12475257
ProClass Protein Family Database
ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PIR superfamilies and PROSITE patterns. By combining global similarities and functional motifs into a single classification scheme, ProClass helps to reveal domain and family relationships and classify multi-domain proteins. The database currently consists of >155 000 sequence entries retrieved from both PIR-International and SWISS-PROT databases. Approximately 92 000 or 60% of the ProClass entries are classified into approximately 6000 families, including a large number of new members detected by our GeneFIND family identification system. The ProClass motif collection contains approximately 72 000 motif sequences and >1300 multiple alignments for all PROSITE patterns, including >21 000 matches not listed in PROSITE and mostly detected from unique PIR sequences. To maximize family information retrieval, the database provides links to various protein family, domain, alignment and structural class databases. With its high classification rate and comprehensive family relationships, ProClass can be used to support full-scale genomic annotation. The database, now being implemented in an object-relational database management system, is available for online sequence search and record retrieval from our WWW server at http://pir.georgetown.edu/gfserver/proclass.html
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.
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 ().
3641866
The iProClass integrated database for protein functional analysis
Increasingly, scientists have begun to tackle gene functions and other complex regulatory processes by studying organisms at the global scales for various levels of biological organization, ranging from genomes to metabolomes and physiomes. Meanwhile, new bioinformatics methods have been developed for inferring protein function using associative analysis of functional properties to complement the traditional sequence homology-based methods. To fully exploit the value of the high-throughput system biology data and to facilitate protein functional studies requires bioinformatics infrastructures that support both data integration and associative analysis. The iProClass database, designed to serve as a framework for data integration in a distributed networking environment, provides comprehensive descriptions of all proteins, with rich links to over 50 databases of protein family, function, pathway, interaction, modification, structure, genome, ontology, literature, and taxonomy. In particular, the database is organized with PIRSF family classification and maps to other family, function, and structure classification schemes. Coupled with the underlying taxonomic information for complete genomes, the iProClass system (http://pir.georgetown.edu/iproclass/) supports associative studies of protein family, domain, function, and structure. A case study of the phosphoglycerate mutases illustrates a systematic approach for protein family and phylogenetic analysis. Such studies may serve as a basis for further analysis of protein functional evolution, and its relationship to the co-evolution of metabolic pathways, cellular networks, and organisms.
197229
The PIR integrated protein databases and data retrieval system
The Protein Information Resource (PIR) provides many databases and tools to support genomic and proteomic research. PIR is a member of UniProt—Universal Protein Resource—the central repository of protein sequence and function, which maintains UniProt Knowledgebase with extensively curated annotation, UniProt Reference databases to speed sequence searches, and UniProt Archive to reflect sequence history. PIR also provides PIRSF family classification system based on evolutionary relationships of full-length proteins, and iProClass integrated database of protein family, function, and structure. These databases are easily accessible from PIR web site using a centralized data retrieval system for information retrieval and knowledge discovery.
7795322
The Protein Information Resource
The Protein Information Resource (PIR) is an integrated public resource of protein informatics that supports genomic and proteomic research and scientific discovery. PIR maintains the Protein Sequence Database (PSD), an annotated protein database containing over 283 000 sequences covering the entire taxonomic range. Family classification is used for sensitive identification, consistent annotation, and detection of annotation errors. The superfamily curation defines signature domain architecture and categorizes memberships to improve automated classification. To increase the amount of experimental annotation, the PIR has developed a bibliography system for literature searching, mapping, and user submission, and has conducted retrospective attribution of citations for experimental features. PIR also maintains NREF, a non-redundant reference database, and iProClass, an integrated database of protein family, function, and structure information. PIR-NREF provides a timely and comprehensive collection of protein sequences, currently consisting of more than 1 000 000 entries from PIR-PSD, SWISS-PROT, TrEMBL, RefSeq, GenPept, and PDB. The PIR web site (http://pir.georgetown.edu) connects data analysis tools to underlying databases for information retrieval and knowledge discovery, with functionalities for interactive queries, combinations of sequence and text searches, and sorting and visual exploration of search results. The FTP site provides free download for PSD and NREF biweekly releases and auxiliary databases and files.
5642826
A garbage-collecting typed assembly language
Typed assembly languages usually support heap allocation safely, but often rely on an external garbage collector to deallocate objects from the heap and prevent unsafe dangling pointers. Even if the external garbage collector is provably correct, verifying the safety of the interaction between TAL programs and garbage collection is nontrivial. This paper introduces a typed assembly language whose type system is expressive enough to type-check a Cheney-queue copying garbage collector, so that ordinary programs and garbage collection can co-exist and interact inside a single typed language. The only built-in types for memory are linear types describing individual memory words, so that TAL programmers can define their own object layouts, method table layouts, heap layouts, and memory management techniques.
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/.
1404678
SpliceInfo: an information repository for mRNA alternative splicing in human genome
We have developed an information repository named SpliceInfo to collect the occurrences of the four major alternative-splicing (AS) modes in human genome; these include exon skipping, 5′-alternative splicing, 3′-alternative splicing and intron retention. The dataset is derived by comparing the nucleotide and protein sequences available for a given gene for evidence of AS. Additional features such as the tissue specificity of the mRNA, the protein domain contained by exons, the GC-ratio of exons, the repeats contained within the exons, and the Gene Ontology are annotated computationally for each exonic region that is alternatively spliced. Motivated by a previous investigation of AS-related motifs such as exonic splicing enhancer and exonic splicing silencer, this resource also provides a means of identifying motifs candidates and this should help to identify potential regulatory mechanisms within a particular exonic sequence set and its two flanking intronic sequence sets. This is carried out using motif discovery tools to identify motif candidates related to alternative splicing regulation and together with a secondary structure prediction tool, will help in the identification of the structural properties of such regulatory motifs. The integrated resource is now available on http://SpliceInfo.mbc.NCTU.edu.tw/.
7975589
Layered access control schemes on watermarked scalable media
Intellectual property (IP) protection is a critical element in multimedia transmission and delivery systems. Conventional IP protection can be categorized into encryption and watermarking. In this paper, a structure to perform layered access control of scalable media by combining encryption and robust watermarking is proposed and implemented. By taking advantage of the nature of both encryption and watermarking, copyrights of multimedia content can be well protected. In the summated example, we assume a scalable transmission scheme over the broadcasting environment and test the effectiveness of the proposed method. The embedded watermark can be extracted with high confidence. Then, the next temporal-layer keys can be perfectly decrypted and reconstructed. Finally, the media contents are recovered and the copyrights are assured.
12795062
Mutually independent hamiltonian paths in star networks
Two hamiltonian paths P1 = 〈u1, u2, . . . , un(G)〉 and P2 = 〈v1, v2, . . . , vn(G)〉 of G from u to v are independent if u = u1 = v1, v = vn(G) = un(G), and vi = ui for every 1 < i < n(G). A set of hamiltonian paths, {P1, P2, . . . , Pk }, of G from u to v are mutually independent if any two different hamiltonian paths are independent from u to v . A bipartite graph G is hamiltonian laceable if there exists a hamiltonian path joining any two nodes from different partite sets. A bipartite graph is k -mutually independent hamiltonian laceable if there exists k -mutually independent hamiltonian paths between any two nodes from distinct partite sets. The mutually independent hamiltonian laceability of a bipartite graph G, IHPL(G), is the maximum integer k such that G is k -mutually independent hamiltonian laceable. Let Sn denote the n-dimensional star graph. We prove that IHPL(S2) = 1, IHPL(S3) = 0, and IHPL(Sn) = n − 2 if n ≥ 4. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 46(2), 110–117 2005