Abstract

The regulation of gene expression by transcriptional repression is an ancient and conserved mechanism that manifests itself in diverse ways. Here we summarize conserved pathways for transcriptional repression prevalent throughout all forms of life, as well as indirect mechanisms that appear to have originated in eukaryotes, consistent with the unique chromatin environment of eukaryotic genes. The direct interactions between transcriptional repressors and the core transcriptional machinery in bacteria and archaea are sufficient to generate a sophisticated suite of mechanisms that provide flexible control. These direct interactions contrast with the activity of corepressors, which provide an additional regulatory control in eukaryotes. Their modulation of chromatin structure represents an indirect pathway to downregulate transcription, and their diversity and modulation provide additional complexity suited to the requirements of elaborate eukaryotic repression patterns. New findings indicate that corepressors are not necessarily restricted to generating a single stereotypic output, but can rather exhibit diverse functional responses depending on the context in which they are recruited, providing a hitherto unsuspected additional source of diversity in transcriptional control. Mechanisms within eukaryotes appear to be highly conserved, with novel aspects chiefly represented by addition of lineage-specific corepressor scaffolds that provide additional opportunities for recruiting the same core machinery.

Copyright © 2010 Elsevier Ltd. All rights reserved.

Abstract

For 350 years after Galileo’s discoveries, ground-based telescopes and theoretical modelling furnished everything we knew about the Sun’s planetary retinue. Over the past five decades, however, spacecraft visits to many targets transformed these early notions, revealing the diversity of Solar System bodies and displaying active planetary processes at work. Violent events have punctuated the histories of many planets and satellites, changing them substantially since their birth. Contemporary knowledge has finally allowed testable models of the Solar System’s origin to be developed and potential abodes for extraterrestrial life to be explored. Future planetary research should involve focused studies of selected targets, including exoplanets.

Abstract

Genome-wide sequencing has enabled modern biomedical research to relate more and more events in healthy as well as disease-affected cells and tissues to the genomic sequence. Now next generation sequencing (NGS) extends that reach into multiple almost complete genomes of the same species, revealing more and more details about how individual genomes as well as individual aspects of their regulation differ from each other. The inclusion of NGS-based transcriptome sequencing, chromatin-immunoprecipitation (ChIP) of transcription factor binding and epigenetic analyses (usually based on DNA methylation or histone modification ChIP) completes the picture with unprecedented resolution enabling the detection of even subtle differences such as alternative splicing of individual exons. Functional genomics aims at the elucidation of the molecular basis of biological functions and requires analyses that go far beyond the primary analysis of the reads such as mapping to a genome template sequence. The various and complex interactions between the genome, gene products and metabolites define biological function, which necessitates inclusion of results other than sequence tags in quite elaborative approaches. However, the extra efforts pay off in revealing mechanisms as well as providing the foundation for new strategies in systems biology and personalized medicine. This review emphasizes the particular contribution NGS-based technologies make to functional genomics research with a special focus on gene regulation by transcription factor binding sites.

Abstract

Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist – such as cloud and heterogeneous computing – to successfully tackle our big data problems.

Abstract

The rapidly accumulating genome sequence data allow researchers to address fundamental biological questions that were not even asked just a few years ago. A major problem in genomics is the widening gap between the rapid progress in genome sequencing and the comparatively slow progress in the functional characterization of sequenced genomes. Here we discuss two key questions of genome biology: whether we need more genomes, and how deep is our understanding of biology based on genomic analysis. We argue that overly specific annotations of gene functions are often less useful than the more generic, but also more robust, functional assignments based on protein family classification. We also discuss problems in understanding the functions of the remaining ‘conserved hypothetical’ genes.

Abstract

BACKGROUND: The development of high-throughput laboratory techniques created a demand for computer-assisted result analysis tools. Many of these techniques return lists of genes whose interpretation requires finding relevant biological roles for the problem at hand. The required information is typically available in public databases, and usually, this information must be manually retrieved to complement the analysis. This process is a very time-consuming task that should be automated as much as possible.

RESULTS: GeneBrowser is a web-based tool that, for a given list of genes, combines data from several public databases with visualisation and analysis methods to help identify the most relevant and common biological characteristics. The functionalities provided include the following: a central point with the most relevant biological information for each inserted gene; a list of the most related papers in PubMed and gene expression studies in ArrayExpress; and an extended approach to functional analysis applied to Gene Ontology, homologies, gene chromosomal localisation and pathways.

CONCLUSIONS: GeneBrowser provides a unique entry point to several visualisation and analysis methods, providing fast and easy analysis of a set of genes. GeneBrowser fills the gap between Web portals that analyse one gene at a time and functional analysis tools that are limited in scope and usually desktop-based.

Abstract

Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

Abstract

BACKGROUND: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment.

RESULTS: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data.A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome.

CONCLUSIONS: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

Abstract

Integrating results from diverse experiments is an essential process in our effort to understand the logic of complex systems, such as development, homeostasis and responses to the environment. With the advent of high-throughput methods – including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and RNA sequencing (RNA-seq) – acquisition of genome-scale data has never been easier. Epigenomics, transcriptomics, proteomics and genomics each provide an insightful, and yet one-dimensional, view of genome function; integrative analysis promises a unified, global view. However, the large amount of information and diverse technology platforms pose multiple challenges for data access and processing. This Review discusses emerging issues and strategies related to data integration in the era of next-generation genomics.

Abstract

Integrating results from diverse experiments is an essential process in our effort to understand the logic of complex systems, such as development, homeostasis and responses to the environment. With the advent of high-throughput methods – including genome-wide association (GWA) studies, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and RNA sequencing (RNA-seq) – acquisition of genome-scale data has never been easier. Epigenomics, transcriptomics, proteomics and genomics each provide an insightful, and yet one-dimensional, view of genome function; integrative analysis promises a unified, global view. However, the large amount of information and diverse technology platforms pose multiple challenges for data access and processing. This Review discusses emerging issues and strategies related to data integration in the era of next-generation genomics.

Abstract

Metastasis resistant to therapy is the major cause of death from cancer. Despite almost 200 years of study, the process of tumor metastasis remains controversial. Stephen Paget initially identified the role of host-tumor interactions on the basis of a review of autopsy records. His “seed and soil” hypothesis was substantiated a century later with experimental studies, and numerous reports have confirmed these seminal observations. An improved understanding of the metastatic process and the attributes of the cells selected by this process is critical for the treatment of patients with systemic disease. In many patients, metastasis has occurred by the time of diagnosis, so metastasis prevention may not be relevant. Treating systemic disease and identifying patients with early disease should be our goal. Revitalized research in the past three decades has focused on new discoveries in the biology of metastasis. Even though our understanding of molecular events that regulate metastasis has improved, the contributions and timing of molecular lesion(s) involved in metastasis pathogenesis remain unclear. Review of the history of pioneering observations and discussion of current controversies should increase understanding of the complex and multifactorial interactions between the host and selected tumor cells that contribute to fatal metastasis and should lead to the design of successful therapy.

Abstract

BACKGROUND: Observed co-expression of a group of genes is frequently attributed to co-regulation by shared transcription factors. This assumption has led to the hypothesis that promoters of co-expressed genes should share common regulatory motifs, which forms the basis for numerous computational tools that search for these motifs. While frequently explored for yeast, the validity of the underlying hypothesis has not been assessed systematically in mammals. This demonstrates the need for a systematic and quantitative evaluation to what degree co-expressed genes share over-represented motifs for mammals. RESULTS: We identified 33 experiments for human and mouse in the ArrayExpress Database where transcription factors were manipulated and which exhibited a significant number of differentially expressed genes. We checked for over-representation of transcription factor binding sites in up- or down-regulated genes using the over-representation analysis tool oPOSSUM. In 25 out of 33 experiments, this procedure identified the binding matrices of the affected transcription factors. We also carried out de novo prediction of regulatory motifs shared by differentially expressed genes. Again, the detected motifs shared significant similarity with the matrices of the affected transcription factors. CONCLUSIONS: Our results support the claim that functional regulatory motifs are over-represented in sets of differentially expressed genes and that they can be detected with computational methods.