7472466
A Protein Class Database Organized with ProSite Protein Groups and PIR Superfamilies
A protein class (ProClass) database is developed as a "value-added" "second-generation" database organized according to family relationships. The database collects non-redundant protein sequence entries from SwissProt and PIR databases, and classifies them in families defined collectively by the ProSite protein groups and PIR superfamilies. The major objectives of the database are to maximize family information retrieval, to provide speedy family identification, and to help organizing existing protein sequence databases. The database has two sub-databases: PCFam (ProClass Family) to define protein families and provide links to ProSite patterns and PIR superfamilies, and PCSeq (ProClass Sequence) to describe sequence entries and provide links to PCFam, SwissProt, PIR, and ProSite databases. The current ProClass release has a total of 85,165 sequence entries, about half of which are classified in 3072 ProClass families; it also contains 10,431 newly established SwissProt-PIR links. The database can help reveal domain structures of related families, define new ProSite and PIR families, and provide family assignments for unclassified sequence entries. New ProSite and PIR family members are readily identified via database cross-reference, including 9437 SwissProt entries and 8522 PIR entries. False negative family members missed by both ProSite and PIR are detected using a neural network family identification system. The newly identified superfamily memberships are being incorporated into the current PIR database releases in a collaborative effort with the PIR. The ProClass database is accessible through anonymous FTP and on-line search on the World Wide Web.
205225795
ProClass protein family database
ProClass is a protein family database that organizes non-redundant sequence entries into families defined collectively by PROSITE patterns and PIR superfamilies. 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 more than 120 000 sequence entries, approximately 60% of which is classified into about 3500 families. To maximize family information retrieval, the database provides links to various protein family/domain and structural class databases and contains multiple motif alignments of all PROSITE patterns as well as global alignments of PIR superfamilies. The motif sequences are retrieved from both PIR-International and SWISS-PROT databases, including a large number of new members detected by our GeneFIND family identification system. ProClass can be used to support full-scale genomic annotation, because of its high classification rate. The ProClass database is available for on-line search and record retrieval from our WWW server at http://diana.uthct.edu/proclass.html
21872273
The Protein Information Resource (PIR)
The Protein Information Resource (PIR) produces the largest, most comprehensive, annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Sequence Database (JIPID). The expanded PIR WWW site allows sequence similarity and text searching of the Protein Sequence Database and auxiliary databases. Several new web-based search engines combine searches of sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. New capabilities for searching the PIR sequence databases include annotation-sorted search, domain search, combined global and domain search, and interactive text searches. The PIR-International databases and search tools are accessible on the PIR WWW site at http://pir.georgetown.edu and at the MIPS WWW site at http://www. mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.
6603995
Protein family classification and functional annotation
With the accelerated accumulation of genomic sequence data, there is a pressing need to develop computational methods and advanced bioinformatics infrastructure for reliable and large-scale protein annotation and biological knowledge discovery. The Protein Information Resource (PIR) provides an integrated public resource of protein informatics to support genomic and proteomic research. PIR produces the Protein Sequence Database of functionally annotated protein sequences. The annotation problems are addressed by a classification-driven and rule-based method with evidence attribution, coupled with an integrated knowledge base system being developed. The approach allows sensitive identification, consistent and rich annotation, and systematic detection of annotation errors, as well as distinction of experimentally verified and computationally predicted features. The knowledge base consists of two new databases, sequence analysis tools, and graphical interfaces. PIR-NREF, a non-redundant reference database, provides a timely and comprehensive collection of all protein sequences, totaling more than 1,000,000 entries. iProClass, an integrated database of protein family, function, and structure information, provides extensive value-added features for about 830,000 proteins with rich links to over 50 molecular databases. This paper describes our approach to protein functional annotation with case studies and examines common identification errors. It also illustrates that data integration in PIR supports exploration of protein relationships and may reveal protein functional associations beyond sequence homology.
5414394
Artificial Neural Networks for Molecular Sequence Analysis
Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines. As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data. It has been applied successfully to a variety of problems, ranging from gene identification, to protein structure prediction and sequence classification. This article provides an overview of major neural network paradigms, discusses design issues, and reviews current applications in DNA/RNA and protein sequence analysis.
205231250
The Universal Protein Resource (UniProt)
The ability to store and interconnect all available information on proteins is crucial to modern biological research. Accordingly, the Universal Protein Resource (UniProt) plays an increasingly important role by providing a stable, comprehensive, freely accessible central resource on protein sequences and functional annotation. UniProt is produced by the UniProt Consortium, formed in 2002 by the European Bioinformatics Institute (EBI), the Protein Information Resource (PIR) and the Swiss Institute of Bioinformatics (SIB). The core activities include manual curation of protein sequences assisted by computational analysis, sequence archiving, development of a user-friendly UniProt web site and the provision of additional value-added information through cross-references to other databases. UniProt is comprised of three major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase and the UniProt Reference Clusters. An additional component consisting of metagenomic and environmental sequences has recently been added to UniProt to ensure availability of such sequences in a timely fashion. UniProt is updated and distributed on a bi-weekly basis and can be accessed online for searches or download at .
26993316
Construction of Cryptographically Important Boolean Permutations
Abstract.In this paper a new general methodology is developed to construct Boolean permutations such that any non-trivial linear combination of their components has the largest algebraic degree.
6100465
A distributed connection manager interface for web services on IBM SP systems
In essence, the World Wide Web is a worldwide string of computer databases using a common information retrieval architecture. With the increasing popularity of the World Wide Web, more and more functions have been added to retrieve not only documents written in HTML (Hypertext Markup Language), but also those in other forms through the Common Gateway Interface (CGI), by constructing HTML documents dynamically. Dynamic construction of HTML documents for handling information such as digital libraries is slow and requires much more computer power. A significant performance bottleneck is the initialization and setup phase for a CGI process to gain access to the system containing the data. In this paper we describe the design and implementation of a Connection Manager Interface on IBM SP systems. The Connection Manager provides cliette processes to serve CGI requests and eliminates such bottlenecks. An IBM SP system is used for this emerging area to show that our design and implementation is flexible enough to take advantage of the High-Performance Switch in an IBM SP system. We trace and monitor this scalable Web services using UTE (Unified Trace Environment) tools, and present its performance analysis and visualization.
206622161
The Reverse-Exchange Interconnection Network
Properties of the reverse-exchange interconnection network are used to develop a reconfiguration scheme and a two-pass structure for enhancing the efficiency of a class of multistage interconnection networks. Functional relationships among a class of multistage interconnection networks are first derived. According to the functional relationships, we propose a reconfiguration scheme which enables a network to accomplish various interconnection functions of other networks. Then the admissible permutations along with related recursive control algorithms of the reverse-exchange interconnection network are specified through a set of theorems. Using the reverse-exchange property, we also prove that the algorithms actually work. Finally, we prove that arbitrary permutations can be realized in two passes (or 2 · 1og2N switching steps where N is the network size). By taking advantage of Benes network control algorithms, a way to control the two-pass structure is also developed.
16628287
I-NET mechanism for issuing multiple instructions
Conventional instruction issuing methods use hardware control mechanism to issue instructions in multiple-functional-unit systems. They reach physical limitations due to the complexity of issuing logic when they intend to issue multiple instruction per cycle. A method called I-NET is proposed to overcome this shortcoming. I-NET uses a postcompiler to detect the data dependencies among instructions. The detected data dependence is then attached to the instruction code to form an instruction unit that can be executed independently with other such units. These precoded instruction units are fetched from the memory and sent to functional units directly. A maximal instruction-issuing rate is achieved by avoiding a lengthy decoding procedure in the hardware. The performance study has shown I-NET to be a very promising method for exploring massive parallelism of a program in a multiple-functional-unit system.<<ETX>>
6331913
Speech content integrity verification integrated with ITU G.723.1 speech coding
A speech content integrity verification scheme integrated with ITU G.723.1 speech coding to minimize the total computational cost is proposed in this research. Speech features relevant to the semantic meaning are extracted, encrypted and attached as the header information. This scheme is not only much faster than cryptographic bitstream integrity algorithms, but also more compatible with a variable of applications. The speech signal could go through recompression, amplification, transcoding, re-sampling, D/A and A/D conversion and minor white noise pollution without triggering the verification alarm.