Bioinformatics: programmed for success.
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Algorithms↗
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Results for “bioinformatic database”
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A bioinformatic approach to the identification of candidate genes for the development of new cancer diagnostics.
A multivariate analysis of the National Cancer Institute gene expression database is reported here. The soft independent modelling of a class analogy approach achieved cell line classification according to histological origin. With the PCA method, based on the expression of 9605 genes and ESTs, classification of colon, leukaemia, renal, melanoma and CNS cells could be performed, but not of lung, breast and ovarian cells. Another multivariate procedure, called partial least squares discriminant analysis (PLS-DA), provides bioinformatic clues for the selection of a limited number of gene transcripts most effective in discriminating different tumoral histotypes. Among them it is possible to identify candidates in the development of new diagnostic tests for cancer detection and unknown genes deserving high priority in further studies. In particular, melan-A, acid phosphatase 5, dopachrome tautomerase, S100-beta and acid ceramidase were found to be among the most important genes for melanoma. The potential of the present bioinformatic approach is exemplified by its ability to identify differentiation and diagnostic markers already in use in clinical settings, such as protein S-100, a prognostic parameter in patients with metastatic melanoma and a screening marker for melanoma metastasis.
Biomarkers, Tumor↗
Improved small volume lung cancer detection with computer-aided detection: database characteristics and imaging of response to breast cancer risk reduction strategies.
Computer-aided detection (CAD) and diagnosis (CADx) of in vivo imaging studies are important tools based on bioinformatics. Currently, there are two diseases for which the United States Food and Drug Administration (FDA) has given premarket approval (PMA): the detection of signs consistent with lung cancer on chest radiographs and breast cancer on mammograms. There are systems for other diseases and other types of images under development; however, this process depends on the availability of an accurate database. The author helped in the development of the databases for such systems and management of the clinical trial that resulted in the FDA-PMA of the system that detects findings consistent with lung cancer. The characteristics of the database used will be described. Further, a woman's risk of developing breast cancer differs from those of other women. Risk can be high, average, or low. There are now pharmaceuticals that decrease the risk that women, as a group, will develop breast cancer and it has been suggested that dietary changes could have similar effects. The pharmaceutical agents, though, have some associated side effects, and it is clinically important to determine whether these agents have decreased an individual woman's risk of breast cancer. In vivo imaging biomarkers of risk and successful risk reduction are therefore sought, but the information on possible in vivo imaging biomarkers is less mature than activities in CAD. Bioinformatics will be an important contributor to this in vivo imaging biomarker development.
Breast Neoplasms↗
ASD: a bioinformatics resource on alternative splicing.
Alternative splicing is an important regulatory mechanism of mammalian gene expression. The alternative splicing database (ASD) consortium is systematically collecting and annotating data on alternative splicing. We present the continuation and upgrade of the ASD [T. A. Thanaraj, S. Stamm, F. Clark, J. J. Riethoven, V. Le Texier, J. Muilu (2004) Nucleic Acids Res. 32, D64-D69] that consists of computationally and manually generated data. Its largest parts are AltSplice, a value-added database of computationally delineated alternative splicing events. Its data include alternatively spliced introns/exons, events, isoform splicing patterns and isoform peptide sequences. AltSplice data are generated by examining gene-transcript alignments. The data are annotated for various biological features including splicing signals, expression states, (SNP)-mediated splicing and cross-species conservation. AEdb forms the manually curated component of ASD. It is a literature-based data set containing sequence and properties of alternatively spliced exons, functional enumeration of observed splicing events, characterization of observed splicing regulatory elements, and a collection of experimentally clarified minigene constructs. ASD includes a workbench, which is an analysis tool that enables users to carry out splicing related analysis such as characterization of introns for various splicing signals, identification of splicing regulatory elements on a given RNA sequence, prediction of putative exons and prediction of putative translation start codons. The different ASD modules are integrated and can be accessed through user-friendly interfaces and visualization tools. ASD data has been integrated with Ensembl genome annotation project as a Distributed Annotation System (DAS) resource and can be viewed on Ensembl genome browser. The ASD resource is presented at (http://www.ebi.ac.uk/asd).
Alternative Splicing↗
Public access for teaching genomics, proteomics, and bioinformatics.
When the human genome project was conceived, its leaders wanted all researchers to have equal access to the data and associated research tools. Their vision of equal access provides an unprecedented teaching opportunity. Teachers and students have free access to the same databases that researchers are using. Furthermore, the recent movement to deliver scientific publications freely has presented a second source of current information for teaching. I have developed a genomics course that incorporates many of the public-domain databases, research tools, and peer-reviewed journals. These online resources provide students with exciting entree into the new fields of genomics, proteomics, and bioinformatics. In this essay, I outline how these fields are especially well suited for inclusion in the undergraduate curriculum. Assessment data indicate that my students were able to utilize online information to achieve the educational goals of the course and that the experience positively influenced their perceptions of how they might contribute to biology.
Computational Biology↗
FeatureExtract--extraction of sequence annotation made easy.
Work on a large number of biological problems benefits tremendously from having an easy way to access the annotation of DNA sequence features, such as intron/exon structure, the contents of promoter regions and the location of other genes in upsteam and downstream regions. For example, taking the placement of introns within a gene into account can help in a phylogenetic analysis of homologous genes. Designing experiments for investigating UTR regions using PCR or DNA microarrays require knowledge of known elements in UTR regions and the positions and strandness of other genes nearby on the chromosome. A wealth of such information is already known and documented in databases such as GenBank and the NCBI Human Genome builds. However, it usually requires significant bioinformatics skills and intimate knowledge of the data format to access this information. Presented here is a highly flexible and easy-to-use tool for extracting feature annotation from GenBank entries. The tool is also useful for extracting datasets corresponding to a particular feature (e.g. promoters). Most importantly, the output data format is highly consistent, easy to handle for the user and easy to parse computationally. The FeatureExtract web server is freely available for both academic and commercial use at http://www.cbs.dtu.dk/services/FeatureExtract/.
Chromosomes↗
The method to compare nucleotide sequences based on the minimum entropy principle.
A new method to compare two (or several) symbol sequences is developed. The method is based on the comparison of the frequencies of the small fragments of the compared sequences; it requires neither string editing, nor other transformations of the compared objects. The comparison is executed through a calculation of the specific entropy of a frequency dictionary against the special dictionary called the hybrid one; this latter is the statistical ancestor of the group of sequences under comparison. Some applications of the developed method in the fields of genetics and bioinformatics are discussed.
Animals↗
From biological databases to platforms for biomedical discovery.
The use of high-throughput DNA sequencing and proteomic methods has led to an unprecedented increase in the amount of genomic and proteomic data. Application of computing technologies and development of computational tools to analyze and present these data has not kept pace with the accumulation of information. Here, we discuss the use of different database systems to store biological information and mention some of the key emerging computing technologies that are likely to have a key role in the future of bioinformatics.
Algorithms↗
Recent developments in biological sequence databases.
Biological sequence databases are currently being re-engineered to make them more efficient and easier to use. This re-engineering is also providing an infrastructure to make it easier to interrogate and integrate data from different sources. The net result of this effort should be a great improvement in the power and availability of bioinformatics resources to the general biology community.
Databases, Factual↗
Computing chemistry on the web.
The development of on-line software tools is changing the way we traditionally perform our analysis in drug design, but will chemoinformatics be forever behind bioinformatics in this development?
Computer Simulation↗
The early days of bioinformatics publishing.
A brief history of the early days of publishing in the bioinformatics field is presented.
Computational Biology↗
A library of efficient bioinformatics algorithms.
In this paper we review some of the existing projects available in the bioinformatics field for facilitating the development of programs, but for which minimising the running time is not of primary importance. We point out the advantages of open source libraries for such tasks and we discuss some of the open source licenses available. Finally, we present the project ALiBio, which is aimed at facilitating the development of efficient programs in bioinformatics.
Algorithms↗
SPiD: a subtilis protein interaction database.
MOTIVATION: Protein-protein interactions are a potential source of valuable clues in determining the functional role of as yet uncharacterized gene products in metabolic pathways. Graph-like structures emerging from the accumulation of interaction data make it difficult to maintain a consistent and global overview by hand. Bioinformatics tools are needed to perform this graph visualization while maintaining a link to the experimental data. RESULTS: "SPiD" is an online database for exploring networks of interacting proteins in Bacillus subtilis characterized by the two-hybrid system. Graphical displays of interaction networks are created dynamically as users interactively navigate through these networks. Third party applications can interface the database through a Common Object Request Broker Architecture (CORBA) tier. AVAILABILITY: SPiD is available through its web site at http://www-mig.versailles.inra.fr/bdsi/SPiD, and through an Interoperable Object Reference (IOR) and its associated Interface Definition Language (IDL). CONTACT: hoebeke@versailles.inra.fr
Bacillus subtilis↗
Penaeus monodon gene discovery project: the generation of an EST collection and establishment of a database.
A large-scale expressed sequence tag (EST) sequencing project was undertaken for the purpose of gene discovery in the black tiger shrimp Penaeus monodon. Initially, 15 cDNA libraries were constructed from different tissues (eyestalk, hepatopancrease, haematopoietic tissue, haemocyte, lymphoid organ, and ovary) of shrimp, reared under normal or stress conditions, to identify tissue-specific genes and genes responding to infection and heat stress. A total of 10,100 clones were analyzed by single-pass sequencing from the 5' end. Clustering and assembling of these ESTs resulted in a total of 4845 unique sequences with 917 overlapping contigs and 3928 singletons. The redundancy of each cDNA library ranged from 13.4% to 61.3% with an overall redundancy of 61.1%. About half of these ESTs (2365 clones, 48.8%) showed significant homology (BLASTX, e-values <10(-4)) to known genes. A high proportion of P. monodon ESTs was most similar to the predicted protein sequences from various organisms, e.g. Homo sapiens (9%), Mus musculus (7%), Drosophila (6%), Gallus sp.(6%), and Anopheles (5%). Only 6% showed the highest similarity to other known genes from shrimp due to the limited sequence entries of the species in the public database. Several tissue-specific transcripts were identified as well as the candidate genes that may be implicated in the immune response. In addition, bioinformatic mining of microsatellites from the P. monodon ESTs identified 997 unique microsatellite containing ESTs in which 74 loci resided within the genes of known functions. Consequently, the P. monodon EST database was established. The EST sequence data and the BLAST results were stored and made available through a web-accessible database (). This EST database provides a useful resource for gene identification and functional genomic studies of shrimp.
Animals↗
Panzea: a database and resource for molecular and functional diversity in the maize genome.
Serving as a community resource, Panzea (http://www.panzea.org) is the bioinformatics arm of the Molecular and Functional Diversity in the Maize Genome project. Maize, a classical model for genetic studies, is an important crop species and also the most diverse crop species known. On average, two randomly chosen maize lines have one single-nucleotide polymorphism every approximately 100 bp; this divergence is roughly equivalent to the differences between humans and chimpanzees. This exceptional genotypic diversity underlies the phenotypic diversity maize needs to be cultivated in a wide range of environments. The Molecular and Functional Diversity in the Maize Genome project aims to understand how selection has shaped molecular diversity in maize and then relate molecular diversity to functional phenotypic variation. The project will screen 4000 loci for the signature of selection and create a wide range of maize and maize-teosinte mapping populations. These populations will be genotyped and phenotyped, permitting high-power and high-resolution dissection of the traits and relating the molecular diversity to functional variation. Panzea provides access to the genotype, phenotype and polymorphism data produced by the project through user-friendly web-based database searches and data retrieval/visualization tools, as well as a wide variety of information and services related to maize diversity.
Chromosome Mapping↗
Combining bioinformatics resources for the structural modelling of eukaryotic metabolic networks.
The architecture of the cellular metabolic network is almost completely available from several databases. This has paved the way for computational analyses. Whereas kinetic modelling is still restrained to small metabolic sub-systems for which enzyme-kinetic details are known, so-called structural modelling techniques can be applied to complete metabolic networks even if the kinetics and regulation of the underlying enzymes is still unknown. Structural modelling requires detailed information on the presence of metabolic enzymes in a specific cell type of interest and the thermodynamics of the reactions, determining their direction under cellular conditions. If compartments are distinguished the sub-cellular compartmentation of reactions and enzymes and the membrane transporters exchanging metabolites between cellular compartments must be included. All this information cannot be taken from a single data base but has to be compiled from various Bioinformatics resources. Here we present an approach towards the organization of Bioinformatics data that enables the flux-balance analysis of comprehensive compartmentalized metabolic networks of eukaryotic cells with special focus on human hepatocytes.
Aspartate Aminotransferases↗
Inhibiting the expression of spindle appendix cooled coil protein 1 can suppress tumor cell growth and metastasis and is associated with cancer immune cells in esophageal squamous cell carcinoma.
Inhibiting the expression of spindle appendix cooled coil protein 1 (SPDL1) can slow down disease progression and is related to poor prognosis in patients with esophageal cancer. However, the specific roles and molecular mechanisms of SPDL1 in esophageal squamous cell carcinoma (ESCC) have not been explored yet. The current study aimed to investigate the expression levels of SPDL1 in ESCC via transcriptome analysis using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Moreover, the biological roles, molecular mechanisms, and protein networks involved in SPDL1 were identified using machine learning and bioinformatics. The cell counting kit-8 assay, EdU staining, and transwell assay were used to investigate the effects of inhibiting SPDL1 expression on ESCC cell proliferation, migration, and invasion. Finally, the correlation between the SPDL1 expression and cancer immune infiltrating cells was evaluated by analyzing data from the TCGA database. Results showed that SPDL1 was overexpressed in the ESCC tissues. The SPDL1 expression was related to age in patients with ESCC. The SPDL1 co-expressed genes included those involved in cell division, cell cycle, DNA repair and replication, cell aging, and other processes. The high-risk scores of SPDL1-related long non-coding RNAs were significantly correlated with overall survival and cancer progression in patients with ESCC (P < 0.05). Inhibiting the SPDL1 expression was effective in suppressing the proliferation, migration, and invasion of ESCC TE-1 cells (P < 0.05). The overexpression of SPDL1 was positively correlated with the levels of Th2 and T-helper cells, and was negatively correlated with the levels of plasmacytoid dendritic cells and mast cells. In conclusion, SPDL1 was overexpressed in ESCC and was associated with immune cells. Further, inhibiting the SPDL1 expression could effectively slow down cancer cell growth and migration. SPDL1 is a promising biomarker for treating patients with ESCC.
Humans↗
ONCOMINE: a cancer microarray database and integrated data-mining platform.
DNA microarray technology has led to an explosion of oncogenomic analyses, generating a wealth of data and uncovering the complex gene expression patterns of cancer. Unfortunately, due to the lack of a unifying bioinformatic resource, the majority of these data sit stagnant and disjointed following publication, massively underutilized by the cancer research community. Here, we present ONCOMINE, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses. To date, ONCOMINE contains 65 gene expression datasets comprising nearly 48 million gene expression measurements form over 4700 microarray experiments. Differential expression analyses comparing most major types of cancer with respective normal tissues as well as a variety of cancer subtypes and clinical-based and pathology-based analyses are available for exploration. Data can be queried and visualized for a selected gene across all analyses or for multiple genes in a selected analysis. Furthermore, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-drug target pairs to facilitate the discovery of novel biomarkers and therapeutic targets.
Databases, Genetic↗