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PubMed2006

Analysing the developing brain transcriptome with the GenePaint platform.

We discuss technical means by which the complexity of gene and protein signalling cascades can be projected onto the complex structure of the mammalian brain. We argue that this requires both robotics and novel computational tools to register images of gene expression, annotate expression patterns and quantify gene expression. When sufficiently enriched and detailed, such gene expression/neuroanatomical atlases are hypothesis-generating tools and contain in themselves much of the information needed to investigate function in normal and genetically or otherwise modified brains. To be successful and useful, data-rich and comprehensive gene expression/neuroanatomical atlases have to be web accessible and structured in a way that allows the application of data exploration and mining tools.

Animals↗
PubMed2006

Machine learning for detecting gene-gene interactions: a review.

Complex interactions among genes and environmental factors are known to play a role in common human disease aetiology. There is a growing body of evidence to suggest that complex interactions are 'the norm' and, rather than amounting to a small perturbation to classical Mendelian genetics, interactions may be the predominant effect. Traditional statistical methods are not well suited for detecting such interactions, especially when the data are high dimensional (many attributes or independent variables) or when interactions occur between more than two polymorphisms. In this review, we discuss machine-learning models and algorithms for identifying and characterising susceptibility genes in common, complex, multifactorial human diseases. We focus on the following machine-learning methods that have been used to detect gene-gene interactions: neural networks, cellular automata, random forests, and multifactor dimensionality reduction. We conclude with some ideas about how these methods and others can be integrated into a comprehensive and flexible framework for data mining and knowledge discovery in human genetics.

Algorithms↗
PubMed2000

Searching the expressed sequence tag (EST) databases: panning for genes.

The genomes of living organisms contain many elements, including genes coding for proteins. The portions of the genes expressed as mature mRNA, collectively known as the transcriptome, represent only a small part of the genome. The expressed sequence tag (EST) databases contain an increasingly large part of the transcriptome of many species. For this reason, these databases are probably the most abundant source of new coding sequences available today. However, the raw data deposited in the EST databases are to a large extent unorganised, unannotated, redundant and of relatively low quality. This paper reviews some of the characteristics of the EST data, and the methods that can be used to find novel protein sequences within them. It also documents a collection of databases, software and web sites that can be useful to biologists interested in mining the EST databases over the Internet, or in establishing a local environment for such analyses.

Algorithms↗
PubMed2004

Drosophila DNase I footprint database: a systematic genome annotation of transcription factor binding sites in the fruitfly, Drosophila melanogaster.

UNLABELLED: Despite increasing numbers of computational tools developed to predict cis-regulatory sequences, the availability of high-quality datasets of transcription factor binding sites limits advances in the bioinformatics of gene regulation. Here we present such a dataset based on a systematic literature curation and genome annotation of DNase I footprints for the fruitfly, Drosophila melanogaster. Using the experimental results of 201 primary references, we annotated 1367 binding sites from 87 transcription factors and 101 target genes in the D.melanogaster genome sequence. These data will provide a rich resource for future bioinformatics analyses of transcriptional regulation in Drosophila such as constructing motif models, training cis-regulatory module detectors, benchmarking alignment tools and continued text mining of the extensive literature on transcriptional regulation in this important model organism. AVAILABILITY: http://www.flyreg.org/ CONTACT: cbergman@gen.cam.ac.uk.

Binding Sites↗
PubMed2005

GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists.

Phenotype analysis is commonly recognized to be of great importance for gaining insight into genetic interaction underlying inherited diseases. However, few computational contributions have been proposed for this purpose, mainly owing to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. We developed and made available through GFINDer web server an original approach for the analysis of genetic disorder related genes by exploiting the information on genetic diseases and their clinical phenotypes present in textual form within the Online Mendelian Inheritance in Man (OMIM) database. Because several synonyms for the same name and different names for overlapping concepts are often used in OMIM, we first normalized phenotype location descriptions reducing them to a list of unique controlled terms representing phenotype location categories. Then, we hierarchically structured them and the correspondent genetic diseases according to their topology and granularity of description, respectively. Thus, in GFINDer we could implement specific Genetic Disorders modules for the analysis of these structured data. Such modules allow to automatically annotate user-classified gene lists with updated disease and clinical information, classify them according to the genetic syndrome and the phenotypic location categories, and statistically identify the most relevant categories in each gene class. GFINDer is available for non-profit use at http://www.bioinformatics.polimi.it/GFINDer/.

Data Interpretation, Statistical↗
PubMed2006

AnimalQTLdb: a livestock QTL database tool set for positional QTL information mining and beyond.

The Animal Quantitative Trait Loci (QTL) database (AnimalQTLdb) is designed to house all publicly available QTL data on livestock animal species from which researchers can easily locate and compare QTL within species. The database tools are also added to link the QTL data to other types of genomic information, such as radiation hybrid (RH) maps, finger printed contig (FPC) physical maps, linkage maps and comparative maps to the human genome, etc. Currently, this database contains data on 1287 pig, 630 cattle and 657 chicken QTL, which are dynamically linked to respective RH, FPC and human comparative maps. We plan to apply the tool to other animal species, and add more structural genome information for alignment, in an attempt to aid comparative structural genome studies (http://www.animalgenome.org/QTLdb/).

Animals↗
PubMed2004

RNA interference in protozoan parasites.

RNA interference or RNAi is defined as the mechanism through which gene-specific, double-stranded RNA (dsRNA) triggers degradation of homologous transcripts. Besides providing an invaluable tool to downregulate gene expression in a variety of organisms, it is now evident that RNAi extends its tentacles into both the nucleus and the cytoplasm and is involved in a variety of gene silencing phenomena. Here we review the current status of RNAi in protozoan parasites that cause diseases of considerable medical and veterinary importance throughout Africa, Asia and the Americas. RNAi was first discovered in Trypanosoma brucei, a species of the family Trypanosomatidae, and it rapidly became the method of choice to downregulate gene expression in these organisms. At the same time, mechanistic studies exposed a role for RNAi in the control of retroposon transcript abundance. Whereas RNAi is also present in T. congolense, other members of the same family of organisms, namely T. cruzi and Leishmania major, are RNAi-negative. In apicomplexan parasites, there is experimental evidence for RNAi in Plasmodium, but this is not supported by their genetic make up. In contrast, the genome of Toxoplasma gondii harbours gene candidates with convincing similarity to 'classical' RNAi genes. Thus, as previously shown in fungi, protozoan parasites are genetically heterogeneous as far as the RNAi pathway is concerned. Finally, database mining predicts that Entamoeba histolytica and Giardia intestinalis have an RNAi pathway and the presence of RNAi genes in Giardia supports the view that gene silencing by dsRNA appeared very early during evolution of the eukaryotic lineage.

Animals↗
PubMed2003

Bioinformatics Resources for In Silico Proteome Analysis.

In the growing field of proteomics, tools for the in silico analysis of proteins and even of whole proteomes are of crucial importance to make best use of the accumulating amount of data. To utilise this data for healthcare and drug development, first the characteristics of proteomes of entire species-mainly the human-have to be understood, before secondly differentiation between individuals can be surveyed. Specialised databases about nucleic acid sequences, protein sequences, protein tertiary structure, genome analysis, and proteome analysis represent useful resources for analysis, characterisation, and classification of protein sequences. Different from most proteomics tools focusing on similarity searches, structure analysis and prediction, detection of specific regions, alignments, data mining, 2D PAGE analysis, or protein modelling, respectively, comprehensive databases like the proteome analysis database benefit from the information stored in different databases and make use of different protein analysis tools to provide computational analysis of whole proteomes.

Journal Article↗
PubMed2005

EXPANDER--an integrative program suite for microarray data analysis.

BACKGROUND: Gene expression microarrays are a prominent experimental tool in functional genomics which has opened the opportunity for gaining global, systems-level understanding of transcriptional networks. Experiments that apply this technology typically generate overwhelming volumes of data, unprecedented in biological research. Therefore the task of mining meaningful biological knowledge out of the raw data is a major challenge in bioinformatics. Of special need are integrative packages that provide biologist users with advanced but yet easy to use, set of algorithms, together covering the whole range of steps in microarray data analysis. RESULTS: Here we present the EXPANDER 2.0 (EXPression ANalyzer and DisplayER) software package. EXPANDER 2.0 is an integrative package for the analysis of gene expression data, designed as a 'one-stop shop' tool that implements various data analysis algorithms ranging from the initial steps of normalization and filtering, through clustering and biclustering, to high-level functional enrichment analysis that points to biological processes that are active in the examined conditions, and to promoter cis-regulatory elements analysis that elucidates transcription factors that control the observed transcriptional response. EXPANDER is available with pre-compiled functional Gene Ontology (GO) and promoter sequence-derived data files for yeast, worm, fly, rat, mouse and human, supporting high-level analysis applied to data obtained from these six organisms. CONCLUSION: EXPANDER integrated capabilities and its built-in support of multiple organisms make it a very powerful tool for analysis of microarray data. The package is freely available for academic users at http://www.cs.tau.ac.il/~rshamir/expander.

Algorithms↗
PubMed2003

Genomics and variation of ionotropic glutamate receptors.

Sequencing of the human, mouse, and rat genomes has enabled a comprehensive informatics approach to gene families. This approach is informative for identification of new members of gene families, for cross-species sequence conservation related to functional conservation, for within-species diversity related to functional variation, and for historical effects of selection. This genome informatics approach also focuses our attention on genes whose genomic locations coincide with linkages to phenotypes. We are identifying ionotropic glutamate receptor (IGR) sequence variation by resequencing technologies, including denaturing high-performance liquid chromoatography (dHPLC), for screening and direct sequencing, and by information mining of public (e.g., dbSNP and ENSEMBL) and private (i.e., Celera Discovery System) sequence databases. Each of the 16 known IGRs is represented in these databases, their positions on a canonical physical map (for example, the Celera map) are established, and comparison to mouse and rat sequences has been performed, revealing substantial conservation of these genes, which are located on different chromosomes but found within syntenic groups of genes. A collection of 38 missense variants were identified by the informatics and resequencing approaches in several of these receptor genes, including GRIN2B, GRIN3B, GRIA2, GRIA3, and GRIK1. This represents only a fraction of the sequence variation across these genes, but, in fact, these may constitute a large fraction of the common polymorphisms at these genes, and these polymorphisms are a starting point for understanding the role of these receptors in neurogenetic variation. Genetically influenced human neurobehavioral phenotypes that are likely to be linked to IGR genetic variants include addictions, anxiety/dysphoria disorders, post-brain injury behavioral disorders, schizophrenia, epilepsy, pain perception, learning, and cognition. Thus, the effects of glutamate receptor variation may be protean, and the task of relating variation to behavior difficult. However, functional variants of (1) catechol-O-methyltransferase, (2) serotonin transporter, and (3) brain-derived neurotrophic factor have recently been linked both to behavioral differences and to intermediate phenotypes, suggesting a pathway by which functional variation at IGRs can be tied to an etiologically complex phenotype.

Animals↗
PubMed2003

Phosphoregulators: protein kinases and protein phosphatases of mouse.

With the completion of the human and mouse genome sequences, the task now turns to identifying their encoded transcripts and assigning gene function. In this study, we have undertaken a computational approach to identify and classify all of the protein kinases and phosphatases present in the mouse gene complement. A nonredundant set of these sequences was produced by mining Ensembl gene predictions and publicly available cDNA sequences with a panel of InterPro domains. This approach identified 561 candidate protein kinases and 162 candidate protein phosphatases. This cohort was then analyzed using TribeMCL protein sequence similarity clustering followed by CLUSTALV alignment and hierarchical tree generation. This approach allowed us to (1) distinguish between true members of the protein kinase and phosphatase families and enzymes of related biochemistry, (2) determine the structure of the families, and (3) suggest functions for previously uncharacterized members. The classifications obtained by this approach were in good agreement with previous schemes and allowed us to demonstrate domain associations with a number of clusters. Finally, we comment on the complementary nature of cDNA and genome-based gene detection and the impact of the FANTOM2 transcriptome project.

Animals↗
PubMed2002

A genomics approach to the early stages of triterpene saponin biosynthesis in Medicago truncatula.

The saponins of the model legume Medicago truncatula are glycosides of at least five different triterpene aglycones: soyasapogenol B, soyasapogenol E, medicagenic acid, hederagenin and bayogenin. These aglycones are most likely derived from beta-amyrin, a product of the cyclization of 2,3-oxidosqualene. Mining M. truncatula EST data sets led to the identification of sequences putatively encoding three early enzymes of triterpene aglycone formation: squalene synthase (SS), squalene epoxidase (SE), and beta-amyrin synthase (beta-AS). SS was functionally characterized by expression in Escherichia coli, two forms of SE by complementation of the yeast erg1 mutant, and beta-AS by expression in yeast. Beta-amyrin was the sole product of the cyclization of squalene epoxide by the recombinant M. truncatulabeta-AS, as judged by GC-MS and NMR. Transcripts encoding beta-AS, SS and one form of SE were strongly and co-ordinately induced, associated with accumulation of triterpenes, upon exposure of M. truncatula cell suspension cultures to methyl jasmonate. Sterol composition remained unaffected by jasmonate treatment. Molecular verification of induction of the triterpene pathway in a cell culture system provides a new tool for saponin pathway gene discovery by DNA array-based approaches.

Acetates↗
PubMed2002

PESI--an intelligent system for prediction of enzyme-substrate interactions based on experimental constraints.

We present a system for predicting protein-protein modifications, and demonstrate its usefulness in the field of signal transduction research. Signal transduction is one of the most important areas of investigation in biological research. One of the major mechanisms frequently employed by cells to regulate signal transduction processes involves protein phosphorylation by various kinases. As many as 1,000 protein kinases and 500 protein phosphatases in the human genome are thought to be involved in phosphorylation processes which regulate all aspects of cell function. The complexity of such interactions stems from the enormous number of factors and interactions, which makes the identification of putative substrates for any given enzyme by straightforward experimentation increasingly difficult. We present here a data mining algorithm, based on the similarity between the modifier proteins and between the modified proteins, and on experimental constraints. The application presented here (PESI) focuses on substrate phosphorylation by various enzymes. This algorithm reduces the number of substrate candidates for experimental study by about two orders of magnitude. Moreover, this algorithm has already yielded predictions for previously unknown substrates of the enzymes PKCdelta and PKCeta, which we have confirmed experimentally.

Algorithms↗
PubMed2005

Virus infection and the interferon response: a global view through functional genomics.

The primary focus of this chapter is on providing an overview of how we use the tools of functional genomics to study virus infection, the interferon response, and the mechanisms by which viruses attenuate or evade this response to ensure successful replication. We provide examples of the types of analyses we perform, experimental design considerations, and the tools and techniques we use for data processing and mining. We have not attempted to provide detailed protocols for performing microarray or proteomics experiments because even individual components of such analyses, for example, techniques for the isolation and amplification of ribonucleic acid, could easily be the subject of an entire chapter. Rather, our goal is to show how data obtained from global gene expression and protein profiling can be used to gain new insights into virus-host interactions, with particular emphasis on the interferon response and its modulation by virus infection.

Animals↗
PubMed2001

Analysis of complex brain disorders with gene expression microarrays: schizophrenia as a disease of the synapse.

The level of cellular and molecular complexity of the nervous system creates unique problems for the neuroscientist in the design and implementation of functional genomic studies. Microarray technologies can be powerful, with limitations, when applied to the analysis of human brain disorders. Recently, using cDNA microarrays, altered gene expression patterns between subjects with schizophrenia and controls were shown. Functional data mining led to two novel discoveries: a consistent decrease in the group of transcripts encoding proteins that regulate presynaptic function; and the most changed gene, which has never been previously associated with schizophrenia, regulator of G-protein signaling 4. From these and other findings, a hypothesis has been formulated to suggest that schizophrenia is a disease of the synapse. In the context of a neurodevelopmental model, it is proposed that impaired mechanics of synaptic transmission in specific neural circuits during childhood and adolescence ultimately results in altered synapse formation or pruning, or both, which manifest in the clinical onset of the disease.

Brain Chemistry↗
PubMed2026

Landscape of retron diversity across the SPIRE microbial metagenome resource reveals candidate novel type XI-like lineages.

Retrons are bacterial genetic elements encoding a specialized reverse transcriptase (RT) that synthesizes multicopy single-stranded DNA and are increasingly recognized as components of bacterial anti-phage defense systems. However, their diversity and ecological distribution across large-scale genomic resources remain poorly characterized. Here, we surveyed retron RTs across the SPIRE representative metagenome collection, a non-redundant, species-level data set spanning diverse microbial habitats. Using a curated panel of type-specific hidden Markov models, we identified retrons representing all canonical types together with additional divergent lineages. Retron distribution showed strong taxonomic and ecological structuring, with some groups restricted to specific bacterial phyla, whereas others were broadly distributed across environmental categories. Systematic novelty assessment identified two candidate type XI-like lineages, TXI_C2like and TXI_noncan_h, characterized by protease-independent architectures and distinct accessory modules associated with WYL- and DnaB_C-containing proteins, respectively. De novo covariance-based analyses further identified candidate msr/msd-like non-coding RNA structures in both lineages, supporting conservation of the canonical RT-ncRNA organizational framework despite extensive sequence divergence. Together, these findings expand the known diversity of retron systems and identify type XI-like retrons as a dynamic and previously underexplored evolutionary group.IMPORTANCERetrons are bacterial genetic elements that are increasingly exploited as programmable tools for genome editing, molecular recording, and biosensing in addition to their natural role in anti-phage defense. Despite this growing biotechnological interest, the true diversity of retrons across the bacterial world has remained largely unmapped. By mining a resource of over 100,000 processed microbial metagenomes, we uncovered thousands of retron sequences spanning known types as well as previously unrecognized lineages and found that their distribution is strongly shaped by both bacterial taxonomy and ecological niche. Among these, we identified two candidate new lineages related to type XI retrons that lack the protease domain typical of this group but instead carry distinct accessory proteins, expanding the known architectural diversity of these systems. These findings broaden the catalog of retron diversity available for functional characterization and biotechnological engineering and provide a framework for prioritizing candidate lineages for future experimental validation.

effectors↗
PubMed2000

Stochastic segment models of eukaryotic promoter regions.

We present a new statistical approach for eukaryotic polymerase II promoter recognition. We apply stochastic segment models in which each state represents a functional part of the promoter. The segments are trained in an unsupervised way. We compare segment models with three and five states with our previous system which modeled the promoters as a whole, i.e. as a single state. Results on the classification of a representative collection of human and D. melanogaster promoter and non-promoter sequences show great improvements. The practical importance is demonstrated on the mining of large contiguous sequences.

Algorithms↗
PubMed2004

TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences.

BACKGROUND: In the emerging field of environmental genomics, direct cloning and sequencing of genomic fragments from complex microbial communities has proven to be a valuable source of new enzymes, expanding the knowledge of basic biological processes. The central problem of this so called metagenome-approach is that the cloned fragments often lack suitable phylogenetic marker genes, rendering the identification of clones that are likely to originate from the same genome difficult or impossible. In such cases, the analysis of intrinsic DNA-signatures like tetranucleotide frequencies can provide valuable hints on fragment affiliation. With this application in mind, the TETRA web-service and the TETRA stand-alone program have been developed, both of which automate the task of comparative tetranucleotide frequency analysis. AVAILABILITY: http://www.megx.net/tetra. RESULTS: TETRA provides a statistical analysis of tetranucleotide usage patterns in genomic fragments, either via a web-service or a stand-alone program. With respect to discriminatory power, such an analysis outperforms the assignment of genomic fragments based on the (G+C)-content, which is a widely-used sequence-based measure for assessing fragment relatedness. While the web-service is restricted to the calculation of correlation coefficients between tetranucleotide usage patterns of submitted DNA sequences, the stand-alone program generates a much more detailed output, comprising all raw data and graphical plots. The stand-alone program is controlled via a graphical user interface and can batch-process a multitude of sequences. Furthermore, it comes with pre-computed tetranucleotide usage patterns for 166 prokaryote chromosomes, providing a useful reference dataset and source for data-mining. CONCLUSIONS: Up to now, the analysis of skewed oligonucleotide distributions within DNA sequences is not a commonly used tool within metagenomics. With the TETRA web-service and stand-alone program, the method is now accessible in an easy to use manner for a broad audience. This will hopefully facilitate the interrelation of genomic fragments from metagenome libraries, ultimately leading to new insights into the genetic potentials of yet uncultured microorganisms.

Base Composition↗