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At least 793 records · Page 44Linked to original sources
PubMed2002

Transcriptome analysis and related databases of Lactococcus lactis.

Several complete genome sequences of Lactococcus lactis and their annotations will become available in the near future, next to the already published genome sequence of L. lactis ssp. lactis IL 1403. This will allow intraspecies comparative genomics studies as well as functional genomics studies aimed at a better understanding of physiological processes and regulatory networks operating in lactococci. This paper describes the initial set-up of a DNA-microarray facility in our group, to enable transcriptome analysis of various Gram-positive bacteria, including a ssp. lactis and a ssp. cremoris strain of Lactococcus lactis. Moreover a global description will be given of the hardware and software requirements for such a set-up, highlighting the crucial integration of relevant bioinformatics tools and methods. This includes the development of MolGenIS, an information system for transcriptome data storage and retrieval, and LactococCye, a metabolic pathway/genome database of Lactococcus lactis.

Databases, Nucleic Acid↗
PubMed2005

Anatomics: the intersection of anatomy and bioinformatics.

Computational resources are now using the tissue names of the major model organisms so that tissue-associated data can be archived in and retrieved from databases on the basis of developing and adult anatomy. For this to be done, the set of tissues in that organism (its anatome) has to be organized in a way that is computer-comprehensible. Indeed, such formalization is a necessary part of what is becoming known as systems biology, in which explanations of high-level biological phenomena are not only sought in terms of lower-level events, but are articulated within a computational framework. Lists of tissue names alone, however, turn out to be inadequate for this formalization because tissue organization is essentially hierarchical and thus cannot easily be put into tables, the natural format of relational databases. The solution now adopted is to organize the anatomy of each organism as a hierarchy of tissue names and linking relationships (e.g. the tibia is PART OF the leg, the tibia IS-A bone) within what are known as ontologies. In these, a unique ID is assigned to each tissue and this can be used within, for example, gene-expression databases to link data to tissue organization, and also used to query other data sources (interoperability), while inferences about the anatomy can be made within the ontology on the basis of the relationships. There are now about 15 such anatomical ontologies, many of which are linked to organism databases; these ontologies are now publicly available at the Open Biological Ontologies website (http://obo.sourceforge.net) from where they can be freely downloaded and viewed using standard tools. This review considers how anatomy is formalized within ontologies, together with the problems that have had to be solved for this to be done. It is suggested that the appropriate term for the analysis, computer formulation and use of the anatome is anatomics.

Adult↗
PubMed2005

Comparative bioinformatic analysis of genes expressed in common bean (Phaseolus vulgaris L.) seedlings.

To rapidly and cost-effectively generate gene expression data, we developed an annotated unigene database of common bean (Phaseolus vulgaris L.). In this study, 3 cDNA libraries were constructed from the bean breeding line SEL1308, 1 from young leaf and 2 from seedlings inoculated or not inoculated with the fungal pathogen Colletotrichum lindemuthianum (Sacc. & Magnus) Briosi & Cavara, which causes anthracnose in common bean. To this date, 5255 single-pass sequences have been included in the database after selection based on sequence quality. These ESTs were trimmed and clustered using the computer programs Phred and CAP3 to form a unigene collection of 3126 unique sequences. Within clusters, 318 single nucleotide polymorphisms (SNPs) and 68 insertions-deletions (indels) were found, indicating the presence of paralogous gene families in our database. Each unigene sequence was analyzed for possible function using their similarity to known genes represented in the GenBank database and classified into 14 categories. Only 314 unigenes showed significant similarities to Phaseolus genomic sequences and P. vulgaris ESTs, which indicates that 90% (2818 unigenes) of our database represent newly discovered common bean genes. In addition, 12% (387 unigenes) were shown to be specific to common bean. This study represents a first step towards the discovery of novel genes in beans and a valuable source of molecular markers for expressed gene tagging and mapping.

Computational Biology↗
PubMed2005

GERMINATE. a generic database for integrating genotypic and phenotypic information for plant genetic resource collections.

The extensive germplasm resource collections that are now available for major crop plants and their wild relatives will increasingly provide valuable biological and bioinformatics resources for plant physiologists and geneticists to dissect the molecular basis of key traits and to develop highly adapted plant material to sustain future breeding programs. A key to the efficient deployment of these resources is the development of information systems that will enable the collection and storage of biological information for these plant lines to be integrated with the molecular information that is now becoming available through the use of high-throughput genomics and post-genomics technologies. The GERMINATE database has been designed to hold a diverse variety of data types, ranging from molecular to phenotypic, and to allow querying between such data for any plant species. Data are stored in GERMINATE in a technology-independent manner, such that new technologies can be accommodated in the database as they emerge, without modification of the underlying schema. Users can access data in GERMINATE databases either via a lightweight Perl-CGI Web interface or by the more complex Genomic Diversity and Phenotype Connection software. GERMINATE is released under the GNU General Public License and is available at http://germinate.scri.sari.ac.uk/germinate/.

Computational Biology↗
PubMed2004

Functional prediction of a T-DNA tagged gene of Arabidopsis thaliana by in silico analysis.

We have employed a gene-knockout approach using T-DNA tagging and in vivo gene fusion in Arabidopsis thaliana for identification and isolation of specific plant genes. Screening of about 3,000 T-DNA tagged lines resulted in identification of a mutant line (no. 197) exhibiting a significant delay in flowering. From this line a 600-bp plant DNA fragment downstream of the left T-DNA junction was cloned by inverse PCR. BLAST searching in the A. thaliana genomic database indicated a putative gene, frf (flowering regulating factor), with unknown function downstream of the T-DNA insert. Bioinformatic tools were used to predict possible protein structure and function. The protein structure predicted by fold recognition indicates that frf is a transcriptional regulator, a ligand-binding receptor responsive to steroids and hormones. Analyzing the predicted results and the phenotype of the T-DNA tagged plant we hypothesized that FRF might be involved in hormone response in A. thaliana. For verification of this hypothesis we exposed the plants of line no. 197 to gibberellic acid (GA3), a potential growth regulator in higher plants. This treatment resulted in an earlier onset of flowering, almost similar to that in wild type control plants.

Amino Acid Sequence↗
PubMed2004

An environment for knowledge discovery in biology.

This paper describes a data mining environment for knowledge discovery in bioinformatics applications. The system has a generic kernel that implements the mining functions to be applied to input primary databases, with a warehouse architecture, of biomedical information. Both supervised and unsupervised classification can be implemented within the kernel and applied to data extracted from the primary database, with the results being suitably stored in a complex object database for knowledge discovery. The kernel also includes a specific high-performance library that allows designing and applying the mining functions in parallel machines. The experimental results obtained by the application of the kernel functions are reported.

Computational Biology↗
PubMed2006

Expressed peptide tags: an additional layer of data for genome annotation.

While genome sequencing is becoming ever more routine, genome annotation remains a challenging process. Identification of the coding sequences within the genomic milieu presents a tremendous challenge, especially for eukaryotes with their complex gene architectures. Here, we present a method to assist the annotation process through the use of proteomic data and bioinformatics. Mass spectra of digested protein preparations of the organism of interest were acquired and searched against a protein database created by a six-frame translation of the genome. The identified peptides were mapped back to the genome, compared to the current annotation, and then categorized as supporting or extending the current genome annotation. We named the classified peptides Expressed Peptide Tags (EPTs). The well-annotated bacterium Rhodopseudomonas palustris was used as a control for the method and showed a high degree of correlation between EPT mapping and the current annotation, with 86% of the EPTs confirming existing gene calls and less than 1% of the EPTs expanding on the current annotation. The eukaryotic plant pathogens Phytophthora ramorum and Phytophthora sojae, whose genomes have been recently sequenced and are much less well-annotated, were also subjected to this method. A series of algorithmic steps were taken to increase the confidence of EPT identification for these organisms, including generation of smaller subdatabases to be searched against, and definition of EPT criteria that accommodates the more complex eukaryotic gene architecture. As expected, the analysis of the Phytophthora species showed less correlation between EPT mapping and their current annotation. While approximately 76% of Phytophthora EPTs supported the current annotation, a portion of them (7.7% and 12.9% for P. ramorum and P. sojae, respectively) suggested modification to current gene calls or identified novel genes that were missed by the current genome annotation of these organisms.

Amino Acid Sequence↗
PubMed2005

Expression profile of two storage-protein gene families in hexaploid wheat revealed by large-scale analysis of expressed sequence tags.

To discern expression patterns of individual storage-protein genes in hexaploid wheat (Triticum aestivum cv Chinese Spring), we analyzed comprehensive expressed sequence tags (ESTs) of common wheat using a bioinformatics technique. The gene families for alpha/beta-gliadins and low molecular-weight glutenin subunit were selected from the EST database. The alignment of these genes enabled us to trace the single nucleotide polymorphism sites among both genes. The combinations of single nucleotide polymorphisms allowed us to assign haplotypes into their homoeologous chromosomes by allele-specific PCR. Phylogenetic analysis of these genes showed that both storage-protein gene families rapidly diverged after differentiation of the three genomes (A, B, and D). Expression patterns of these genes were estimated based on the frequencies of ESTs. The storage-protein genes were expressed only during seed development stages. The alpha/beta-gliadin genes exhibited two distinct expression patterns during the course of seed maturation: early expression and late expression. Although the early expression genes among the alpha/beta-gliadin and low molecular-weight glutenin subunit genes showed similar expression patterns, and both genes from the D genome were preferentially expressed rather than those from the A or B genome, substantial expression of two early expression genes from the A genome was observed. The phylogenetic relationships of the genes and their expression patterns were not correlated. These lines of evidence suggest that expression of the two storage-protein genes is independently regulated, and that the alpha/beta-gliadin genes possess novel regulation systems in addition to the prolamin box.

Base Sequence↗
PubMed2004

Update on genome completion and annotations: Protein Information Resource.

The Protein Information Resource (PIR) recently joined the European Bioinformatics Institute (EBI) and Swiss Institute of Bioinformatics (SIB) to establish UniProt--the Universal Protein Resource--which now unifies the PIR, Swiss-Prot and TrEMBL databases. The PIRSF (SuperFamily) classification system is central to the PIR/UniProt functional annotation of proteins, providing classifications of whole proteins into a network structure to reflect their evolutionary relationships. Data integration and associative studies of protein family, function and structure are supported by the iProClass database, which offers value-added descriptions of all UniProt proteins with highly informative links to more than 50 other databases. The PIR system allows consistent, rich and accurate protein annotation for all investigators.

Animals↗
PubMed2026

Supplementation with effective microorganisms in earthen ponds affects common carp growth and abundance of specific bacterial families.

BACKGROUND: Effective microorganisms are increasingly explored in aquaculture to improve fish health and growth without leaving harmful residues. However, their efficacy in real-world pond environments remains poorly understood. Here, we conducted a 103-day field experiment to assess the effects of supplementation with two effective commercial microorganism products on the microbial communities and growth performance of common carp (Cyprinus carpio). BACKGROUND: Effective microorganisms were added to the feed and directly to the pond water. Microbial diversity was analysed via 16 S rRNA and whole-genome shotgun sequencing across three environments: water (three time points), sediment (two time points), and fish intestine (one time point) from 25 experimental ponds. Bioinformatics processing was performed using the QIIME2 and MG-TK pipelines with taxonomic classification based on the SILVA database. The results showed that although supplemented bacterial families did not establish significantly in pond environments, fish exposed to specific effective microorganism treatments showed improved growth metrics. CONCLUSIONS: These findings suggest that effective microorganisms can increase carp growth in aquaculture without significantly altering the resident microbial communities, suggesting a promising residue-free alternative to traditional additives in aquaculture.

Animals↗
PubMed2003

[In silico data mining of the human programmed cell death 5 (PDCD5) sequences].

OBJECTIVE: To lay foundation for the functional studies of programmed cell death 5 (PDCD5) and develop new technical pathway for bioinformatics analysis of human functional genes. METHODS: Using PDCD5 as the target molecule, intensive bioinformatics analysis of the nucleic acid and protein sequences were conducted. Data mining and comprehensive analysis by sequence against database similarity searching, ortholog structure comparison, expression profile analysis and gene "neighbor" listing were performed. RESULTS: Two human putative pseudogenes on chromosomes 12 and 5, and one mouse putative pseudogene on chromosome 1 were identified. The methanobacterium thermoautotrophicum ortholog was classified as the same fold as ubiquitin and ribosomal protein S13. The C. elegans ortholog, ubiquitin and IAP (inhibitor of apoptosis proteins) belonged to the same expression profile cluster. This cluster was related to biosynthesis and protein synthesis. PDCD5 orthologs in various genomes were adjacent to various ribosomal proteins on the chromosome. CONCLUSION: The human genome contains at least two processed pseudogenes of PDCD5. Besides the relationship with cell apoptosis, PDCD5 is predicted to have functional relationship with ubiquitin and participate in the translation regulation.

Amino Acid Sequence↗
PubMed2002

Large-scale computational identification of HIV T-cell epitopes.

Bioinformatics-driven T-cell epitope-identification methods can enhance vaccine target selection significantly. We evaluated three unrelated computational methods to screen Pol, Gag and Env sequences extracted from the Los Alamos HIV database for HLA-A*0201 and HLA-B*3501 T-cell epitope candidates. The hidden Markov model predicted 389 HLA-B*3501-restricted candidates from 374 HIV-1 and 97 HIV-2 sequences. The artificial neural network (ANN) model, and Bioinformatics and Molecular Analysis Section (BIMAS) quantitative matrix predictions for A*0201 yielded 1122 HIV-1 and 548 HIV-2 candidates. The overall sequence coverage of the predicted A*0201 T-cell epitopes was 2.7% (HIV-1)and 3.0% (HIV-2). HLA-B*3501-predicted epitopes covered 0.9% (HIV-1) and 1.4% (HIV-2) of the total sequence. Comparison of 890 ANN- and 397 BIMAS-derived HIV-1 A*0201- restricted epitope candidates showed that only 13-19% of the predicted and 26% of the experimentally confirmed T-cell epitopes were captured by both methods. Extrapolating these results, we estimated that at least 247 predicted HIV-1 epitopes are yet to be discovered as active A*0201-restricted T-cell epitopes. Adequate comparison and combined usage of various predictive bioinformatics methods, rather than uncritical use of any single prediction method, will enable cost-effective and efficient T-cell epitope screening.

Computational Biology↗
PubMed1996

Information services of the European Bioinformatics Institute.

The scope of the EBI is focused on providing better services to the scientific community. Technological advancements in the hardware area provide EBI with means of producing data much faster than before, and with greater accuracy since there is now a better technical ability to produce more exhaustive searches through larger indices. Hand in hand with the technological developments, research and development work is continuing on better indexing systems and more efficient ways of establishing and maintaining the future databases. The existing links of communication between EBI and the user community are exploited to study the needs of the scientific community, to provide better services, and to enhance the quality of databases by interpreting user feedback and updates. A very important goal is to enhance the awareness of the scientific (and, maybe even more, the nonscientific) public of the importance of the modern field of bioinformatics and to introduce special meetings and courses, in which more specific subjects will be studied in depth. Another aspect of this goal is to help in constructing special bioinformatics programs in university faculties. In such programs, in contrast to the existing layout, students will pursue studies in a combined environment that provides basic training in biology and in computation. Currently, one of the main problems in the field is that scientists are either biologists, who are self-educated in the field of computers and programming, or computer scientists without sufficient knowledge of biology. It is hoped that a combined program will provide a high level of education in both fields of interest at the appropriate ratios. Building an efficient and friendly interface between the EBI and the user community is the basis for any future development. This aim is achieved by using the most modern server systems while continuously researching newer and better systems and interfaces. This task can never be complete without involvement of the user community by providing feedback to any of EBI's services. A better bioinformatics community is a necessity for any future development of the biological research aiming at a better society.

Amino Acid Sequence↗