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PubMed2006

An application of statistics to comparative metagenomics.

BACKGROUND: Metagenomics, sequence analyses of genomic DNA isolated directly from the environments, can be used to identify organisms and model community dynamics of a particular ecosystem. Metagenomics also has the potential to identify significantly different metabolic potential in different environments. RESULTS: Here we use a statistical method to compare curated subsystems, to predict the physiology, metabolism, and ecology from metagenomes. This approach can be used to identify those subsystems that are significantly different between metagenome sequences. Subsystems that were overrepresented in the Sargasso Sea and Acid Mine Drainage metagenome when compared to non-redundant databases were identified. CONCLUSION: The methodology described herein applies statistics to the comparisons of metabolic potential in metagenomes. This analysis reveals those subsystems that are more, or less, represented in the different environments that are compared. These differences in metabolic potential lead to several testable hypotheses about physiology and metabolism of microbes from these ecosystems.

Algorithms↗
PubMed2004

Mining EST databases to resolve evolutionary events in major crop species.

Using plant EST collections, we obtained 1392 potential gene duplicates across 8 plant species: Zea mays, Oryza sativa, Sorghum bicolor, Hordeum vulgare, Solanum tuberosum, Lycopersicon esculentum, Medicago truncatula, and Glycine max. We estimated the synonymous and nonsynonymous distances between each gene pair and identified two to three mixtures of normal distributions corresponding to one to three rounds of genome duplication in each species. Within the Poaceae, we found a conserved duplication event among all four species that occurred approximately 50-60 million years ago (Mya); an event that probably occurred before the major radiation of the grasses. In the Solanaceae, we found evidence for a conserved duplication event approximately 50-52 Mya. A duplication in soybean occurred approximately 44 Mya and a duplication in Medicago about 58 Mya. Comparing synonymous and nonsynonymous distances allowed us to determine that most duplicate gene pairs are under purifying, negative selection. We calculated Pearson's correlation coefficients to provide us with a measure of how gene expression patterns have changed between duplicate pairs, and compared this across evolutionary distances. This analysis showed that some duplicates seemed to retain expression patterns between pairs, whereas others showed uncorrelated expression.

Computational Biology↗
PubMed2005

Expression profiling by microarrays in colorectal cancer (Review).

Genome-wide gene profiling studies using microarrays have the potential to improve diagnosis and treatment of human cancers. Microarrays have identified many genes that are deregulated in colorectal cancer compared to normal tissue. Groups of genes that are predictive of tumor stage or presence of metastases, hence putatively associated with cancer progression have also been revealed. Microarray studies have identified genes whose expression are impacted by chemotherapies for colorectal cancer, thus could potentially be used to predict response to treatments. Unique gene expression profiles have also been used to classify metastases of uncertain origin. The wide application of microarrays generates exciting prospects in translational research. However, to date overlaps of candidate gene lists associated with specific clinical/biological phenotypes remain disturbingly poor between studies. Overfitting, bias, reporting of only the best results, and fidelity of probe annotations could present limitations for the interpretation of results shown in microarray publications. Making raw data from these microarray experiments publicly available for analysis by other investigators using different analytical algorithms or for in silico studies may facilitate the most thorough mining of data from these expensive studies. Validations of the results using other more precise techniques and at the biological level represent critical follow-up goals for microarray studies.

Adenoma↗
PubMed2001

Mining associations between genetic markers, phenotypes, and covariates.

We used Haplotype Pattern Mining, HPM [Toivonen et al., Am J Hum Genet 67:133-45, 2000], for gene localization in Genetic Analysis Workshop (GAW) 12 isolate data. In HPM, association is analyzed by searching all trait-associated haplotype patterns. Data mining algorithms are utilized to make the search efficient. The strength of the haplotype-trait associations is measured by a linear model, into which a pre-seelected set of covariates is incorporated. Marker-wise patterns of association are used for predicting the disease gene location. Genome-wide scans of susceptibility genes for affection status as well as for the quantitative traits (Q1-Q5) were performed. First analyses were made with small sample sizes, 63-94 trios per trait, which is compared with a pilot study of a larger complex disease-mapping project. Subsequently, the analysis was repeated with approximately 600 cases and 600 controls per trait to give higher power to the analyses. With small sample sizes, only the susceptibility genes having the strongest effects on the traits could be localized. The larger sample size gave very good results: all susceptibility genes, except one, could be correctly localized. First experiments on candidate genes suggested that HPM is applicable even to fine mapping of mutations in DNA sequence.

Algorithms↗
PubMed2005

Analysing proteomic data.

The rapid growth of proteomics has been made possible by the development of reproducible 2D gels and biological mass spectrometry. However, despite technical improvements 2D gels are still less than perfectly reproducible and gels have to be aligned so spots for identical proteins appear in the same place. Gels can be warped by a variety of techniques to make them concordant. When gels are manipulated to improve registration, information is lost, so direct methods for gel registration which make use of all available data for spot matching are preferable to indirect ones. In order to identify proteins from gel spots a property or combination of properties that are unique to that protein are required. These can then be used to search databases for possible matches. Molecular mass, pI, amino acid composition and short sequence tags can all be used in database searches. Currently the method of choice for protein identification is mass spectrometry. Proteins are eluted from the gels and cleaved with specific endoproteases to produce a series of peptides of different molecular mass. In peptide mass fingerprinting, the peptide profile of the unknown protein is compared with theoretical peptide libraries generated from sequences in the different databases. Tandem mass spectroscopy (MS/MS) generates short amino acid sequence tags for the individual peptides. These partial sequences combined with the original peptide masses are then used for database searching, greatly improving specificity. Increasingly protein identification from MS/MS data is being fully or partially automated. When working with organisms, which do not have sequenced genomes (the case with most helminths), protein identification by database searching becomes problematical. A number of approaches to cross species protein identification have been suggested, but if the organism being studied is only distantly related to any organism with a sequenced genome then the likelihood of protein identification remains small. The dynamic nature of the proteome means that there really is no such thing as a single representative proteome and a complete set of metadata (data about the data) is going to be required if the full potential of database mining is to be realised in the future.

Animals↗
PubMed2006

Metabolomics technology and bioinformatics.

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.

Animals↗
PubMed2001

The utility of different representations of protein sequence for predicting functional class.

MOTIVATION: Data Mining Prediction (DMP) is a novel approach to predicting protein functional class from sequence. DMP works even in the absence of a homologous protein of known function. We investigate the utility of different ways of representing protein sequence in DMP (residue frequencies, phylogeny, predicted structure) using the Escherichia coli genome as a model. RESULTS: Using the different representations DMP learnt prediction rules that were more accurate than default at every level of function using every type of representation. The most effective way to represent sequence was using phylogeny (75% accuracy and 13% coverage of unassigned ORFs at the most general level of function: 69% accuracy and 7% coverage at the most detailed). We tested different methods for combining predictions from the different types of representation. These improved both the accuracy and coverage of predictions, e.g. 40% of all unassigned ORFs could be predicted at an estimated accuracy of 60% and 5% of unassigned ORFs could be predicted at an estimated accuracy of 86%.

Bacterial Proteins↗
PubMed2005

Which gene did you mean?

Computational Biology needs computer-readable information records. Increasingly, meta-analysed and pre-digested information is being used in the follow up of high throughput experiments and other investigations that yield massive data sets. Semantic enrichment of plain text is crucial for computer aided analysis. In general people will think about semantic tagging as just another form of text mining, and that term has quite a negative connotation in the minds of some biologists who have been disappointed by classical approaches of text mining. Efforts so far have tried to develop tools and technologies that retrospectively extract the correct information from text, which is usually full of ambiguities. Although remarkable results have been obtained in experimental circumstances, the wide spread use of information mining tools is lagging behind earlier expectations. This commentary proposes to make semantic tagging an integral process to electronic publishing.

Abstracting and Indexing↗
PubMed2006

dictyBase and the Dicty Stock Center.

dictyBase is the model organism database that houses all the sequence and associated data for Dictyostelium discoideum, including literature, researchers, and strains from the Dicty Stock Center. The database makes it possible to connect genes, proteins, and publications and is designed to address the needs of biologists and bioinformaticists alike. We provide tools for retrieving and analyzing data and strive to compile the highest quality, most up-to-date information. Here we will describe how to navigate the website and mine the extensive database to help users make optimal use of this invaluable resource.

Animals↗
PubMed2006

Predictive data mining in clinical medicine: current issues and guidelines.

BACKGROUND: The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments. PURPOSE: The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine. METHODS: We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons. RESULTS: The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this field. CONCLUSIONS: Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Understanding the main issues underlying these methods and the application of agreed and standardized procedures is mandatory for their deployment and the dissemination of results. Thanks to the integration of molecular and clinical data taking place within genomic medicine, the area has recently not only gained a fresh impulse but also a new set of complex problems it needs to address.

Clinical Medicine↗
PubMed2001

Proteomics of the nervous system.

The recent success of large-scale industrialized genomic sequencing opens new doors in studies of biological systems. In the current post-genomic era we must ask how to translate this DNA sequence information into an understanding of living cells, tissues and organisms. One of the major goals is to characterize protein function, biochemical pathways and networks. Achieving this aim is greatly advanced by application of new proteomic tools combined with database mining. Neuroscience in particular is poised to benefit from these approaches in light of its high complexity and cross-talk between different neurotransmitter receptors within the same synapse or across the synaptic cleft. Little is known about the global in vivo protein interactions within synapses, and the knowledge of all proteins present in such structures will help in determining sub-complexes and the modular arrangement of proteins within them. This article reviews the impact of and outlines the application of proteomic analysis in the field of neuroscience, illustrating this with the example of NMDA receptor complexes.

Animals↗
PubMed2004

An integrated tool for microarray data clustering and cluster validity assessment.

UNLABELLED: In this paper we present a data mining system, which allows the application of different clustering and cluster validity algorithms for DNA microarray data. This tool may improve the quality of the data analysis results, and may support the prediction of the number of relevant clusters in the microarray datasets. This systematic evaluation approach may significantly aid genome expression analyses for knowledge discovery applications. The developed software system may be effectively used for clustering and validating not only DNA microarray expression analysis applications but also other biomedical and physical data with no limitations. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html CONTACT: Nadia.Bolshakova@cs.tcd.ie.

Algorithms↗
PubMed2003

On combining multiple microarray studies for improved functional classification by whole-dataset feature selection.

As microarray technologies become routinely applied in genome laboratories for studying gene expression, it is not uncommon that experiments on identical or similar sets of genes are conducted by multiple laboratories for various functional studies of these genes. Much of such data are often available to researchers for their data analysis, either through collaborators or from online gene expression databases. It will be useful to combine data from different microarray studies to improve the microarray data mining results. We show that the functional classification of genes from microarray data can be improved further by combining gene expression data from multiple microarray studies, even if the experimental focus or conditions for each experimental study may differ. However, blindly combining all available datasets may not always improve the analysis results---it is important to be selective of the datasets for inclusion. In our approach, we consider each dataset to be one feature, and then apply feature selection strategies to select appropriate datasets for training. With a simple hill-climbing method, we show that gene classification performances can be improved by whole-dataset feature selection.

Algorithms↗
PubMed2005

ParPEST: a pipeline for EST data analysis based on parallel computing.

BACKGROUND: Expressed Sequence Tags (ESTs) are short and error-prone DNA sequences generated from the 5' and 3' ends of randomly selected cDNA clones. They provide an important resource for comparative and functional genomic studies and, moreover, represent a reliable information for the annotation of genomic sequences. Because of the advances in biotechnologies, ESTs are daily determined in the form of large datasets. Therefore, suitable and efficient bioinformatic approaches are necessary to organize data related information content for further investigations. RESULTS: We implemented ParPEST (Parallel Processing of ESTs), a pipeline based on parallel computing for EST analysis. The results are organized in a suitable data warehouse to provide a starting point to mine expressed sequence datasets. The collected information is useful for investigations on data quality and on data information content, enriched also by a preliminary functional annotation. CONCLUSION: The pipeline presented here has been developed to perform an exhaustive and reliable analysis on EST data and to provide a curated set of information based on a relational database. Moreover, it is designed to reduce execution time of the specific steps required for a complete analysis using distributed processes and parallelized software. It is conceived to run on low requiring hardware components, to fulfill increasing demand, typical of the data used, and scalability at affordable costs.

Algorithms↗
PubMed2026

Rhodococcus folensis sp. nov., an orange-red-pigmented bacterium from mining soil.

Mining-impacted environments represent chemically complex ecosystems that may harbor metabolically versatile and pigment-producing microorganisms. During a survey of pigment-producing bacteria from abandoned mining soil in Trabzon, Türkiye, a red-pigmented strain, designated FMA22T, was isolated and characterized using a polyphasic taxonomic approach. 16 S rRNA gene sequence analysis placed the strain within the genus Rhodococcus, showing the highest similarity to R. corynebacterioides DSM 20,151T (99.57%), R. kroppenstedtii DSM 44908ᵀ (99.06%) and R. trifolii T8T (98.96%). The strain was Gram-stain-positive, aerobic and non-motile, and grew at 4-40 °C. Polar lipids included phosphatidylethanolamine, diphosphatidylglycerol, phosphatidylinositol, phosphatidylinositol mannoside, phosphatidylcholine, five unidentified glycolipids, four unidentified lipids, one unidentified phospholipid and one unidentified phosphoglycolipid; MK-8(H2) was the major respiratory quinone. Major fatty acids were C18:1 ω9c, summed feature 3 (C16:1 ω7c/C16:1 ω6c) and C16:0. ANI and dDDH values with the closest relatives were below 76.8% and 20.5%, respectively. The draft genome (4.23 Mb; 67.2 mol% G + C; 4,106 CDSs) harbors a terpene-associated carotenoid cluster containing crtB, crtI and crtY. The orange-red pigment (λmax = 475 nm) showed antioxidant activity (DPPH SC₅₀ = 5.38 mg mL⁻¹; FRAP = 4.34 µmol TE g⁻¹) and weak but measurable HIV-1 reverse transcriptase inhibition (IC₅₀ = 22 mg mL⁻¹). These data support the proposal of Rhodococcus folensis sp. nov., with FMA22ᵀ (= LMG 34144ᵀ = DSM 120048ᵀ) as the type strain.

Soil Microbiology↗
PubMed2001

Differential selection after duplication in mammalian developmental genes.

Gene duplication provides the opportunity for subsequent refinement of distinct functions of the duplicated copies. Either through changes in coding sequence or changes in regulatory regions, duplicate copies appear to obtain new or tissue-specific functions. If this divergence were driven by natural selection, we would expect duplicated copies to have differentiated patterns of substitutions. We tested this hypothesis using genes that duplicated before the human/mouse split and whose orthologous relations were clear. The null hypothesis is that the number of amino acid changes between humans and mice was distributed similarly across different paralogs. We used a method modified from Tang and Lewontin to detect heterogeneity in the amino acid substitution pattern between those different paralogs. Our results show that many of the paralogous gene pairs appear to be under differential selection in the human/mouse comparison. The properties that led to diversification appear to have arisen before the split of the human and mouse lineages. Further study of the diverged genes revealed insights regarding the patterns of amino acid substitution that resulted in differences in function and/or expression of these genes. This approach has utility in the study of newly identified members of gene families in genomewide data mining and for contrasting the merits of alternative hypotheses for the evolutionary divergence of function of duplicated genes.

Amino Acid Substitution↗
PubMed2004

Building mouse phenotype ontologies.

The structured description of mutant phenotypes presents a major conceptual and practical problem. A general model for generating mouse phenotype ontologies that involves combing a variety of different ontologies to better link and describe phenotypes is presented. This model is based on the Phenotype and Trait Ontology schema proposal and incorporates practical limitations and designing solutions in an attempt to model a testbed for the first phenotype ontology constructed in this manner, namely the mouse behavior phenotype ontology. We propose the application of such a model could provide curators with a powerful mechanism of annotation, mining and knowledge representation as well as achieving some level of free text disassociation.

Animals↗
PubMed2005

Plant viral intergenic DNA sequence repeats with transcription enhancing activity.

BACKGROUND: The geminivirus and nanovirus families of DNA plant viruses have proved to be a fertile source of viral genomic sequences, clearly demonstrated by the large number of sequence entries within public DNA sequence databases. Due to considerable conservation in genome organization, these viruses contain easily identifiable intergenic regions that have been found to contain multiple DNA sequence elements important to viral replication and gene regulation. As a first step in a broad screen of geminivirus and nanovirus intergenic sequences for DNA segments important in controlling viral gene expression, we have 'mined' a large set of viral intergenic regions for transcriptional enhancers. Viral sequences that are found to act as enhancers of transcription in plants are likely to contribute to viral gene activity during infection. RESULTS: DNA sequences from the intergenic regions of 29 geminiviruses or nanoviruses were scanned for repeated sequence elements to be tested for transcription enhancing activity. 105 elements were identified and placed immediately upstream from a minimal plant-functional promoter fused to an intron-containing luciferase reporter gene. Transient luciferase activity was measured within Agrobacteria-infused Nicotiana tobacum leaf tissue. Of the 105 elements tested, 14 were found to reproducibly elevate reporter gene activity (>25% increase over that from the minimal promoter-reporter construct, p < 0.05), while 91 elements failed to increase luciferase activity. A previously described "conserved late element" (CLE) was identified within tested repeats from 5 different viral species was found to have intrinsic enhancer activity in the absence of viral gene products. The remaining 9 active elements have not been previously demonstrated to act as functional promoter components. CONCLUSION: Biological significance for the active DNA elements identified is supported by repeated isolation of a previously defined viral element (CLE), and the finding that two of three viral enhancer elements examined were markedly enriched within both geminivirus sequences and within Arabidopsis promoter regions. These data provide a useful starting point for virologists interested in undertaking more detailed analysis of geminiviral promoter function.

Arabidopsis↗