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PubMed2004

BOD: a customizable bioinformatics on demand system accommodating multiple steps and parallel tasks.

The integration of bioinformatics resources worldwide is one of the major concerns of the biological community. We herein established the BOD (Bioinformatics on demand) system to use Grid computing technology to set up a virtual workbench via a web-based platform, to assist researchers performing customized comprehensive bioinformatics work. Users will be able to submit entire search queries and computation requests, e.g. from DNA assembly to gene prediction and finally protein folding, from their own office using the BOD end-user web interface. The BOD web portal parses the user's job requests into steps, each of which may contain multiple tasks in parallel. The BOD task scheduler takes an entire task, or splits it into multiple subtasks, and dispatches the task or subtasks proportionally to computation node(s) associated with the BOD portal server. A node may further split and distribute an assigned task to its sub-nodes using a similar strategy. In the end, the BOD portal server receives and collates all results and returns them to the user. BOD uses a pipeline model to describe the user's submitted data and stores the job requests/status/results in a relational database. In addition, an XML criterion is established to capture task computation program details.

Computational Biology↗
PubMedDate not supplied

Prediction of enzyme family classes.

Classes of newly found enzyme sequences are usually determined either by biochemical analysis of eukaryotic and prokaryotic genomes or by microarray chips. These experimental methods are both time-consuming and costly. With the explosion of protein sequences entering into databanks, it is highly desirable to explore the feasibility of selectively classifying newly found enzyme sequences into their respective enzyme classes by means of an automated method. This is indeed important because knowing which family or subfamily an enzyme belongs to may help deduce its catalytic mechanism and specificity, giving clues to the relevant biological function. In this study, a bioinformatical analysis was conducted for 2640 oxidoreductases classified into 16 subclasses according to the different types of substrates they act on during the catalytic process. Although it is an extremely complicated problem and might involve the knowledge of 3-dimensional structure as well as many other physical chemistry factors, some quite promising results have been obtained indicating that the family or subfamily of an enzyme is predictable to a considerable degree by means of sequence-based approach alone if a good training dataset can be established.

Amino Acid Sequence↗
PubMed2006

The Life Sciences Semantic Web is full of creeps!

The Semantic Web for the Life Sciences (SWLS), when realized, will dramatically improve our ability to conduct bioinformatics analyses using the vast and growing stores of web-accessible resources. This ability will be achieved through the widespread acceptance and application of standards for naming, representing, describing and accessing biological information. The W3C-led Semantic Web initiative has established most, if not all, of the standards and technologies needed to achieve a unified, global SWLS. Unfortunately, the bioinformatics community has, thus far, appeared reluctant to fully adopt them. Rather, we are seeing what could be described as 'semantic creep'-timid, piecemeal and ad hoc adoption of parts of standards by groups that should be stridently taking a leadership role for the community. We suggest that, at this point, the primary hindrances to the creation of the SWLS may be social rather than technological in nature, and that, like the original Web, the establishment of the SWLS will depend primarily on the will and participation of its consumers.

Computational Biology↗
PubMed2003

[Cloning, sequencing and bioinformatics analysis of a new tumor suppressor gene ndr2 from mouse].

BACKGROUND & OBJECTIVE: Ndr2 (N-myc down stream regulator) gene in human is a new gene cloned with the human adult whole brain cDNA as template in 1999, which accession number is AF159092 in GenBank. Locating backward position of the N-myc gene in human chromosome, this gene was named Ndr2 gene. The previous experimental results showed Ndr2 gene probably is a tumor suppressor gene. To research the function of Ndr2 gene, the authors cloned the genomic sequence of ndr2 from mouse. METHODS: To clone Ndr2 genomic sequence by reverse transcription-polymerase chain reaction(RT-PCR) with the mouse genome library as template; automatic sequencing was performed using 310 Genetic Analyzer; homogeneous analysis was made using GenBank BLAST; open reading fragment(ORF) analysis was made using PC Gene and ORF Finder; domain analysis was made using ProDom system. RESULTS: A fragment (about 3310bp,identified by agarose gel electrophoresis) was obtained using RT-PCR with the mouse genome library as template. The fragment was cloned in pMD18-T vector. BLAST analysis showed that the sequence was highly homogeneous (with the homogeneity rate of 91.4%) with Ndr2 gene in human and non-homogeneous with genomic sequence database in mouse. ORF analysis showed that there was a complete coding region in it, which including 8 extrons and 7 introns; it can interpret a protein containing about 200 amino acid residuals. ProDom analysis showed there was a domain like acyl carrier protein(ACP) in it. CONCLUSION: The authors cloned Ndr2 gene in mouse and proved that the sequence is a new genome sequence in mouse genomic sequence database. At present, the genome sequence has been submitted to GenBank(the accession number: AY151387).

Adaptor Proteins, Signal Transducing↗
PubMed2006

MicroRNA sequence motifs reveal asymmetry between the stem arms.

The processing of micro RNAs (miRNAs) from their stemloop precursor have revealed asymmetry in the processing of the mature and its star sequence. Furthermore, the miRNA processing system between organism differ. To assess this at the sequence level we have investigated mature miRNAs in their genomic contexts. We have compared profiles of mature miRNAs within their genomic context of the 5' and 3' stemloop precursor arms and we find asymmetry between mature sequences of the 5' and 3' stemloop precursor arms. The main observation is that vertebrate organisms have a characteristic motif on the 5' arm which is in contrast to the 3' arm motif which mainly show the conserved U at the position of the mature start. Also the vertebrate 5' arm motif show a semi-conserved G 13 nucleotides upstream from the first position. We compared the 5' and 3' arm profiles using the average log likelihood ratio (ALLR) score, as defined by Wang and Stormo (2003) [Wang T., Stormo, G.D., 2003. Combining phylogenetic data with co-regulated genes to identify regulatory motifs. Bioinformatics 2369-2380.] and computing a p-value we find that the two profiles differs significantly in their 3' end where the 5' arm motif (in contrast to the 3' arm motif) has a semi-conserved GU rich region. Similar findings are also obtained for other organisms, such as fly, worm and plants. The observed similarities and differences between closely and distantly related organisms are discussed and related to current knowledge of miRNA processing.

Animals↗
PubMed2003

Molecular identification of high and low affinity receptors for nicotinic acid.

Nicotinic acid has been used clinically for over 40 years in the treatment of dyslipidemia producing a desirable normalization of a range of cardiovascular risk factors, including a marked elevation of high density lipoprotein and a reduction in mortality. The precise mechanism of action of nicotinic acid is unknown, although it is believed that activation of a G(i)-G protein-coupled receptor may contribute. Utilizing available information on the tissue distribution of nicotinic acid receptors, we identified candidate orphan receptors. The selected orphan receptors were screened for responses to nicotinic acid, in an assay for activation of G(i)-G proteins. Here we describe the identification of the G protein-coupled receptor HM74 as a low affinity receptor for nicotinic acid. We then describe the subsequent identification of HM74A in follow-up bioinformatics searches and demonstrate that it acts as a high affinity receptor for nicotinic acid and other compounds with related pharmacology. The discovery of HM74A as a molecular target for nicotinic acid may facilitate the discovery of superior drug molecules to treat dyslipidemia.

Amino Acid Sequence↗
PubMed2004

ClusterControl: a web interface for distributing and monitoring bioinformatics applications on a Linux cluster.

UNLABELLED: ClusterControl is a web interface to simplify distributing and monitoring bioinformatics applications on Linux cluster systems. We have developed a modular concept that enables integration of command line oriented program into the application framework of ClusterControl. The systems facilitate integration of different applications accessed through one interface and executed on a distributed cluster system. The package is based on freely available technologies like Apache as web server, PHP as server-side scripting language and OpenPBS as queuing system and is available free of charge for academic and non-profit institutions. AVAILABILITY: http://genome.tugraz.at/Software/ClusterControl

Computational Biology↗
PubMed2003

Microarray expression profiling: analysis and applications.

Recent advances in the technology of massive parallel gene expression profiling using microarrays are revolutionizing biomedical research and pharmacological discovery. These new and powerful tools allow researchers to examine the dynamics of a whole biological system by simultaneously interrogating the expression of tens of thousands of genes. This article will review the various bioinformatics and statistical methods commonly used to analyze the enormous amounts of data generated by this technology. The objective of these analyses is to reliably extract important biological information from carefully designed and controlled microarray experiments, and to organize the information in ways that reveal structured and coordinated physiological processes and key components controlling the biological responses studied. The major applications of the microarray technology, recently used to address a variety of biological questions of interest to scientists and clinicians in academia, healthcare and the pharmaceutical industry, will also be presented.

Databases, Genetic↗
PubMed2006

Meta-analysis based on control of false discovery rate: combining yeast ChIP-chip datasets.

MOTIVATION: High-throughput microarray technology can be used to examine thousands of features, such as all the genes of an organism, and measure their expression. Two important issues of microarray bioinformatics are first, how to combine the significance values for each feature across experiments with high statistical power, and second, how to control the proportion of false positives. Existing methods address these issues separately, in spite of their linked usage. RESULTS: We present a novel method (ESP) to address the two requirements in an interdependent way. It generalizes the truncated product method of Zaykin et al. to combine only those significance values which clear their respective experiment-specific false discovery restrictive thresholds, thus allowing us to control the false discovery rate (FDR) for the final combined result. Further, we introduce several concepts that together offer FDR control, high power, quality control and speed-up in meta-analysis as done by our algorithm. Computational and statistical methods of research synthesis like the one described here will be increasingly important as additional genome-wide datasets accumulate in databases. We apply our method to combine three well-known ChIP-chip transcription factor binding datasets for budding yeast to identify significant intergenic regulatory sequences for nine cell cycle regulating transcription factors, both with high power and controlled FDR.

Algorithms↗
PubMed2004

Fungal genetic resource centres and the genomic challenge.

Fungal research and education has for many years been supported by public service genetic resource centres, whose roles have been to maintain, preserve and supply living cultures to the research community. In the genomic era, genetic resource centres are perhaps more important than ever before. The cultures held, many of which are described and validated by expert biosystematists, are valuable resources for the future. There is a need to supply genomic and proteomic research programmes with fully characterised organisms, as usage of organisms from unreliable sources can prove disastrous, not least in economical terms. However, mycologists often require more than just the organisms, for example, their associated information is vital for bioinformatic applications and some researchers may only require genomic DNA from the organism rather than the organism per se. Genetic resource centres are continually adapting to meet the needs of their users and the wider mycological research community, this associated with OECD international initiatives should ensure they exist to support research for many years to come. This review considers the impact of such initiatives, the current roles of fungal genetic resource centres, the mechanisms used to preserve organisms in a stable manner and the range of resources that are offered for genomic research.

Computational Biology↗
PubMed2002

Functional proteomic analysis of human nucleolus.

The notion of a "plurifunctional" nucleolus is now well established. However, molecular mechanisms underlying the biological processes occurring within this nuclear domain remain only partially understood. As a first step in elucidating these mechanisms we have carried out a proteomic analysis to draw up a list of proteins present within nucleoli of HeLa cells. This analysis allowed the identification of 213 different nucleolar proteins. This catalog complements that of the 271 proteins obtained recently by others, giving a total of approximately 350 different nucleolar proteins. Functional classification of these proteins allowed outlining several biological processes taking place within nucleoli. Bioinformatic analyses permitted the assignment of hypothetical functions for 43 proteins for which no functional information is available. Notably, a role in ribosome biogenesis was proposed for 31 proteins. More generally, this functional classification reinforces the plurifunctional nature of nucleoli and provides convincing evidence that nucleoli may play a central role in the control of gene expression. Finally, this analysis supports the recent demonstration of a coupling of transcription and translation in higher eukaryotes.

Cell Nucleolus↗
PubMed2000

Conceptual modelling of genomic information.

MOTIVATION: Genome sequencing projects are making available complete records of the genetic make-up of organisms. These core data sets are themselves complex, and present challenges to those who seek to store, analyse and present the information. However, in addition to the sequence data, high throughput experiments are making available distinctive new data sets on protein interactions, the phenotypic consequences of gene deletions, and on the transcriptome, proteome, and metabolome. The effective description and management of such data is of considerable importance to bioinformatics in the post-genomic era. The provision of clear and intuitive models of complex information is surprisingly challenging, and this paper presents conceptual models for a range of important emerging information resources in bioinformatics. It is hoped that these can be of benefit to bioinformaticians as they attempt to integrate genetic and phenotypic data with that from genomic sequences, in order to both assign gene functions and elucidate the different pathways of gene action and interaction. RESULTS: This paper presents a collection of conceptual (i.e. implementation-independent) data models for genomic data. These conceptual models are amenable to (more or less direct) implementation on different computing platforms.

Computational Biology↗
PubMed2003

The European Bioinformatics Institute web site: a new view.

SUMMARY: The European Bioinformatics Institute (EBI), and outstation of the European Molecular Biology laboratory, has revamped its web site for the second time since 1997 in order to address increased user demand as well as establishing better uniformity and easier accessibility for the ever growing number of users and services it offers to the community. A GRID-like hardware infrastructure has been put in place to provide round the clock services in a redundant and reliable fashion. AVAILABILITY: http://www.ebi.ac.uk/

Academies and Institutes↗
PubMed2006

Development of bioinformatic tools to support EST-sequencing, in silico- and microarray-based transcriptome profiling in mycorrhizal symbioses.

The great majority of terrestrial plants enters a beneficial arbuscular mycorrhiza (AM) or ectomycorrhiza (ECM) symbiosis with soil fungi. In the SPP 1084 "MolMyk: Molecular Basics of Mycorrhizal Symbioses", high-throughput EST-sequencing was performed to obtain snapshots of the plant and fungal transcriptome in mycorrhizal roots and in extraradical hyphae. To focus activities, the interactions between Medicago truncatula and Glomus intraradices as well as Populus tremula and Amanita muscaria were selected as models for AM and ECM symbioses, respectively. Together, almost, 20.000 expressed sequence tags (ESTs) were generated from different random and suppressive subtractive hybridization (SSH) cDNA libraries, providing a comprehensive overview of the mycorrhizal transcriptome. To automatically cluster and annotate EST-sequences, the BioMake and SAMS software tools were developed. In connection with the eNorthern software SteN, plant genes with a predicted mycorrhiza-induced expression were identified. To support experimental transcriptome profiling, macro- and microarray tools have been constructed for the two model mycorrhizae, based either on PCR-amplified cDNAs or 70mer oligonucleotides. These arrays were used to profile the transcriptome of AM and ECM roots under different conditions, and the data obtained were uploaded to the ArrayLIMS and EMMA databases that are designed to store and evaluate expression profiles from DNA arrays. Together, the EST- and transcriptome databases can be mined to identify candidate genes for targeted functional studies.

Computational Biology↗
PubMed2026

ReGAIN: a bioinformatics platform for assessing probabilistic co-occurrence between resistance genes in bacterial pathogens.

MOTIVATION: Multidrug-resistant bacterial pathogens continue to rise globally, yet scalable methods are needed to infer how resistance determinants co-occur across pathogen populations and to quantify conditional dependencies underlying co-occurrence and shared genetic context. RESULTS: We present ReGAIN (Resistance Gene Association and Inference Network), an open-source platform that applies Bayesian network structure learning to infer probabilistic, conditional dependency relationships among antibiotic resistance, heavy metal tolerance, stress response, and virulence determinants in bacteria. In contrast to pairwise co-occurrence analyses, ReGAIN reports conditional probabilities, relative risks, and absolute risk differences with confidence intervals to prioritize candidate relationships for downstream prioritization. Applied across ESKAPEE pathogens, ReGAIN recapitulated established resistance gene relationships and identified additional candidate patterns consistent with co-selection and shared genetic context. Together, these results support scalable, reproducible population-wide analysis of resistance networks for surveillance, comparative genomics and epidemiology. AVAILABILITY: ReGAIN analyses are performed using Python v3.11.5 and R v4.4.1 and is available as open-source software through Bioconda at {https://anaconda.org/bioconda/regain-cli}. Source code and documentation can be found at {https://github.com/ERBringHorvath/regain_CLI}. All genomes used in this publication were downloaded from the National Center for Biotechnology Information database. Large supplementary tables and results data from the ESKAPEE pathogen example network analyses can be downloaded from https://figshare.com/articles/dataset/ReGAIN_command_line_software_and_supplemental_figures_/28959431.

Computational Biology↗
PubMed2004

Mutations of RAI1, a PHD-containing protein, in nondeletion patients with Smith-Magenis syndrome.

Smith-Magenis syndrome (SMS) is a mental retardation/multiple congenital anomalies disorder associated with a heterozygous approximately 4-Mb deletion in 17p11.2. Patients with SMS show variability in clinical phenotype despite a common deletion found in >75-80% of patients. Recently, point mutations in the retinoic acid induced 1 (RAI1) gene, which lies within the SMS critical interval, were identified in three patients with many SMS features in whom no deletion was detected. It is not clear if the entire SMS phenotype can be accounted for by RAI1 haploinsufficiency, nor has the precise function of RAI1 been delineated. We report two novel RAI1 mutations, one frameshift and one nonsense allele, in nondeletion SMS patients. Comparisons of the clinical features in these two patients, three of the previously reported RAI1 point mutation cases, and the patients with a common deletion suggest that the majority of the clinical features in SMS result from RAI1 mutation, although phenotypic variability exists even among the individuals with RAI1 point mutations. Bioinformatics analyses of RAI1 and comparative genomics between human and mouse orthologues revealed a zinc finger-like plant homeo domain (PHD) at the carboxyl terminus that is conserved in the trithorax group of chromatin-based transcription regulators. These findings suggest RAI1 is involved in transcriptional control through a multi-protein complex whose function may be altered in individuals with SMS.

Alleles↗
PubMed2004

Bio-Mirror project for public bio-data distribution.

Timely worldwide distribution of biosequence and bioinformatics data depends on high performance networking and advances in Internet transport methods. The Bio-Mirror project focuses on providing up-to-date distribution of this rapidly growing and changing data. It offers FTP, Web and Rsync access to many high-volume databanks from several sites around the world. Experiments with data grids and other methods offer future improvements in biology data distribution.

Biology↗
PubMed2002

Application of high-throughput computing in bioinformatics.

We describe the challenges faced when developing a Linux/PC-based cluster to apply bioinformatics algorithms to the rapidly increasing raw genomics data available. The calculations, which take around two months to complete, result in a powerful resource that can be used for data mining--most obviously for the human genome. Our current infrastructure consists of a 1314 node cluster with 1734 processors supporting both production and research. This paper highlights the problems in achieving high data throughput with such systems and shows that raw computer power is only one component of a complex problem.

Computational Biology↗