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PubMed2004

caGEDA: a web application for the integrated analysis of global gene expression patterns in cancer.

The explosion of microarray data from pilot studies, basic research and large-scale clinical trials requires the development of integrative computational tools that can not only analyse gene expression patterns but that can also evaluate the methods of analysis adopted and then provide a boost to post-analysis translational interpretation of those patterns. We have developed a web application called caGEDA (cancer gene expression data analyzer) that can: (1) upload gene expression profiles from cDNA or oligonucleotide microarrays; (2) conduct a diverse range of serial linear normalisations; (3) identify differentially expressed genes using a variety of tests - either threshold or permutation tests; (4) produce tables of literature references to papers reporting that specific genes (identified by accession numbers) are up- or down-regulated in specific cancers; (5) estimate the error of sample class prediction using the significant gene set for features; (6) perform low-bias and accurate validated learning using three computational validation techniques (leave-one out validation, k-fold validation, random re-sampling validation); and (7) validate a classifier with a randomly selected or user-defined validation set. Significant genes are reported in a table of links to entries in the following databases: Locus Link, Genome View, UCSC, Ensembl, UniGene, dbSNP, AmiGO and OMIM. caGEDA is seamlessly integrated via embedded forms with UCSD's (University of California at San Diego) 2HAPI server (for medical subject heading (MeSH) term exploration) and EZ-Retrieve (to identify common transcription factors located upstream of sets of genes that exhibit similar modes of differential expression). caGEDA offers a variety of previously described and novel tests for differentially expressed genes, most notably the permutation percentile separability test, which is most appropriate for identifying genes that are significantly differentially expressed in a subset of patients. caGEDA, which is open source and free to academic users, will soon be greatly enhanced by operating with the components of the National Cancer Institute's new cancer bioinformatics grid (caBIG).

Biomarkers, Tumor↗
PubMed2004

Development of public toxicogenomics software for microarray data management and analysis.

A robust bioinformatics capability is widely acknowledged as central to realizing the promises of toxicogenomics. Successful application of toxicogenomic approaches, such as DNA microarray, inextricably relies on appropriate data management, the ability to extract knowledge from massive amounts of data and the availability of functional information for data interpretation. At the FDA's National Center for Toxicological Research (NCTR), we are developing a public microarray data management and analysis software, called ArrayTrack. ArrayTrack is Minimum Information About a Microarray Experiment (MIAME) supportive for storing both microarray data and experiment parameters associated with a toxicogenomics study. A quality control mechanism is implemented to assure the fidelity of entered expression data. ArrayTrack also provides a rich collection of functional information about genes, proteins and pathways drawn from various public biological databases for facilitating data interpretation. In addition, several data analysis and visualization tools are available with ArrayTrack, and more tools will be available in the next released version. Importantly, gene expression data, functional information and analysis methods are fully integrated so that the data analysis and interpretation process is simplified and enhanced. ArrayTrack is publicly available online and the prospective user can also request a local installation version by contacting the authors.

Databases, Genetic↗
PubMed2003

A biophysical approach to transcription factor binding site discovery.

Identification of transcription factor binding sites within regulatory segments of genomic DNA is an important step toward understanding of the regulatory circuits that control expression of genes. Here, we describe a novel bioinformatics method that bases classification of potential binding sites explicitly on the estimate of sequence-specific binding energy of a given transcription factor. The method also estimates the chemical potential of the factor that defines the threshold of binding. In contrast with the widely used information-theoretic weight matrix method, the new approach correctly describes saturation in the transcription factor/DNA binding probability. This results in a significant improvement in the number of expected false positives, particularly in the ubiquitous case of low-specificity factors. In the strong binding limit, the algorithm is related to the "support vector machine" approach to pattern recognition. The new method is used to identify likely genomic binding sites for the E. coli transcription factors collected in the DPInteract database. In addition, for CRP (a global regulatory factor), the likely regulatory modality (i.e., repressor or activator) of predicted binding sites is determined.

Algorithms↗
PubMed2004

PFIT and PFRIT: bioinformatic algorithms for detecting glycosidase function from structure and sequence.

The identification of the enzymes involved in the metabolism of simple and complex carbohydrates presents one bioinformatic challenge in the post-genomic era. Here, we present the PFIT and PFRIT algorithms for identifying those proteins adopting the alpha/beta barrel fold that function as glycosidases. These algorithms are based on the observation that proteins adopting the alpha/beta barrel fold share positions in their tertiary structures having equivalent sets of atomic interactions. These are conserved tertiary interaction positions, which have been implicated in both structure and function. Glycosidases adopting the alpha/beta barrel fold share more conserved tertiary interactions than alpha/beta barrel proteins having other functions. The enrichment pattern of conserved tertiary interactions in the glycosidases is the information that PFIT and PFRIT use to predict whether any given alpha/beta barrel will function as a glycosidase or not. Using as a test set a database of 19 glycosidase and 45 nonglycosidase alpha/beta barrel proteins with low sequence similarity, PFIT and PFRIT can correctly predict glycosidase function for 84% of the proteins known to function as glycosidases. PFIT and PFRIT incorrectly predict glycosidase function for 25% of the nonglycosidases. The program PSI-BLAST can also correctly identify 84% of the 19 glycosidases, however, it incorrectly predicts glycosidase function for 50% of the nonglycosidases (twofold greater than PFIT and PFRIT). Overall, we demonstrate that the structure-based PFIT and PFRIT algorithms are both more selective and sensitive for predicting glycosidase function than the sequence-based PSI-BLAST algorithm.

Algorithms↗
PubMed2002

Expressed sequence tag analysis of human retina for the NEIBank Project: retbindin, an abundant, novel retinal cDNA and alternative splicing of other retina-preferred gene transcripts.

PURPOSE: Expressed sequence tag (EST) analysis was performed on un-normalized, unamplified cDNA libraries constructed from adult human retina to examine the expression profile of the tissue and to contribute resources for functional genomics studies. METHODS: Two size fractionated cDNA libraries (designated hd and he) were constructed from human retina RNA. Clones were randomly selected for sequencing and analyzed using the bioinformatics program GRIST (GRouping and Identification of Sequence Tags). PCR, Northern blotting and other techniques have been used to examine selected novel transcripts. RESULTS: After informatics analysis, 2200 retina cDNAs yield 1254 unique clusters, potentially representing individual genes. Opsin is the most abundant transcript and other retina transcripts are prominently represented. One abundant cluster of cDNAs encodes retbindin, a novel, retina preferred transcript which has sequence similarity to riboflavin binding proteins and whose gene is on chromosome 19. Variant transcripts of known retina genes are also observed, including an alternative exon in the coding sequence of the transcription factor NRL and a skipped coding sequence exon in the phosphodiesterase gammasubunit (PDE6G). CONCLUSIONS: The new retina cDNA libraries compare favorably in quality with those already represented in public databases. They are rich in retina specific sequences and include abundant cDNAs for a novel protein, retbindin. The function of retbindin remains to be determined, but it is a candidate for flavinoid or carotenoid binding. Analysis of multiple clones for highly expressed retina genes reveals several alternative splice variants in both coding and noncoding sequences which may have functional significance. The validated set of retina cDNAs will contribute to a nonredundant set for microarray construction.

Aged↗
PubMed2004

Identification of genes expressed in primate primordial oocytes.

BACKGROUND: The factors involved in oocyte survival and transition from quiescence to the growing phenotype remain unknown. Herein we report genes that are differentially expressed in the primordial oocyte revealed by DNA arrays. METHODS: Primordial oocytes were captured selectively in rhesus monkey ovary sections using laser capture microdissection. The RNA was extracted and amplified in two rounds by T7-based linear RNA amplification, fluorescence labelled and then hybridized to human cDNA arrays containing 7680 elements. RNA from human placenta served as a reference sample. RESULTS: Ninety-five genes were found to be consistently expressed at a higher level in primordial oocytes. Expression of several of these genes in the oocyte has been reported before, e.g. deleted in azoospermia (DAZ), prohibitin and transglutaminase 2. Oocyte expression of several novel transcripts revealed on array hybridization, such as gene 33, ubiquitin-conjugating enzyme E2A, G1 to S phase transition 1, growth arrest and DNA damage-inducible (GADD), and dendritic cell-derived ubiquitin-like protein (DC-UbP) was confirmed by in situ hybridization. Some array-identified gene products [integrin beta3, alpha-tubulin, regulatory telomere elongation protein (RAP1) and cellular repressor of EIA-stimulated genes (CREG protein)] were detected in human oocytes by immunofluorescence. Bioinformatic analysis of the oocyte-enriched transcripts reveals a functional profile summarized as follows: cell cycle (14%); transporter (13%); signal transduction (10%); cytoskeletal (7%); transcription factor (5%); immune response (5%); apoptosis-related (5%); RNA processing (5%); and the remainder of miscellaneous categories. CONCLUSIONS: These observations may contribute to the elucidation of molecular pathways involved in oocyte survival and maturation.

Animals↗
PubMed2002

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Base Sequence↗
PubMed2004

A framework for scientific data modeling and automated software development.

MOTIVATION: The lack of standards for storage and exchange of data is a serious hindrance for the large-scale data deposition, data mining and program interoperability that is becoming increasingly important in bioinformatics. The problem lies not only in defining and maintaining the standards, but also in convincing scientists and application programmers with a wide variety of backgrounds and interests to adhere to them. RESULTS: We present a UML-based programming framework for the modeling of data and the automated production of software to manipulate that data. Our approach allows one to make an abstract description of the structure of the data used in a particular scientific field and then use it to generate fully functional computer code for data access and input/output routines for data storage, together with accompanying documentation. This code can be generated simultaneously for different programming languages from a single model, together with, for example for format descriptions and I/O libraries XML and various relational databases. The framework is entirely general and could be applied in any subject area. We have used this approach to generate a data exchange standard for structural biology and analysis software for macromolecular NMR spectroscopy. AVAILABILITY: The framework is available under the GPL license, the data exchange standard with generated subroutine libraries under the LGPL license. Both may be found at http://www.ccpn.ac.uk; http://sourceforge.net/projects/ccpn CONTACT: ccpn@mole.bio.cam.ac.uk.

Biopolymers↗
PubMed2002

A strategy for the rapid identification of phosphorylation sites in the phosphoproteome.

Edman phosphate ((32)P) release sequencing provides a high sensitivity means of identifying phosphorylation sites in proteins that complements mass spectrometry techniques. We have developed a bioinformatic assessment tool, the cleavage of radiolabeled protein (CRP) program, which enables experimental identification of phosphorylation sites via (32)P labeling and Edman degradation of cleaved proteins obtained at femtomole levels. By observing the Edman cycle(s) in which radioactivity is found, candidate phosphorylation sites are identified by determining which residues occur at the observed number of cycles downstream from a peptide cleavage site. In cases where more than one residue could be responsible for the observed radioactivity, additional experiments with cleavage reagents having alternative specificities may resolve the ambiguity. Given a protein sequence and a cleavage site, CRP performs these experiments in silico, identifying resolved sites based on user-supplied experimental data, as well as suggesting combinations of reagents for additional analyses. Analysis of the PhosphoBase protein sequence database suggests that CRP data from two cleavage experiments can be used to identify unambiguously 60% of known phosphorylation sites. Data from additional cleavage experiments may increase the overall coverage to 70% of known sites. By comparing theoretical data obtained from the CRP program with (32)P release data obtained from an Edman sequencer, a known phosphorylation site was identified unambiguously and correctly. In addition, our results show that in vivo phosphorylation sites can be determined routinely by differential proteolysis analysis and Edman cycling with less than 1 fmol of protein and 1000 cpm.

Amino Acid Sequence↗
PubMedDate not supplied

Applications of emerging technologies in toxicology and safety assessment.

Standardized protocols for repeat-dose toxicity studies have many advantages, including experimental redundancy (i.e., the use of more than a single experimental approach in the assessment of a given organ or tissue) and evaluation of numerous tissues to ensure detection of adverse effects, as well as the ability to develop robust historical control databases. However, traditional toxicology study designs may not adequately address questions of a mechanistic nature that might provide insights on whether particular toxicology findings in animals are relevant to humans. Such questions may be more readily answered using mechanism-based technologies such as toxicogenomics, proteomics, or metabonomics. Such newer approaches may permit, for example, the detailed assessment of the transcriptional profile differences that distinguish a normal healthy tissue from a diseased or damaged tissue. The resultant information can be used to elucidate the mechanism and accompanying biomarkers for toxicity, as well as to identify potential molecular targets for therapeutic intervention by drugs. Despite their conceptual appeal, the use of emerging technologies in toxicology is accompanied by significant challenges. For example, toxicogenomic assessments entail the generation of large amounts of bioinformatic data that must be interpretable for their full value to be realized. Also, none of these newer approaches have established uniformly acceptable quality standards (e.g., such as may be defined in interlaboratory validation studies) or a track record of achievement in guiding regulatory decisions. As a result, newer techniques, at least for the present, are more likely to be focused on mechanistic questions with compounds of known toxicity (either positive indicator compounds or "failed" pharmaceutical candidates). If the use of a nascent or emerging technology is contemplated for mechanistic studies of pharmaceutical compounds later in preregistration development, it will be crucial for toxicologists to engage their regulatory colleagues in discussions at an early stage to ensure closer alignment in thought. The successful use of emerging technologies to address toxicology issues will require a close partnership between industry and regulatory agencies.

Animals↗
PubMed2026

Clinical, genetic and bioinformatic analysis of Saudi families with Joubert syndrome and related disorders.

BACKGROUND: Joubert syndrome and related disorders (JSRD) are clinically and genetically heterogeneous ciliopathies caused by pathogenic variants in over 40 genes, mainly encoding ciliary proteins. However, data on JSRD in the Saudi population remain limited. This study aimed to identify novel and reported JSRD-causing variants in Saudi families and analyze their potential functional impact on protein structure using advanced computational tools. METHODS: Patients with genetically or clinically confirmed/suspected JSRD were recruited according to ethical protocols. Clinical data were collected, and exome sequencing was performed, followed by Sanger validation of identified variant. Pathogenicity was assessed using bioinformatics tools, while conservation, expression and RNA structure analyses were conducted to evaluate the potential functional consequences. RESULTS: Homozygous variants were identified in three consanguineous Saudi families, including a novel stop-gain variant in KIF7 (c.2992 C > T; p.(Gln998Ter)), and novel splice-site variant in CEP104 (c.489 + 1G > A) and a previously reported splice-site variant in TMEM237 (c.869 + 1G > A). All variants were predicted to be pathogenic as per ACMG criteria and located in highly conserved regions, suggesting potential functional impact. RNA analysis suggested possible alterations in folding. CONCLUSION: This study identified three pathogenic variants in JSRD-related genes in Saudi families, including two novel variants. These findings expand the variant spectrum for JSRD, particularly in the Saudi population. Establishing a comprehensive regional variant database is essential for improving molecular diagnosis and genetic counseling in population with high consanguinity. Finally, these results highlight the need for further functional studies to validate their pathogenicity and elucidate their role in JSRD pathogenesis.

Humans↗
PubMed2003

SNOW: standard nomenclature wizard to help searching for (bio) chemical standardized names.

UNLABELLED: When developing bioinformatical tools dealing with enzymatic activity, metabolism or enzymatic networks, the problem of the lack of a clear nomenclature for biochemical compounds often arises. This problem leads us to develop a small web-based tool (SNOW, Standard NOmenclature Wizard) which may help to find recommended and trivial names or the correct closest spelling for a query compound name, if it exists. AVAILABILITY: Web-based interface available at http://ibb.uab.es/snow/ SUPPLEMENTARY INFORMATION: http://ibb.uab.es/snow/snow_moreinfo.html

Algorithms↗
PubMed2006

Discrimination of non-protein-coding transcripts from protein-coding mRNA.

Several recent studies indicate that mammals and other organisms produce large numbers of RNA transcripts that do not correspond to known genes. It has been suggested that these transcripts do not encode proteins, but may instead function as RNAs. However, discrimination of coding and non-coding transcripts is not straightforward, and different laboratories have used different methods, whose ability to perform this discrimination is unclear. In this study, we examine ten bioinformatic methods that assess protein-coding potential and compare their ability and congruency in the discrimination of non-coding from coding sequences, based on four underlying principles: open reading frame size, sequence similarity to known proteins or protein domains, statistical models of protein-coding sequence, and synonymous versus non-synonymous substitution rates. Despite these different approaches, the methods show broad concordance, suggesting that coding and non-coding transcripts can, in general, be reliably discriminated, and that many of the recently discovered extra-genic transcripts are indeed non-coding. Comparison of the methods indicates reasons for unreliable predictions, and approaches to increase confidence further. Conversely and surprisingly, our analyses also provide evidence that as much as approximately 10% of entries in the manually curated protein database Swiss-Prot are erroneous translations of actually non-coding transcripts.

Algorithms↗
PubMed2002

Inferring protein function from genomic sequence: Giardia lamblia expresses a phosphatidylinositol kinase-related kinase similar to yeast and mammalian TOR.

Functional assays of genes have historically led to insights about the activities of a protein or protein cascade. However, the rapid expansion of genomic and proteomic information for a variety of diverse taxa is an alternative and powerful means of predicting function by comparing the enzymes and metabolic pathways used by different organisms. As part of the Giardia lamblia genome sequencing project, we routinely survey the complement of predicted proteins and compare those found in this putatively early diverging eukaryote with those of prokaryotes and more recently evolved eukaryotic lineages. Such comparisons reveal the minimal composition of conserved metabolic pathways, suggest which proteins may have been acquired by lateral transfer, and, by their absence, hint at functions lost in the transition from a free-living to a parasitic lifestyle. Here, we describe the use of bioinformatic approaches to investigate the complement and conservation of proteins in Giardia involved in the regulation of translation. We compare an FK506 binding protein homologue and phosphatidylinositol kinase-related kinase present in Giardia to those found in other eukaryotes for which complete genomic sequence data are available. Our investigation of the Giardia genome suggests that PIK-related kinases are of ancient origin and are highly conserved.

Amino Acid Sequence↗
PubMedDate not supplied

Proteome analysis of rhoptry-enriched fractions isolated from Plasmodium merozoites.

The rhoptries of Plasmodium species participate in merozoite invasion and modification of the host erythrocyte. However, only a few rhoptry proteins have been identified using conventional gene identification protocols. To investigate the protein organization of this organelle and to identify new rhoptry proteins, merozoite rhoptries from three different Plasmodium rodent species were enriched by sucrose density gradient fractionation, and subjected to proteome analysis using multidimensional protein identification technology (MudPIT); 148 proteins were identified. To distinguish abundant cellular contaminants from bona fide organellar proteins, a differential analysis comparing the proteins in the rhoptry-enriched fractions to proteins identified from whole cell lysates of P. berghei mixed asexual blood stages was undertaken. In addition, the proteins detected were analyzed for the presence of transmembrane domains, secretory signal peptide, cell adhesion motifs, and/or rhoptry-specific tyrosine-sorting motifs. Combining the differential analysis and bioinformatic approaches, a set of 36 proteins was defined as being potentially located to the Plasmodium rhoptries. Among these potential rhoptry proteins were homologues of known rhoptry proteins, proteases, and enzymes involved in lipid metabolism. Molecular characterization and understanding of the supramolecular organization of these novel potential rhoptry proteins may assist in the identification of new intervention targets for the asexual blood stages of malaria.

Animals↗
PubMed2003

Wrapping up BLAST and other applications for use on Unix clusters.

UNLABELLED: We have developed two programs that speed up common bioinformatic applications by spreading them across a UNIX cluster.(1) BLAST.pm, a new module for the 'MOLLUSC' package. (2) WRAPID, a simple tool for parallelizing large numbers of small instances of programs such as BLAST, FASTA and CLUSTALW. AVAILABILITY: The packages were developed in Perl on a 20-node Linux cluster and are provided together with a configuration script and documentation. They can be freely downloaded from http://wolfe.gen.tcd.ie/wrapper.

Computer Communication Networks↗
PubMedDate not supplied

Global genechip profiling to identify genes responsive to p53-induced growth arrest and apoptosis in human lung carcinoma cells.

To identify critical genes that mediate p53-induced growth arrest and apoptosis at a global level, we profiled a human lung carcinoma cell model in which cells undergo growth arrest and apoptosis in a p53 and DNA damage-dependent manner. Profiling of the Affymetrix human HG-U1333 GeneChip, covering the entire human transcriptome, revealed about 3, 000 unique genes either induced or repressed during p53-induced growth arrest or apoptosis, respectively. A total of 1, 057 genes, including many well-known p53 targets, responded to both conditions. A mini apoptotic protein database was generated from 3, 033 unique apoptosis responsive genes. Analysis of this database yielded 23 proteins with a pro-apoptotic BH3 domain and three with anti-apoptotic BIR2/BIR3 domains, including well-known p53 targets: Bax, Puma, Noxa and survivin. In addition, 14 mitochondrial proteins were identified that contain a pro-apoptotic AVPI-like motif, and 15 proteins were identified that contain a DAVPI-like domain with the potential of being cleaved by caspases during apoptosis to release the AVPI motif. Many of the genes we identified with these domains do contain p53-binding sites either in the promoter or in the first three introns, suggesting a high probability of being direct p53 targets. Pathway analysis revealed that p53 might control the Wnt pathway through transcriptional regulation of some of its components. Thus, global chip profiling coupled with bioinformatics analysis is a powerful tool in identification of genes critical for p53-induced apoptosis. Further characterization of these genes will lead to a better understanding of the mechanism of p53 action and p53 regulation of other signaling pathways. It will also provide novel cancer drug targets for further validation.

Apoptosis↗
PubMed2005

Assigning new GO annotations to protein data bank sequences by combining structure and sequence homology.

Accompanying the discovery of an increasing number of proteins, there is the need to provide functional annotation that is both highly accurate and consistent. The Gene Ontology (GO) provides consistent annotation in a computer readable and usable form; hence, GO annotation (GOA) has been assigned to a large number of protein sequences based on direct experimental evidence and through inference determined by sequence homology. Here we show that this annotation can be extended and corrected for cases where protein structures are available. Specifically, using the Combinatorial Extension (CE) algorithm for structure comparison, we extend the protein annotation currently provided by GOA at the European Bioinformatics Institute (EBI) to further describe the contents of the Protein Data Bank (PDB). Specific cases of biologically interesting annotations derived by this method are given. Given that the relationship between sequence, structure, and function is complicated, we explore the impact of this relationship on assigning GOA. The effect of superfolds (folds with many functions) is considered and, by comparison to the Structural Classification of Proteins (SCOP), the individual effects of family, superfamily, and fold.

Algorithms↗