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Poxvirus bioinformatics.

Biochemical and functional analysis of poxvirus genomes, genes, and proteins has entered a new era with the recent sequencing of more than 30 poxvirus genomes. The management and analysis of this volume of sequence data in an efficient and effective manner requires specialized computer software. This chapter describes a number of bioinformatics techniques useful for analyzing poxvirus genomes. Some of the software discussed here have been developed by members of the Poxvirus Bioinformatics Resource Center (PBRC; funded by National Institutes of Health [NIH]) specifically for use with poxvirus genomes. These programs or, more accurately, suites of programs have many functions dedicated to poxvirus genome characterization. Significantly, this software has been designed with ease of use at a single location as the major goal.

Computational Biology↗

Receptor-binding sites: bioinformatic approaches.

It is increasingly clear that both transient and long-lasting interactions between biomacromolecules and their molecular partners are the most fundamental of all biological mechanisms and lie at the conceptual heart of protein function. In particular, the protein-binding site is the most fascinating and important mechanistic arbiter of protein function. In this review, I examine the nature of protein-binding sites found in both ligand-binding receptors and substrate-binding enzymes. I highlight two important concepts underlying the identification and analysis of binding sites. The first is based on knowledge: when one knows the location of a binding site in one protein, one can "inherit" the site from one protein to another. The second approach involves the a priori prediction of a binding site from a sequence or a structure. The full and complete analysis of binding sites will necessarily involve the full range of informatic techniques ranging from sequence-based bioinformatic analysis through structural bioinformatics to computational chemistry and molecular physics. Integration of both diverse experimental and diverse theoretical approaches is thus a mandatory requirement in the evaluation of binding sites and the binding events that occur within them.

Binding Sites↗

Bioinformatics platform development: from gene to lead compound.

In the past 10 yr, the field of bioinformatics has been characterized by the mapping of many genomes. These efforts have stimulated explosive development of novel bioinformatics and experimental approaches to predict the functions and metabolic role of the new proteins. The main application of the work is to search, validate, and prioritize new targets for designing a new generation of drugs. Modern computer and experimental methods for discovery of new lead compounds have also expanded and integrated into the process referred to as rational drug design. They are directed to accelerate and optimize the drug discovery process using experimental and virtual (computer-aided drug discovery) methods. Recently, these methods and approaches have merged into a "from gene to lead" platform that includes the processes from new target discovery through obtaining highly effective lead compounds. This chapter describes the strategies as employed by the "From Gene to Lead" platform, including the major computer and experimental approaches and their interrelationship. The latter part of the chapter contains some examples of the steps required for implementing this platform.

Computational Biology↗

Bioinformatics approaches to cancer gene discovery.

The Cancer Gene Anatomy Project (CGAP) database of the National Cancer Institute has thousands of known and novel expressed sequence tags (ESTs). These ESTs, derived from diverse normal and tumor cDNA libraries, offer an attractive starting point for cancer gene discovery. Data-mining the CGAP database led to the identification of ESTs that were predicted to be specific to select solid tumors. Two genes from these efforts were taken to proof of concept for diagnostic and therapeutics indications of cancer. Microarray technology was used in conjunction with bioinformatics to understand the mechanism of one of the targets discovered. These efforts provide an example of gene discovery by using bioinformatics approaches. The strengths and weaknesses of this approach are discussed in this review.

Basic Helix-Loop-Helix Proteins↗

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↗

A bioinformatics analysis of memory consolidation reveals involvement of the transcription factor c-rel.

Consolidation of long-term memory (LTM) is a complex process requiring synthesis of new mRNAs and proteins. Many studies have characterized the requirement for de novo mRNA and protein synthesis; however, few studies have comprehensively identified genes regulated during LTM consolidation. We show that consolidation of long-term contextual memory in the hippocampus triggers altered expression of numerous genes encompassing many aspects of neuronal function. Like contextual memory formation, this altered gene expression required NMDA receptor activation and was specific for situations in which the animal formed an association between a physical context and a sensory stimulus. Using a bioinformatics approach, we found that regulatory elements for several transcription factors are over-represented in the upstream region of genes regulated during consolidation of LTM. Using a knock-out mouse, we found that c-rel, one of the transcription factors identified in our bioinformatics study, is necessary for hippocampus-dependent long-term memory formation.

Animals↗

Bioinformatics: how it is being used to identify bacterial vaccine candidates.

Genomic sequencing has provided a tremendous amount of information that can be useful in vaccine target identification. The sheer volume of information available necessitates the use of new research disciplines and techniques. Using bioinformatics, researchers sift through available data to identify appropriate candidates for biological analysis. This review provides an overview of available bioinformatic techniques for vaccine candidate identification and a few examples of how these techniques are being applied to specific bacterial pathogens.

Antigens, Bacterial↗

An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer.

OBJECTIVE: To find new potential biomarkers and establish the patterns for the detection of ovarian cancer. METHODS: Sixty one serum samples including 32 ovarian cancer patients and 29 healthy people were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The protein fingerprint data were analyzed by bioinformatics tools. Ten folds cross-validation support vector machine (SVM) was used to establish the diagnostic pattern. RESULTS: Five potential biomarkers were found (2085 Da, 5881 Da, 7564 Da, 9422 Da, 6044 Da), combined with which the diagnostic pattern separated the ovarian cancer from the healthy samples with a sensitivity of 96.7%, a specificity of 96.7% and a positive predictive value of 96.7%. CONCLUSIONS: The combination of SELDI with bioinformatics tools could find new biomarkers and establish patterns with high sensitivity and specificity for the detection of ovarian cancer.

Adolescent↗

Protecting innovation in bioinformatics and in-silico biology.

Commercial success or failure of innovation in bioinformatics and in-silico biology requires the appropriate use of legal tools for protecting and exploiting intellectual property. These tools include patents, copyrights, trademarks, design rights, and limiting information in the form of 'trade secrets'. Potentially patentable components of bioinformatics programmes include lines of code, algorithms, data content, data structure and user interfaces. In both the US and the European Union, copyright protection is granted for software as a literary work, and most other major industrial countries have adopted similar rules. Nonetheless, the grant of software patents remains controversial and is being challenged in some countries. Current debate extends to aspects such as whether patents can claim not only the apparatus and methods but also the data signals and/or products, such as a CD-ROM, on which the programme is stored. The patentability of substances discovered using in-silico methods is a separate debate that is unlikely to be resolved in the near future.

Computational Biology↗

Biotool2Web: creating simple Web interfaces for bioinformatics applications.

UNLABELLED: Currently there are many bioinformatics applications being developed, but there is no easy way to publish them on the World Wide Web. We have developed a Perl script, called Biotool2Web, which makes the task of creating web interfaces for simple ('home-made') bioinformatics applications quick and easy. Biotool2Web uses an XML document containing the parameters to run the tool on the Web, and generates the corresponding HTML and common gateway interface (CGI) files ready to be published on a web server. AVAILABILITY: This tool is available for download at URL http://www.uni-muenster.de/Bioinformatics/services/biotool2web/ CONTACT: Georg Fuellen (fuellen@alum.mit.edu).

Computational Biology↗

Bioinformatics research on the SARS coronavirus (SARS_CoV) in China.

Severe acute respiratory syndrome (SARS) first appeared in 2002 in China, which fastly affected about 8000 patients over 29 countries and caused 774 fatalities. As its pathogen was identified as a new kind of coronavirus (SARS_CoV), its genome was quickly sequenced on several isolates. Studies on its functional genomics were performed by combinatorial application of all the available bioinformatics tools and the development of new programs. In this way, it was found that the four proteins were absolutely responsible for nosogenesis of SARS, i.e. spike (S) protein; small envelop (E) protein; membrane (M) protein; and nucleocaspid (N) protein. Molecular evolution studies have revealed that SARS must be originated from wild animals, and it was demonstrated that the major genetic variations in some critical genes, particularly the Spike gene, was essential for the transition from animal-to-human transmission to human-to-human transmission. Theoretical models, either Logistic model or SIR model, were developed to describe the transmission of SARS. The recorded difference of SARS spreading in Beijing and Hong Kong was also reasonably analyzed according to these models. The whole process of fruitful bioinformatics studies, along with other related scientific investigations have set up an unprecedented paradigm for human of how to battle against sudden-breaking and catastrophic epidemics.

China↗

Bioinformatics and protein design.

Traditionally, the development of drugs has focused on small molecule therapeutics. However, with recent advances in recombinant protein technology the potential of proteins as therapeutics is starting to be realized. Already there are protein drugs on the market, including naturally occurring proteins, engineered proteins and proteins introduced into the patient by way of gene therapy. The next generation of such drugs are likely to be designed molecules; proteins devised from scratch and specifically tailored to have a required 3-dimensional structure and biochemical function. The key to such de novo design is the extensive bioinformatics knowledge that has been obtained from experimentally determined protein structures during the past 40-50 years. The knowledge is far from complete; for example, the protein folding problem has not yet been completely solved. Despite this, bioinformatics plays a crucial role in protein design, as is outlined in this review, and a number of de novo protein structures have been successfully designed in recent years. Some examples of these successes, which are available in the Protein Data Bank, are presented. They suggest that carefully designed protein therapeutics are a genuine prospect for the future.

Animals↗

Crystallographic and bioinformatic studies on restriction endonucleases: inference of evolutionary relationships in the "midnight zone" of homology.

Type II restriction endonucleases (ENases) cleave DNA with remarkable sequence specificity. Their discovery in 1970 and studies on molecular genetics and biochemistry carried out over the past four decades laid foundations for recombinant DNA techniques. Today, restriction enzymes are indispensable tools in molecular biology and molecular medicine and a paradigm for proteins that specifically interact with DNA as well as a challenging target for protein engineering. The sequence-structure-function relationships for these proteins are therefore of central interest in biotechnology. However, among numerous ENase sequences, only a few exhibit statistically significant similarity in pairwise comparisons, which was initially interpreted as evidence for the lack of common origin. Nevertheless, X-ray crystallographic studies of seemingly dissimilar type II ENases demonstrated that they share a common structural core and metal-binding/catalytic site, arguing for extreme divergence rather than independent evolution. A similar nuclease domain has been also identified in various enzymes implicated in DNA repair and recombination. Ironically, following the series of crystallographic studies suggesting homology of all type II ENases, bioinformatic studies provided evidence that some restriction enzymes are in fact diverged members of unrelated nuclease superfamilies: Nuc, HNH and GIY-YIG. Hence, the restriction enzymes as a whole, represent a group of functionally similar proteins, which evolved on multiple occasions and subsequently diverged into the "midnight zone" of homology, where common origins within particular groups can be inferred only from structure-guided comparisons. The structure-guided approaches used for this purpose include: identification of functionally important residues using superposition of atomic coordinates, alignment of sequence profiles enhanced by secondary structures, fold recognition, and homology modeling. This review covers recent results of comparative analyses of restriction enzymes from the four currently known superfamilies of nucleases with distinct folds, using crystallographic and bioinformatic methods, with the emphasis on theoretical predictions and their experimental validation by site-directed mutagenesis and biochemical analyses of the mutants.

Amino Acid Sequence↗

Bioinformatics and functional magnetic resonance imaging in clinical populations: practical aspects of data collection, analysis, interpretation, and management.

In this paper the authors review the issues associated with bioinformatics and functional magnetic resonance (fMR) imaging in the context of neurosurgery. They discuss the practical aspects of data collection, analysis, interpretation, and the management of large data sets, and they consider the challenges involved in the adoption of fMR imaging into clinical neurosurgical practice. Their goal is to provide neurosurgeons and other clinicians with a better understanding of some of the current issues associated with bioinformatics or neuroinformatics and fMR imaging. Thousands to tens of thousands of images are typically acquired during an fMR imaging session. It is essential to follow an activation task paradigm exactly to obtain an accurate representation of cortical activation. These images are then interactively postprocessed offline to produce an activation map, or in some cases a series of maps. The maps may then be viewed and interpreted in consultation with a neurosurgeon and/or other clinicians. After this consultation, long-term archiving of the processed fMR activation maps along with the standard structural MR images is a complex but necessary final step in this process. The fMR modality represents a valuable tool in the neurosurgical planning process that is still in the developmental stages for routine clinical use, but holds exceptional promise for patient care.

Brain Mapping↗

An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics.

AIM: To find new potential biomarkers and to establish patterns for early detection of colorectal cancer. METHODS: One hundred and eighty-two serum samples including 55 from colorectal cancer (CRC) patients, 35 from colorectal adenoma (CRA) patients and 92 from healthy persons (HP) were detected by surface-enhanced laser desorption/ionization mass spectrometry (SELDI-MS). The data of spectra were analyzed by bioinformatics tools like artificial neural network (ANN) and support vector machine (SVM). RESULTS: The diagnostic pattern combined with 7 potential biomarkers could differentiate CRC patients from CRA patients with a specificity of 83%, sensitivity of 89% and positive predictive value of 89%. The diagnostic pattern combined with 4 potential biomarkers could differentiate CRC patients from HP with a specificity of 92%, sensitivity of 89% and positive predictive value of 86%. CONCLUSION: The combination of SELDI with bioinformatics tools could help find new biomarkers and establish patterns with high sensitivity and specificity for the detection of CRC.

Biomarkers, Tumor↗

[Cloning and bioinformatics of human REV3 gene promoter region and its response to carcinogen N-methyl-N'-nitro-N-nitrosoguanidine].

OBJECTIVE: To understand the up regulatory mechanism of human REV3 gene induced by the chemical carcinogen N-methyl-N'-nitro-N-nitrosoguanidine (MNNG). METHODS: Bioinformatic analysis of human REV3 gene promoter region was based on BLAST alignment, promoter prediction software and recognition of transcriptional factor binding sites. Cloning of human REV3 gene promoter region was performed by nested PCR. Response of human REV3 gene promoter to the chemical carcinogen MNNG was measured by transient transfection assay based on the dual luciferase reporter assay system. RESULT: Bioinformatic analysis showed that human REV3 gene promoter region was located on chromosome 6 PAC clone RP3-415N12, and that the hypothetical promoter region contained promoter sequences, rich CpG islands, and putative recognition sites for several transcriptional factors, including AP-1/c-Jun/c-Fos, AP-2, STAT, CREBP, and NF-kappaB. Reconstructed reporter plasmid pGL3- 2582 was established by inserting 2582 nucleotides from the promoter region into the luciferase reporter vector pGL3-Basic. Transient transfection assay showed the hypothetical REV3 promoter region had promoter function, and it responded to MNNG treatment (P<0.01). CONCLUSION: Human mutator REV3 gene promoter region has been successfully cloned. The response of REV3 promoter region to MNNG suggests that REV3 gene can be regulated at transcriptional level under conditions of genotoxic stress.

Base Sequence↗

Primer on medical genomics part V: bioinformatics.

Bioinformatics is the discipline that develops and applies informatics to the field of molecular biology. Although a comprehensive review of the entire field of bioinformatics is beyond the scope of this article, I review the basic tenets of the field and provide a topical sampling of the popular technologies available to clinicians and researchers. These technologies include tools and methods for sequence analysis (nucleotide and protein sequences), rendering of secondary and tertiary structures for these molecules, and protein fold prediction that can lead to rational drug design. I then discuss signaling pathways, new standards for data representation of genes and proteins, and finally the promise of merging these molecular data with the clinical world (the new science of phenomics).

Computational Biology↗

Identification of novel highly expressed genes in pancreatic ductal adenocarcinomas through a bioinformatics analysis of expressed sequence tags.

In most microarray experiments, a significant fraction of the differentially expressed mRNAs identified correspond to expressed sequence tags (ESTs) and are generally discarded from further analyses. We used careful bioinformatics analyses to characterize those ESTs that were found to be highly overexpressed in a series of pancreatic adenocarcinomas. cDNA was prepared from 60 non-neoplastic samples (normal pancreas [n = 20], normal colon [n = 10], or normal duodenal mucosal [n = 30]) and from 64 pancreatic cancers (resected cancers [n = 50] or cancer cell lines [n = 14]) and hybridized to the complete Affymetrix Human Genome U133 GeneChip(R) set (arrays U133A and B) for simultaneous analysis of 45,000 fragments corresponding to 33,000 known genes and 6,000 ESTs. The GeneExpress(R) software system Fold Change Analysis Tool was used and 60 ESTs were identified that were expressed at levels at least 3-fold greater in the pancreatic cancers as compared to normal tissues. Searches against the human genomic sequence and comparative genomic analysis of human and mouse genomes was carried out using basic local alignment search tools (BLAST), BLASTN, and BLASTX, for identifying protein coding genes corresponding to the ESTs. Subsequently, in order to pick the most relevant candidate genes for a more detailed analysis, we looked for domains/motifs in the open reading frames using SMART and Pfam programs. We were able to definitively map 43 of the 60 ESTs to known or novel genes, and 15 of the ESTs could be localized in close proximity to a gene in the human genome although we were unable to establish that the EST was indeed derived from those genes. The differential expression of a subset of genes was confirmed at the protein level by immunohistochemical labeling of tissue microarrays (inhibin beta A [INHBA] and CD29) and/or at the transcript level by RT-PCR (INHBA, AKAP12, ELK3, FOXQ1, EIF5A2, and EFNA5). We conclude that bioinformatics tools can be used to characterize differentially overexpressed ESTs, and that some of these ESTs may represent diagnostically and therapeutically useful targets that might be missed using data solely from currently annotated databases.

Adenocarcinoma↗