An integrated ChIP-seq analysis platform with customizable workflows

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), enables unbiased and genome-wide mapping of protein-DNA interactions and epigenetic marks. The first step in ChIP-seq data analysis involves the identification of peaks (i.e., genomic locations with high density of mapped sequence reads). The next step consists of interpreting the biological meaning of the peaks through their association with known genes, pathways, regulatory elements, and integration with other experiments. Although several programs have been published for the analysis of ChIP-seq data, they often focus on the peak detection step and are usually not well suited for thorough, integrative analysis of the detected peaks.</p> <p>Results</p> <p>To address the peak interpretation challenge, we have developed ChIPseeqer, an integrative, comprehensive, fast and user-friendly computational framework for in-depth analysis of ChIP-seq datasets. The novelty of our approach is the capability to combine several computational tools in order to create easily customized workflows that can be adapted to the user's needs and objectives. In this paper, we describe the main components of the ChIPseeqer framework, and also demonstrate the utility and diversity of the analyses offered, by analyzing a published ChIP-seq dataset.</p> <p>Conclusions</p> <p>ChIPseeqer facilitates ChIP-seq data analysis by offering a flexible and powerful set of computational tools that can be used in combination with one another. The framework is freely available as a user-friendly GUI application, but all programs are also executable from the command line, thus providing flexibility and automatability for advanced users.</p

Colonial America

The first permanent British settlement in what became the United States was established in 1607, nearly 170 years prior to the American declaration of independence. This chapter examines the economic development of the British North American colonies that became the United States. As it describes, abundant natural resources and scarce labor and capital contributed to the remarkable growth in the size of the colonial economy, and allowed the free white colonial population to enjoy a relatively high standard of living. There was not, however, much improvement over time in living standards. Patterns of factor abundance also played an important role in shaping colonial institutions, encouraging reliance on indentured and enslaved labor as well as the development of representative government. For most of the colonial era, the colonists happily accepted their relationship to Britain. After 1763, however, changes in British policies following the end of the Seven Years War created growing tensions with the colonists and ultimately led to the colonies to declare their independence

Cell-Type Independent MYC Target Genes Reveal a Primordial Signature Involved in Biomass Accumulation

The functions of key oncogenic transcription factors independent of context have not been fully delineated despite our richer understanding of the genetic alterations in human cancers. The MYC oncogene, which produces the Myc transcription factor, is frequently altered in human cancer and is a major regulatory hub for many cancers. In this regard, we sought to unravel the primordial signature of Myc function by using high-throughput genomic approaches to identify the cell-type independent core Myc target gene signature. Using a model of human B lymphoma cells bearing inducible MYC, we identified a stringent set of direct Myc target genes via chromatin immunoprecipitation (ChIP), global nuclear run-on assay, and changes in mRNA levels. We also identified direct Myc targets in human embryonic stem cells (ESCs). We further document that a Myc core signature (MCS) set of target genes is shared in mouse and human ESCs as well as in four other human cancer cell types. Remarkably, the expression of the MCS correlates with MYC expression in a cell-type independent manner across 8,129 microarray samples, which include 312 cell and tissue types. Furthermore, the expression of the MCS is elevated in vivo in Eμ-Myc transgenic murine lymphoma cells as compared with premalignant or normal B lymphocytes. Expression of the MCS in human B cell lymphomas, acute leukemia, lung cancers or Ewing sarcomas has the highest correlation with MYC expression. Annotation of this gene signature reveals Myc's primordial function in RNA processing, ribosome biogenesis and biomass accumulation as its key roles in cancer and stem cells

The Extracellular Matrix and Blood Vessel Formation: Not Just a Scaffold

The extracellular matrix plays a number of important roles, among them providing structural support and information to cellular structures such as blood vessels imbedded within it. As more complex organisms have evolved, the matrix ability to direct signalling towards the vasculature and remodel in response to signalling from the vasculature has assumed progressively greater importance. This review will focus on the molecules of the extracellular matrix, specifically relating to vessel formation and their ability to signal to the surrounding cells to initiate or terminate processes involved in blood vessel formation

Reconciling carbon-cycle concepts, terminology, and methods

Author Posting. © The Author(s), 2006. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Ecosystems 9 (2006): 1041-1050, doi:10.1007/s10021-005-0105-7.Recent patterns and projections of climatic change have focused increased scientific and public attention on patterns of carbon (C) cycling and its controls, particularly the factors that determine whether an ecosystem is a net source or sink of atmospheric CO2. Net ecosystem production (NEP), a central concept in C-cycling research, has been used to represent two different concepts by C-cycling scientists. We propose that NEP be restricted to just one of its two original definitions—the imbalance between gross primary production (GPP) and ecosystem respiration (ER), and that a new term—net ecosystem carbon balance (NECB)—be applied to the net rate of C accumulation in (or loss from; negative sign) ecosystems. NECB differs from NEP when C fluxes other than C fixation and respiration occur or when inorganic C enters or leaves in dissolved form. These fluxes include leaching loss or lateral transfer of C from the ecosystem; emission of volatile organic C, methane, and carbon monoxide; and soot and CO2 from fire. C fluxes in addition to NEP are particularly important determinants of NECB over long time scales. However, even over short time scales, they are important in ecosystems such as streams, estuaries, wetlands, and cities. Recent technological advances have led to a diversity of approaches to measuring C fluxes at different temporal and spatial scales. These approaches frequently capture different components of NEP or NECB and can therefore be compared across scales only by carefully specifying the fluxes included in the measurements. By explicitly identifying the fluxes that comprise NECB and other components of the C cycle, such as net ecosystem exchange (NEE) and net biome production (NBP), we provide a less ambiguous framework for understanding and communicating recent changes in the global C cycle. Key words: Net ecosystem production, net ecosystem carbon balance, gross primary production, ecosystem respiration, autotrophic respiration, heterotrophic respiration, net ecosystem exchange, net biome production, net primary production

Genome-Wide Diet-Gene Interaction Analyses for Risk of Colorectal Cancer

Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention. © 2014

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams