoperability and scalability. It is implemented in the Oracle object-relational database system and is updated biweekly. The database is freely accessible from the web site at http://pir.georgetown.edu/iproclass/ and searchable by sequence or text string. The data integration in iProClass supports exploration of protein relationships. Such knowledge is fundamental to the understanding of protein evolution, structure and function and crucial to functional genomic and proteomic research.
3010311
On the spanning w-wide diameter of the star graph
Let u and v be any two distinct nodes of an undirected graph G, which is k -connected. A container C(u, v ) between u and v is a set of internally disjoint paths {P1, P2, . . . , Pw } between u and v where 1 ≤ w ≤ k . The width of C(u, v ) is w and the length of C(u, v ) {written as l [C(u, v )]} is max{l (Pi ) | 1 ≤ i ≤ w }. A w -container C(u, v ) is a container with width w . The w -wide distance between u and v , dw (u, v ), is min{l (C(u, v )) | C(u, v ) is a w -container}. A w -container C(u, v ) of the graph G is a w ∗-container if every node of G is incident with a path in C(u, v ). That means that the w -container C(u, v ) spans the whole graph. Let Sn be the n-dimensional star graph with n ≥ 5. It is known that Sn is bipartite. In this article, we show that, for any pair of distinct nodes u and v in different partite sets of Sn , there exists an (n − 1)∗-container C(u, v ) and the (n − 1)-wide distance d(n−1)(u, v ) is less than or equal to n! n−2 + 1. In addition, we also show the existence of a 2∗-container C(u, v ) and the 2-wide distance d2(u, v ) is bounded above by n! 2 + 1. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(4), 235–249 2006
2900872
Analysis of differentially-regulated genes within a regulatory network by GPS genome navigation
MOTIVATION
A critical challenge of the post-genomic era is to understand how genes are differentially regulated even when they belong to a given network. Because the fundamental mechanism controlling gene expression operates at the level of transcription initiation, computational techniques have been developed that identify cis regulatory features and map such features into expression patterns to classify genes into distinct networks. However, these methods are not focused on distinguishing between differentially regulated genes within a given network. Here we describe an unsupervised machine learning method, termed GPS for gene promoter scan, that discriminates among co-regulated promoters by simultaneously considering both cis-acting regulatory features and gene expression. GPS is particularly useful for knowledge discovery in environments with reduced datasets and high levels of uncertainty.


RESULTS
Application of this method to the enteric bacteria Escherichia coli and Salmonella enterica uncovered novel members, as well as regulatory interactions in the regulon controlled by the PhoP protein that were not discovered using previous approaches. The predictions made by GPS were experimentally validated to establish that the PhoP protein uses multiple mechanisms to control gene transcription, and is a central element in a highly connected network.


AVAILABILITY
The scripts and programs used in this work are accessible from the gps-tools.wustl.edu website. Data and predictions are available by request.
12033624
Computing motif correlations in proteins
Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/.
5664354
Association analysis with one scan of databases
Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves better performance and efficiency than Apriori-like algorithms. However the database still needs to be scanned twice to get the FP-tree. This can be very time-consuming when new data are added to an existing database because two scans may be needed for not only the new data but also the existing data. This paper presents a new data structure P-tree, Pattern Tree, and a new technique, which can get the P-tree through only one scan of the database and can obtain the corresponding FP-tree with a specified support threshold. Updating a P-tree with new data needs one scan of the new data only, and the existing data do not need to be re-scanned.
15014203
A VQ-based image-in-image data hiding scheme
Vector quantization (VQ)-based data hiding and watermarking techniques form a newly developed branch in the digital watermarking research field. We propose a novel watermarking scheme for image-in-image data hiding based on VQ. Unlike existing schemes in the literature for embedding a binary watermark into the original image with VQ, we present an efficient and secure algorithm for embedding a gray-level watermark into the original. The offered scheme is robust to attacks such as VQ compression and JPEG compression. Moreover, it greatly expands the capacity of the watermarking system. Experimental results demonstrate the superiority of our scheme in watermark robustness and watermark capacity. The results also prove the effectiveness of our proposed schemes in VQ-based image-in-image data hiding.