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PubMed2005

A support vector machine approach to the identification of phosphorylation sites.

We describe a bioinformatics tool that can be used to predict the position of phosphorylation sites in proteins based only on sequence information. The method uses the support vector machine (SVM) statistical learning theory. The statistical models for phosphorylation by various types of kinases are built using a dataset of short (9-amino acid long) sequence fragments. The sequence segments are dissected around post-translationally modified sites of proteins that are on the current release of the Swiss-Prot database, and that were experimentally confirmed to be phosphorylated by any kinase. We represent them as vectors in a multidimensional abstract space of short sequence fragments. The prediction method is as follows. First, a given query protein sequence is dissected into overlapping short segments. All the fragments are then projected into the multidimensional space of sequence fragments via a collection of different representations. Those points are classified with pre-built statistical models (the SVM method with linear, polynomial and radial kernel functions) either as phosphorylated or inactive ones. The resulting list of plausible sites for phosphorylation by various types of kinases in the query protein is returned to the user. The efficiency of the method for each type of phosphorylation is estimated using leave-one-out tests and presented here. The sensitivities of the models can reach over 70%, depending on the type of kinase. The additional information from profile representations of short sequence fragments helps in gaining a higher degree of accuracy in some phosphorylation types. The further development of an automatic phosphorylation site annotation predictor based on our algorithm should yield a significant improvement when using statistical algorithms in order to quantify the results.

Algorithms↗
PubMed2004

Identification of Basonuclin2, a DNA-binding zinc-finger protein expressed in germ tissues and skin keratinocytes.

We used a bioinformatics approach to identify Basonuclin2, the second member of the Basonuclin zinc-finger family of transcription factors. The mouse Basonuclin2 protein consists of 1049 amino acids and contains three pairs of zinc fingers in the C-terminus that show a high level of amino acid sequence similarity with Basonuclin1. In addition, other characteristic domains of Basonuclin1, such as the serine strip and a nuclear localization signal, are also present in Basonuclin2. We used genomic and in silico database analysis to identify the human and rat homologs of basonuclin2. A search of the mouse genome showed that the basonuclin2 gene maps to chromosome 4 and consists of six exons spanning approximately 300 kb. Northern blot analysis revealed multiple transcripts of basonuclin2 in tissues of the reproductive system (ovary and testis) and also in kidney and skin. We demonstrate that, as expected from sequence conservation, recombinant Basonuclin2 can bind to a sequence in the promoter of a rRNA gene previously characterized as a Basonuclin-binding site. Full-length Basonuclin2 exclusively localizes to the nucleus, indicating that it likely plays an important role in nuclear function, probably in gene regulation. Our study establishes Basonuclin2 as a novel member of the Basonuclin family. Moreover, the structural and functional similarities with Basonuclin1 suggest that Basonuclin2 may play an analogous function in germ cells and skin keratinocytes.

Amino Acid Sequence↗
PubMed2006

PATRIC: the VBI PathoSystems Resource Integration Center.

The PathoSystems Resource Integration Center (PATRIC) is one of eight Bioinformatics Resource Centers (BRCs) funded by the National Institute of Allergy and Infection Diseases (NIAID) to create a data and analysis resource for selected NIAID priority pathogens, specifically proteobacteria of the genera Brucella, Rickettsia and Coxiella, and corona-, calici- and lyssaviruses and viruses associated with hepatitis A and E. The goal of the project is to provide a comprehensive bioinformatics resource for these pathogens, including consistently annotated genome, proteome and metabolic pathway data to facilitate research into counter-measures, including drugs, vaccines and diagnostics. The project's curation strategy has three prongs: 'breadth first' beginning with whole-genome and proteome curation using standardized protocols, a 'targeted' approach addressing the specific needs of researchers and an integrative strategy to leverage high-throughput experimental data (e.g. microarrays, proteomics) and literature. The PATRIC infrastructure consists of a relational database, analytical pipelines and a website which supports browsing, querying, data visualization and the ability to download raw and curated data in standard formats. At present, the site warehouses complete sequences for 17 bacterial and 332 viral genomes. The PATRIC website (https://patric.vbi.vt.edu) will continually grow with the addition of data, analysis and functionality over the course of the project.

Bioterrorism↗
PubMed2006

VANTED: a system for advanced data analysis and visualization in the context of biological networks.

BACKGROUND: Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes. RESULTS: We present VANTED, a tool for the visualization and analysis of networks with related experimental data. Data from large-scale biochemical experiments is uploaded into the software via a Microsoft Excel-based form. Then it can be mapped on a network that is either drawn with the tool itself, downloaded from the KEGG Pathway database, or imported using standard network exchange formats. Transcript, enzyme, and metabolite data can be presented in the context of their underlying networks, e. g. metabolic pathways or classification hierarchies. Visualization and navigation methods support the visual exploration of the data-enriched networks. Statistical methods allow analysis and comparison of multiple data sets such as different developmental stages or genetically different lines. Correlation networks can be automatically generated from the data and substances can be clustered according to similar behavior over time. As examples, metabolite profiling and enzyme activity data sets have been visualized in different metabolic maps, correlation networks have been generated and similar time patterns detected. Some relationships between different metabolites were discovered which are in close accordance with the literature. CONCLUSION: VANTED greatly helps researchers in the analysis and interpretation of biochemical data, and thus is a useful tool for modern biological research. VANTED as a Java Web Start Application including a user guide and example data sets is available free of charge at http://vanted.ipk-gatersleben.de.

Algorithms↗
PubMed2005

SPS' Digest: the Swiss Proteomics Society selection of proteomics articles.

Despite the consolidation of the specialized proteomics literature around a few established journals, such as Proteomics, Molecular and Cellular Proteomics, and the Journal of Proteome Research, a lot of information is still spread in many different publications from different fields, such as analytical sciences, MS, bioinformatics, etc. The purpose of SPS' Digest is to gather a selection of proteomics articles, to categorize them, and to make the list available on a periodic basis through a web page and email alerts.

Animals↗
PubMed1998

Findings emerging from complete microbial genome sequences.

Sixteen microorganisms, including one eukaryote, four archaeons, and 11 eubacteria, have been completely sequenced and published. More than 50 genomes are scheduled to be completed by the year 2000. This explosive growth of information is forcing change in many scientific disciplines (e.g. bioinformatics and molecular genetics), spawning new fields, and even changing the way scientific information is used and shared. Novel, global genome sequence comparisons seem slow to appear but the infrastructure for these projects is being built, and we expect exciting developments in the near future.

Databases, Factual↗
PubMed1999

Microbial genomes.

Microbial genome sequencing is driven by the need to understand and control pathogens and to exploit extremophiles and their enzymes in bioremediation and industry. It is hard for the traditional bacteriologist to grasp the scale and pace of the venture. Around two dozen microbial genomes have now been completed and, within a decade, genomes from every significant species of bacterial pathogen of humans, animals and plants will have been sequenced. Indeed, we will often have more than one sequence from a species or genus--for example, we already have sequences from two strains of Helicobacter pylori, from two strains of Mycobacterium tuberculosis and from three species of Pyrococcus. However, genome sequencing risks becoming expensive molecular stamp-collecting without the tools to mine the data and fuel hypothesis-driven laboratory-based research. Bioinformatics, twinned with the new experimental approaches forming functional genomics', provides some of the needed tools. Nonetheless, there will be an increasing need for us to explore the detailed implications of genomic findings. Microbial genome sequencing thus represents not a threat, but an exciting opportunity for molecular microbiologists.

Computational Biology↗
PubMed2005

Farm animal genomics and informatics: an update.

Farm animal genomics is of interest to a wide audience of researchers because of the utility derived from understanding how genomics and proteomics function in various organisms. Applications such as xenotransplantation, increased livestock productivity, bioengineering new materials, products and even fabrics are several reasons for thriving farm animal genome activity. Currently mined in rapidly growing data warehouses, completed genomes of chicken, fish and cows are available but are largely stored in decentralized data repositories. In this paper, we provide an informatics primer on farm animal bioinformatics and genome project resources which drive attention to the most recent advances in the field. We hope to provide individuals in biotechnology and in the farming industry with information on resources and updates concerning farm animal genome projects.

Animals↗
PubMed1998

A strategy and demonstration for integrated biotechnology information.

Bioinformatics has developed as a key discipline to support science. Integrated access to the various new and established information resources is a key requirement for their future utility. A strategy for this integration has been developed and is being demonstrated to a core group of European users.

Biotechnology↗
PubMed2005

Chemistry in bioinformatics.

Chemical information is now seen as critical for most areas of life sciences. But unlike Bioinformatics, where data is openly available and freely re-usable, most chemical information is closed and cannot be re-distributed without permission. This has led to a failure to adopt modern informatics and software techniques and therefore paucity of chemistry in bioinformatics. New technology, however, offers the hope of making chemical data (compounds and properties) free during the authoring process. We argue that the technology is already available; we require a collective agreement to enhance publication protocols.

Access to Information↗
PubMed2005

Effect of training datasets on support vector machine prediction of protein-protein interactions.

Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived from the use of shuffled sequences. The real protein sequences of hypothetical noninteracting proteins are generated from an exclusion analysis in combination with subcellular localization information of interacting proteins found in the Database of Interacting Proteins. Prediction accuracy using real protein sequences is 76.9% compared to 94.1% using artificial shuffled sequences. The discrepancy likely arises from the expected higher level of difficulty for separating two sets of real protein sequences than that for separating a set of real protein sequences from a set of artificial sequences. The use of real protein sequences for training a SVM classification system is expected to give better prediction results in practical cases. This is tested by using both SVM systems for predicting putative protein partners of a set of thioredoxin related proteins. The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein-protein interaction prediction.

Algorithms↗
PubMed2001

Bioinformatics and the malaria genome: facilitating access and exploitation of sequence information.

The torrent of sequence information unleashed by the various genome sequencing projects, including that of Plasmodium falciparum, will lead to an unprecedented increase in the data available for research purposes. The scientific community is struggling to develop ways to assimilate this information and ensure that it is fully analysed in a way that enables rapid development of new therapeutic and diagnostic advances. This is particularly so for the field of tropical medicine where many of the scientists have had limited training in the area of Bioinformatics and may be further hampered by poor access to the sequence data. A number of collections of malaria genome sequence are available, each with their own advantages and disadvantages, however further improvements in these information resources are needed. In particular, there would be great benefit in integrating genomic sequence and functional genomics results with the large amount of pre-existing knowledge related to parasite biology and immunological interactions with the host. Attempts to achieve this include the PlasmoDB database, and the lessons learned in this effort could be of great utility to other organism-specific databases.

Animals↗
PubMed2001

Analysis of the Arabidopsis mitochondrial proteome.

The complete set of nuclear genes that encode proteins targeted to mitochondria in plants is currently undefined and thus the full range of mitochondrial functions in plants is unknown. Analysis of two-dimensional gel separations of Arabidopsis cell culture mitochondrial protein revealed approximately 100 abundant proteins and 250 low-abundance proteins. Comparison of subfractions of mitochondrial protein on two-dimensional gels provided information on the soluble, membrane, or integral membrane locations of this protein set. A total of 170 protein spots were excised, trypsin-digested, and matrix-assisted laser desorption ionization/time of flight mass spectrometry spectra obtained. Using this dataset, 91 of the proteins were identified by searching translated Arabidopsis genomic databases. Of this set, 81 have defined functions based on sequence comparison. These functions include respiratory electron transport, tricarboxylic acid cycle metabolism, amino acid metabolism, protein import, processing, and assembly, transcription, membrane transport, and antioxidant defense. A total of 10 spectra were matched to Arabidopsis putative open reading frames for which no specific function has been determined. A total of 64 spectra did not match to an identified open reading frame. Analysis of full-length putative protein sequences using bioinformatic tools to predict subcellular targeting (TargetP, Psort, and MitoProt) revealed significant variation in predictions, and also a lack of mitochondrial targeting prediction for several characterized mitochondrial proteins.

Arabidopsis↗
PubMed2026

Integrated bioinformatics analyses for GSDMB in carcinogenesis and progression of bladder cancer.

BACKGROUND: Emerging evidence suggests that pyroptosis influences the development of various diseases. Gasdermin B (GSDMB), an intracellular protein that executes pyroptosis, has recently attracted attention for its potential role in tumor biology. However, its specific function in bladder cancer (BLCA) remains unclear. Therefore, this study aimed to investigate the potential role of GSDMB in the carcinogenesis and prognosis of BLCA patients. METHODS: Mendelian randomization (MR) studies were conducted to examine relationships between the expression of GSDMB and BLCA with expression quantitative trait loci (eQTL) data. Then, GSDMB mRNA expression data and clinical characteristics of BLCA patients were retrieved from The Cancer Genome Atlas (TCGA) database. Cox regression was used to explore the relationship between GSDMB mRNA expression and patients' survival. Additionally, the correlation between GSDMB and the immune microenvironment, tumor mutational burden (TMB), tumor microenvironment (TME), and drug sensitivity in BLCA was examined. RESULTS: According to MR analysis based on eQTLs, GSDMB mRNA expression has positive causal effects on bladder carcinogenesis and the need for bladder surgery (P<0.05). The analyses of TCGA demonstrated an increased expression of GSDMB in BLCA tissues, correlating with improved patient survival. Additionally, elevated GSDMB mRNA expression was identified as an independent protective prognostic factor for BLCA, and it was associated with immune cell infiltration, TMB, TME score, and drug sensitivity. CONCLUSIONS: Elevated mRNA expression of GSDMB has a causal link to a higher risk of BLCA and the likelihood of bladder surgery, but also indicates a better prognosis. Thus, GSDMB exhibits dual effects and might serve as a potential biomarker for predicting onset and progression of BLCA. Nevertheless, further investigation of pathogenesis and mechanisms underlying GSDMB is warranted.

Bladder cancer (BLCA)↗
PubMed2006

Adaptation of proteins to different environments: a comparison of proteome structural properties in Bacillus subtilis and Escherichia coli.

We compared amino acid solvent accessibilities and helix propensities in data sets of Escherichia coli and Bacillus subtilis proteins. These species reside in very different environments and hold very different physiological properties. From the observations, it was proposed that the cytoplasm of B. subtilis is more ion-rich compared to the cytoplasm of E. coli, which might be more hydrophobic; therefore, during evolution these differences have resulted in different protein folding tracks. Such inherent differences imply that the results of bioinformatic analyses of protein structures might depend on the species from which the proteins are picked. It is also suggested that different cytoplasmic environments cause E. coli and B. subtilis to be appropriate for expression of distinct types of proteins.

Amino Acids↗
PubMed2004

ExQuest, a novel method for displaying quantitative gene expression from ESTs.

There is a pressing need for interactive bioinformatics tools that empower investigators with the means to extract information and organize it in a simplified but meaningful format. A wealth of mammalian gene expression data is readily accessible, much of which is based on expressed sequence tags (ESTs). Many mammalian ESTs are derived from tissue-specific cDNA libraries in which the number of ESTs representing a specific gene approximates the transcriptional expression level in the source tissue. Our program ExQuest (Expressional Quantification of ESTs) organizes the public EST database (dbEST) into hierarchical tissue classes and reports tissue or developmental gene expression patterns for both mRNA and genomic sequences. ExQuest also displays tissue expression patterns of genes in the context of assembled chromosomes. These interactive "transcriptome" maps provide a novel tool for investigating the genomic basis of gene expression as well as prioritizing candidate genes within genetically mapped mutant and quantitative trait loci.

Animals↗
PubMed2000

Bioinformatics tools for whole genomes.

The advent of whole-genome data resources--not only sequence but also other genome-scale data collections such as gene expression, protein interaction, and genetic variation--is having two marked, complementary effects on the relatively new discipline of bioinformatics. First, the veritable flood of data is creating a need and demand for new tools for dealing adequately with the deluge, and, second, the unprecedented extent, diversity, and impending completeness of the data sets are creating opportunities for new approaches to discovery based on computational methods.

Algorithms↗
PubMed2006

A fast parallel algorithm for finding the longest common sequence of multiple biosequences.

BACKGROUND: Searching for the longest common sequence (LCS) of multiple biosequences is one of the most fundamental tasks in bioinformatics. In this paper, we present a parallel algorithm named FAST_LCS to speedup the computation for finding LCS. RESULTS: A fast parallel algorithm for LCS is presented. The algorithm first constructs a novel successor table to obtain all the identical pairs and their levels. It then obtains the LCS by tracing back from the identical character pairs at the last level. Effective pruning techniques are developed to significantly reduce the computational complexity. Experimental results on gene sequences in the tigr database show that our algorithm is optimal and much more efficient than other leading LCS algorithms. CONCLUSION: We have developed one of the fastest parallel LCS algorithms on an MPP parallel computing model. For two sequences X and Y with lengths n and m, respectively, the memory required is max{4*(n+1)+4*(m+1), L}, where L is the number of identical character pairs. The time complexity is O(L) for sequential execution, and O(|LCS(X, Y)|) for parallel execution, where |LCS(X, Y)| is the length of the LCS of X and Y. For n sequences X1, X2, ..., Xn, the time complexity is O(L) for sequential execution, and O(|LCS(X1, X2, ..., Xn)|) for parallel execution. Experimental results support our analysis by showing significant improvement of the proposed method over other leading LCS algorithms.

Algorithms↗