6353106
The Universal Protein Resource (UniProt): an expanding universe of protein information
The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at or downloaded at .
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.
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.
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
17310261
Cross-cultural influences on women in the IT workforce
A review of 862 papers published in SIGMIS/CPR proceedings over the past 44 years revealed only 29 articles that focused on gender and the IT workforce or gender and IT education, the majority of which were presented at the 2003 conference whose theme was diversity in the IT workforce. Therefore, in response to the call for papers to extend our understanding of topics central to computer personnel research, we present data from our field study of gender and IT that is directed at understanding differing cross-cultural influences on female experiences in the IT workforce. Four themes emerged from analysis of data from four separate studies of women in the IT workforces in Australia, Ireland, New Zealand and the United States: motherhood and careers, career choice, family dynamics and gender stereotypes. Analysis of interviews with 167 women reveals a wide range of influences on women's choice of an IT career. These results lend empirical support to the individual differences theory of gender and IT as an alternative to essentialist or social construction theories.
15818599
Efficient Registration of 3D SPHARM Surfaces
We present SHREC, an efficient algorithm for registration of 3D SPHARM (spherical harmonic) surfaces. SHREC follows the iterative closest point (ICP) registration strategy, and alternately improves the surface correspondence and adjusts the object pose. It establishes the surface correspondence by aligning the underlying SPHARM parameterization. It employs a rotational property of the harmonic expansion to accelerate its step for parameterization rotation. It uses a hierarchical icosahedron approach to sample the rotation space and searches for the best parameterization that matches the template. Our experimental results show that SHREC can not only create more accurate registration than previous methods but also do it efficiently. SHREC is a simple, efficient and general registration method, and has a great potential to be used in many shape modeling and analysis applications.
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/.
12419359
Primer design for multiplex PCR using a genetic algorithm
Multiplex Polymerase Chain Reaction (PCR) experiments are used for amplifying several segments of the target DNA simultaneously and thereby to conserve template DNA, reduce the experimental time, and minimize the experimental expense. The success of the experiment is dependent on primer design. However, this can be a dreary task as there are many constrains such as melting temperatures, primer length, GC content and complementarity that need to be optimized to obtain a good PCR product. Motivated by the lack of primer design tools for multiplex PCR genotypic assay, we propose a multiplex PCR primer design tool using a genetic algorithm, which is a stochastic approach based on the concept of biological evolution, biological genetics and genetic operations on chromosomes, to find an optimal selection of primer pairs for multiplex PCR experiments. The presented experimental results indicate that the proposed algorithm is capable of finding a series of primer pairs that obeies the design properties in the same tube.
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/.