9069029
Detection of discriminative sequence motifs in proteins obtained from prokaryotes grown at various temperatures
Recent investigations on the stability of proteins have demonstrated various structural factors, but few have considered sequence factors such as protein motifs. These motifs represent highly conserved regions and describe critical regions that may only exist on proteins that remain functional at high temperatures. This investigation presents a method for identifying and comparing corresponding mesophilic and thermophilic sequence motifs between protein families. Discriminative motifs that are conserved only in the mesophilic or thermophilic subfamily are identified. Analysis of the results shows that, although the subfamilies of most protein families share similar motifs, some discriminative motifs are present in particular thermophilic/mesophilic subfamilies. The thermophilic discriminative motifs are conserved only in thermophilic organisms, revealing that physiochemical principles support thermostability.
13750244
A database to aid probe design for virus identification
FENG-MAO LIN, HSIEN-DA HUANG, YU-CHUNG CHANG, PAK-LEONG CHAN, JORNG-TZONG HORNG, MING-TAT KO Department of Computer Science and Information Engineering, National Central University, No.300, Jungda Rd, Jhongli City, Taoyuan, Taiwan 320 ChungLi, 320, Taiwan Department of Biological Science and Technology & Institute of Bioinformatic, National ChiaoTung University, 1001 Ta Hsueh Road, Hsinchu, Taiwan 300 Hsin-Chu, 300, Taiwan Department of Biotechnology, Ming Chuan University, 250 Chung Shan North Road, Section 5, Taipei, 111, Taiwan Institute of Information Science, Academia Sinica, 128 Sec. 2, Academia Rd, Nankangm, Taipei, 115, Taiwan
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/.
5048088
KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites
KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at .
3255925
A data mining method to predict transcriptional regulatory sites based on differentially expressed genes in human genome
Very large-scale gene expression analysis, i.e., UniGene and dbEST, are provided to find those genes with significantly differential expression in specific tissues. The differentially expressed genes in a specific tissue are potentially regulated concurrently by a combination of transcription factors. This study attempts to mine putative binding sites on how combinations of the known regulatory sites homologs and over-represented repetitive elements are distributed in the promoter regions of considered groups of differentially expressed genes. We propose a data mining approach to statistically discover the significantly tissue-specific combinations of known site homologs and over-represented repetitive sequences, which are distributed in the promoter regions of differential gene groups. The association rules mined would facilitate to predict putative regulatory elements and identify genes potentially co-regulated by the putative regulatory elements.
7391844
Database of repetitive elements in complete genomes and data mining using transcription factor binding sites
Approximately 43% of the human genome is occupied by repetitive elements. Even more, around 51% of the rice genome is occupied by repetitive elements. The analysis presented here indicates that repetitive elements in complete genomes may have been very important in the evolutionary genomics. In this study, a database, called the Repeat Sequence Database, is first designed and implemented to store complete and comprehensive repetitive sequences. See http://rsdb.csie.ncu.edu.tw for more information. The database contains direct, inverted and palindromic repetitive sequences, and each repetitive sequence has a variable length ranging from seven to many hundred nucleotides. The repetitive sequences in the database are explored using a mathematical algorithm to mine rules on how combinations of individual binding sites are distributed among repetitive sequences in the database. Combinations of transcription factor binding sites in the repetitive sequences are obtained and then data mining techniques are applied to mine association rules from these combinations. The discovered associations are further pruned to remove insignificant associations and obtain a set of associations. The mined association rules facilitate efforts to identify gene classes regulated by similar mechanisms and accurately predict regulatory elements. Experiments are performed on several genomes including C. elegans, human chromosome 22, and yeast.
13985475
A clustering-based approach for prediction of cardiac resynchronization therapy
This paper presents a method for predicting pacing sites in the left ventricle of a heart and its result can be used to assist device programming in cardiac resynchronization therapy (CRT), which is a widely adopted therapy for heart failure patients. In a traditional CRT device deployment, pacing sites are selected without quantitative prediction. That runs the risk of suboptimal benefits. In this work, a surface tracking method is proposed to describe the ventricular wall motion and a hierarchical agglomerative clustering technique is applied to radial motion series to find candidate pacing sites. Using clinical MRI data in our experiments, we show that the proposed method performs as well as we expect. Our approach can not only effectively identify suitable pacing sites, but also distinguish patients from normals perfectly to help medical diagnosis.
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
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
18616381
The Cost of Transparency: Grid-Based File Access on the Avaki Data Grid
Grid computing has been a hot topic for a decade. Several systems have been developed. Despite almost a decade of research and tens of millions of dollars spent, uptake of grid technology has been slow. Most deployed grids are based on a toolkit approach that requires significant software modification or development. An operating system technique used for over 30 years has been to reduce application complexity by providing transparency, e.g. file systems mask details of devices, virtual machines mask finite memory, etc. It has been argued that providing transparency in a grid environment is too costly in terms of performance. This paper examines that question in the context of data grids by measuring the performance of a commercially available data grid product – the Avaki Data Grid (ADG). We present the architecture of the ADG, describe our experimental setup, and provide performance results, comparing the ADG to a native NFS V3 implementation for both local and wide area access cases. The results were mixed, though encouraging. For single client local file operations, native NFS outperformed the ADG by 15% to 45% for smaller files, though for files larger than 32 MB ADG outperformed native NFS.
10305231
The Protein Information Resource: an integrated public resource of functional annotation of proteins
The Protein Information Resource (PIR) serves as an integrated public resource of functional annotation of protein data to support genomic/proteomic research and scientific discovery. The PIR, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the PIR-International Protein Sequence Database (PSD), the major annotated protein sequence database in the public domain, containing about 250 000 proteins. To improve protein annotation and the coverage of experimentally validated data, a bibliography submission system is developed for scientists to submit, categorize and retrieve literature information. Comprehensive protein information is available from iProClass, which includes family classification at the superfamily, domain and motif levels, structural and functional features of proteins, as well as cross-references to over 40 biological databases. To provide timely and comprehensive protein data with source attribution, we have introduced a non-redundant reference protein database, PIR-NREF. The database consists of about 800 000 proteins collected from PIR-PSD, SWISS-PROT, TrEMBL, GenPept, RefSeq and PDB, with composite protein names and literature data. To promote database interoperability, we provide XML data distribution and open database schema, and adopt common ontologies. The PIR web site (http://pir.georgetown.edu/) features data mining and sequence analysis tools for information retrieval and functional identification of proteins based on both sequence and annotation information. The PIR databases and other files are also available by FTP (ftp://nbrfa.georgetown.edu/pir_databases).