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A large amount of experimental data generated by modern high-throughput technologies is available through various public repositories. Our knowledge about molecular interaction networks, functional biological pathways and transcriptional regulatory modules is rapidly expanding, and is being organized in lists of functionally related genes. Jointly, these two sources of information hold a tremendous potential for gaining new insights into functioning of living systems.
Genomics Portals platform integrates access to an extensive knowledge base and a large database of human, mouse, and rat genomics data with basic analytical visualization tools. It provides the context for analyzing and interpreting new experimental data and the tool for effective mining of a large number of publicly available genomics datasets stored in the back-end databases. The uniqueness of this platform lies in the volume and the diversity of genomics data that can be accessed and analyzed (gene expression, ChIP-chip, ChIP-seq, epigenomics, computationally predicted binding sites, etc), and the integration with an extensive knowledge base that can be used in such analysis.
The integrated access to primary genomics data, functional knowledge and analytical tools makes Genomics Portals platform a unique tool for interpreting results of new genomics experiments and for mining the vast amount of data stored in the Genomics Portals backend databases. Genomics Portals can be accessed and used freely at http://GenomicsPortals.org webcite.
A large amount of experimental data generated by modern high-throughput technologies is available through public repositories such as GEO [1] and ArrayExpress [2]. Our knowledge about molecular interaction networks and functional biological pathways is rapidly expanding and is being systematically organized into functionally related gene lists [3,4]. Jointly these two sources of information hold a tremendous potential for enhancing the interpretation of experimental results and gaining new insights into function of living systems. Mining such data has been a productive avenue in generating new hypothesis as well as validating experimental results [5]. Unfortunately, repositories currently housing much of the primary genomics data lack mechanisms for effective querying and analysis.
Inadequacies of the major data repositories to serve as access points to genomics data have resulted in numerous fragmented projects providing access to data from a single dataset [6,7], a set of thematically related datasets [8-10], or the results of genomics data analyses [11-15]. Except for the GeneChaser server [11], which provides access to results of differential expression analysis for all GEO DataSets, most of these resources are relatively small scale. Furthermore, they are generally focused on a single data type (mostly gene expression) and none of them facilitate the use of a functional knowledge base to construct query gene lists.
On the other end of the spectrum, several prominent efforts are directed towards constructing lists of functionally related gene lists [3,4], but they do not offer the capacity for querying genomics data based on these lists. The small exception is the capability of MSigDB server [4] to submit directly list of genes to the Gene Atlas server providing access to two microarray datasets [7].
The power of integrative analyses utilizing genomics data and functional knowledge has been demonstrated in the analysis of individual datasets [16] and systematic efforts to expand our understanding of gene functions [17-20]. In some cases, mining of new functional relationships predicted by integrative analysis of functional knowledge and genomics data is facilitated through predictive web servers [17]. Despite all these efforts, the integrated resources for accessing and analysis of both functional knowledge and the primary genomics data on a large scale are still lacking. Genomics Portals platform was designed to fill this gap.
The access to gene expression regulatory data such ChIP-chip and ChIP-seq transcription factor binding and epigenomics data is even more difficult and fewer resources are available. Most of the datasets are still deposited to the main genomics repositories. However, the only meaningful way to access this data is through UCSC and ENSEMBL Genome Browsers [21,22]. Both of these browsers are genomic feature - centric and do not provide meaningful analysis options and graphical displays for multiple gene promoters at the same time. On the other hand, presenting such data using heatmaps of many genes at a time has been commonly used in publications and is an effective way of exposing patterns in such data [23]. In the spirit of "group of genes queries" used throughout Genomics Portals, we facilitate the analysis and graphical presentation of this data in the form of heatmaps for a fixed window around the transcription start site and the whole list of genes at a time. We are unaware of any server other than Genomics Portals that offers similar functionality for accessing and analyzing this kind of data.
Genomics Portals platform was designed around the three conceptual problems faced daily by biomedical scientists:
1. Characterizing experimentally derived gene lists in the context of relevant publicly accessible genomics data (>82,000 genome scale data vectors; more than 1.8 billion data points). By simply pasting the experimentally derived gene list (e.g. differentially expressed genes, co-expressed genes, transcription factor regulated genes, epigenetically modified genes, etc) into the query box and then selecting the relevant datasets, one is able to download the primary data, perform basic analysis and generate publication-quality graphics depicting the expression patterns of the genes queried. The whole process can take less than one minute.
2. Functional analysis of newly generated data. By depositing newly generated data into Genomics Portals databases, one can leverage extensive knowledge base (>20,000 gene lists specific to biological pathways, diseases, transcriptional factor regulatory domains, etc), or browse our collection of analytical results to construct meaningful gene lists for querying their own data.
3. Integrative mining of public genomics data. Researchers can simply use the knowledge base and their own imagination to construct query gene lists and select genomics datasets to mine.
The conceptual structure of Genomics Portals is depicted in Figure 1. The Figure emphasizes the integrative nature of the platform. Diverse types of whole genome datasets are integrated with the functional knowledgebase and basic statistical and machine learning procedures into a comprehensive data mining environment. A typical analysis (Figure 2) starts by constructing a list of genes by either using the predefined lists, or pasting a gene list of interest; querying one of the databases with genome-scale data; and producing analysis summaries. Based on the analysis results, one can further refine their gene query list and repeat the procedure on a different dataset.
Figure 1. Conceptual structure of Genomics Portals. Diverse types of whole genome datasets are integrated with the functional knowledge base and basic statistical and machine learning procedures into a comprehensive data mining environment.
Figure 2. Typical analysis flow. A typical analysis (Figure 2) starts by constructing a list of genes by either using the predefined lists or pasting a gene list of interest, querying one of the databases with genome-scale data, and producing analysis summaries. Based on the analysis results, one can further refine their gene query list and repeat the procedure on a different dataset.
Genomics Portals knowledge base and gene list construction
Genomics Portals knowledge base consists of >20,000 lists of functionally related genes, Entrez GeneRIFs, and BioGRID gene-gene interaction data. One can construct a query gene list by pasting their own list, by manipulating the predefined gene lists, by an open text search of GeneRIFs, and by searching for interaction partners of a gene or a group of genes. One can also elicit a gene list by browsing pre-computed clustering results for different datasets.
Numerous efforts are underway to systematically collect and organize the functional information about genes. Our first goal is to assemble lists of functionally related genes from such collections. Following are sets of gene lists we gathered using publicly available resources: Gene Ontologies [24], KEGG pathways [25], Mouse Phenotypes [26], L2L lists of published differentially expressed genes [27], and miRBase predicted microRNA targets [28]. Additionally, we created lists of computationally predicted transcription factor targets, disease related gene lists based on text-processing GeneRIFs, and a limited set of custom created gene lists taken from the literature that were not found in the L2L database.
For a most of gene expression datasets we also performed unsupervised cluster analysis followed by in-depth functional annotation of the clustering structure. The cluster analysis was performed using the Bayesian model-based procedures [29,30] as well as simple hierarchical clustering. The functional annotation of the clustering structures was performed using the CLEAN framework [31]. The integrative browsing of the data and functional annotations is facilitated through the Functional TreeView (FTreeView) application which is a Java web-start based clustering browser [31] developed based on the open source Java TreeView browser [32]. Using FTreeView, one can identify clusters of genes based on their data profile and correlation with specific functional categories and use such gene lists to query and analyze genomics data in other datasets.
Genomics Portals genomics data
The vast majority of the genomics data deposited in our databases is public data downloaded from the major repositories (GEO, ArrayExpress, and UCSC Genome Browser), or produced by the computational analyses of genomics data (e.g. computationally predicted transcription factor binding sites). A small portion of the database is private data belonging to our collaborators which is accessible after a log-in. No registration or login is required for accessing the public data. In general, "genomics data" refers to genome-scale vector of measurements produced by various experimental assays (expression microarrays, CGH microarrays, ChIP-chip and ChIP-seq, etc.) or computationally constructed scores (CpG islands, transcription factor binding scores and microRNA target scores). Genomics datasets are organized thematically into different data portals. Different portals can contain datasets related to different diseases (e.g. Breast Cancer and Prostate Cancer), specific types of genomics data (e.g. Epigenomics and Transcription Factors), or different biological processes (e.g. Development). The same dataset can be assigned to different portals.
Gene expression data
Gene expression data in our databases consists of the complete collection of human, mouse and rat GEO DataSets (>1,500), and close to 100 manually curated GEO series of particular interest. The majority of the manually curated datasets are not available as GDS DataSets. For some of the manually curated datasets we have re-processed the raw outputs (e.g. CEL and GPR files).
ChIP-chip and ChIP-seq data
Most of these datasets have been manually curated and re-processed. The data is summarized as the average measurement intensities within non-overlapping 50 bp windows for the -4 kb to +1 kb regulatory region around each RefSeq sequence for the given genome build. This representation provides for a straightforward manipulation and graphical representation of such data for gene lists. The definition of "measurement intensities" depends on the primary data that was downloaded from the repository and it can range from typical log2-scaled fluorescence intensities for hybridization-based experiments to sequence read counts and various approaches to identifying and quantifying "peaks" for high-throughput sequencing technologies.
Computationally constructed scores
Most of this type of data is derived from computational assessments of DNA features within the genomic regulatory regions such the existence of transcription factor (TF) binding motifs and CpG islands. Computationally predicted transcription factor binding scores are provided as whole gene (i.e. Refseq) scores that assess the overall likelihood of a transcription factor binding within the gene's promoter and high resolution datasets that provide locations of putative TF binding sites at the same resolution used for ChIP-chip and ChIP-seq data. All scores were calculated using in-house developed scoring algorithms and TRANSFAC transcription factor binding motif definitions [33].
Genomics Portals analysis tools
In designing Genomics Portals we sought to strike a balance between the key limiting factors such as the complexity of query interfaces and the computational complexity of the analyses performed on the data, and the usefulness of the results produced. The portal is designed so that a basic output of the genomics data queries can be obtained in less than one minute with only a few clicks of the mouse. This includes query of our knowledge base or pasting one's own gene list, selecting the genomics data to query, and retrieving the data in the form of a spreadsheet or an R ExpressionSet. At this point, the user can either use their own data analytical tools or perform basic manipulation and analysis of the retrieved data within the portal. Subsequent analyses are performed using the retrieved data and the simple interface that deploys R and Bioconductor procedures using the Rserver infrastructure. The basic manipulations consist of sub-setting the data, selecting grouping variables for the samples in the datasets, identifying differentially expressed genes, cluster analysis, assessing enrichment of differentially expressed genes within the query list, etc. The results of such analyses are provided as static annotated heatmaps, or can be browsed using our FTreeView browser. Analyses of results can also be incorporated within the images of KEGG pathways.
Genomics Portals computational infrastructure
The basic computational infrastructure behind Genomics Portals consist of Java-based web interfaces and data viewers, MySQL databases for organizing knowledge base and genomics data, and R scripts for performing analysis of retrieved data using the RServer infrastructure to connect Java with R. The relational databases storing genomics data are loosely based on the MySQL version of ArrayExpress, MaxD http://www.bioinf.man.ac.uk webcite. Query gene lists are constructed and genomics datasets selected using Java-based interfaces. Java programs then call appropriate R scripts that query genomics databases (using RMySQL) and perform the analysis using various Bioconductor packages. The hardware infrastructure mirrors these three basic computational modules (Figure 3) and consists of the database server containing all MySQL databases, the web-server, and the computational server that runs RServer and is used for performing computational tasks. The modularity of the infrastructure ensures the responsiveness of the web server even under heavy computational load and effectively leverages computational resources of three different 8-core servers.
Figure 3. Hardware infrastructure. The hardware infrastructure mirrors three basic computational modules and consists of the database server containing all MySQL databases, the web-server, and the computational server that runs RServer and is used for performing computational tasks. The modularity of the infrastructure ensures the responsiveness of the web server even under heavy computational load and effectively leverages computational resources of three different 8-core servers.
Maintenance and Updates
Processing and uploading of all data in back-end databases are performed by executing standardized R scripts utilizing RMySQL package. All scripts performing any kind of operation on the back-end databases are documented and archived. In principle, we are able to re-create all databases from raw data by re-running archived R scripts. All updates to the data housed in the back-end databases are semi-automated, meaning that standardized R scripts performing updates are manually executed. Three aspects of the portal are periodically updated:
• The gene annotation table which is constructed by combining gene_info and homologene.data tables downloaded from respective NCBI ftp sites.
• Gene lists constructed from public annotation efforts (GO, KEGG, L2L and miRBase) are periodically updated by executing appropriate R scripts. For GO and KEGG updates we utilize most recent Bioconductor data objects. For L2L and miRBase lists, we download the current version from the primary web sites.
• For gene lists constructed by analyzing public data (Disease gene lists and Mouse phenotype gene lists), the process is similar except it proceeds in two steps. The current version of primary data (Gene RIFs and Mammalian phenotype ontologies) is downloaded and re-analyzed using existing R scripts to construct the gene list which are then uploaded to the back end database.
Processed primary genomics data is continually added, but there are no planned updates of the data that has already been uploaded. Each curated dataset is associated with a standardized R script that was used to process the raw data and upload the dataset to the back-end database. When an error is detected in any of the datasets, we generally remove the dataset, correct the error in the script used to upload the dataset and re-upload the dataset. This way, each manipulation of the data is documented by the R program that performed it. The download and processing of curated GEO data sets (GDS) was performed in batch using again specifically developed R programs, and new datasets will be periodically added as they become available using the same scripts.
Case Study: Characterizing experimentally identified proliferation signature
We demonstrate the utility of the Genomics Portals through a case study investigating a proliferation gene expression signature in rat mammary epithelium induced by different fatty acid diets [34]. That study established the increased proliferation of mammary epithelium as a consequence of several different dietary regiments in virgin female Spraque-Dawley rats. The study also identified a set of 85 genes whose expression levels were correlated with the increased proliferation. We used Genomics Portals to study the functional importance of these genes in five different biological processes examined in four gene expression datasets (Miller, Fournier, Herschkowitz, and Moggs) [35-38]. All analyses shown here were performed using the Genomics Portals web-interface. The step-by-step instructions for reproducing these results are described in Genomics Portals User Manual which is provided in additional file 1 as well as in the online help.
Additional file 1. Genomics Portals User Manual. The online help file containing screenshots and step-by-step instructions on how to use Genomics Portals.
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All four gene expression datasets used in the analysis investigate proliferation-related biological processes. For each of the datasets, we assessed the enrichment of differentially expressed genes in specific comparisons among the proliferation genes identified in the rat dietary study (Figure 4). For each comparison of interest, Genomics Portals scripts calculated Empirical Bayes p-values using the limma package [39] for the selected genes and a randomly selected list of probes of the same length. The enrichment of the statistically significant genes in the query list was then assessed using logistic regression (LRpath) [40]. The query list naturally consisted of rat gene identifiers which were automatically translated to appropriate human and mouse homologs while executing queries.
Figure 4. Proliferation signature in four transcriptional datasets. Genomics Portals were used to study the functional importance of 85 genes constituting in the rat mammary cell proliferation signature in five different biological processes examined in 4 gene expression datasets. A) The comparison of between the largest (top quartile) and smallest (bottom quartile) tumors with the assumption that large tumors are "more proliferative" than small tumors in the Miller dataset. B) The comparison between the proliferating and growth-arrested cell lines in the Fournier expression dataset. C) The comparison between all 13 different murine breast cancer models and normal controls in the Herschkowitz expression dataset. D) The transcriptional effects of estrogen on ER- breast cancer cell line after re-expression ESR1 in the Moggs expression dataset. E) The transcriptional effects of estrogen on ER- breast cancer cell line without re-expression ESR1 in the Moggs expression dataset.
The Miller dataset [35] is a well-annotated gene expression dataset profiling 251 primary human breast tumors. This dataset was re-processed and curated before deposited into the back-end databases under the id "GSE3494Entrez". The comparison of interest in this case (Figure 4A) was between the largest (top quartile) and smallest (bottom quartile) tumors with the assumption that large tumors are "more proliferative" than small tumors. Indeed the genes in the query list were up-regulated in large tumors and enriched for differentially expressed gene (LRpath p-value < 10-9).
The Fournier dataset [36] (Genomics Portals id "EGEOD8096Entrez") profiles gene expression of growth-arrested human mammary epithelium in-vitro. The comparison made was between the proliferating and growth-arrested cell lines (Figure 4B). Again, query genes were generally down-regulated in growth-arrested mammary acini and were enriched for differentially expressed genes (LRpath p-value < 10-8).
The Herschkowitz dataset [37] (Genomics Portals id "GSE3165gpl891") compares gene expression profiles of 13 different murine breast cancer models. The comparison made in our analysis was between all tumor tissues and normal controls (Figure 4C). Query genes were generally up-regulated in tumor tissues indicating, as expected, higher proliferation when compared to normal mammary tissue (LRpath p-value < 10-8).
The Moggs dataset [38] (Genomics Portals id "gdsGDS1326") examines transcriptional effects of estrogen on ER- breast cancer cell line after re-expression ESR1. While estrogen exposure generally increases the proliferation of ER+ breast cancer cells, in the ER- cell line with re-expression of ESR1 estrogen exposure reduces the proliferation. In our analysis we compared expression levels of our gene list before and after estrogen exposure in for both ER- cell line (MDA-MB-231) with and without re-expression of ESR1. In concordance with the observed phenotype, for the cell line with re-expressed ESR1 genes in our list are repressed after estrogen treatment (Figure 4D) in the statistically significant fashion (LRpath p-value < 10-6). For the same cell line without re-expressing ESR1, there was no discernible effect on expression of genes in our list (Figure 4E) after estrogen treatment (LRpath p-value = 0.6). Interestingly, the majority of genes in the re-expression experiments (Figure 4D) did not individually show statistically significant differential expression (at p-value < 0.05 level). However, jointly, the distribution of their p-values was strongly enriched for small p-values in comparison to randomly selected list of genes.
We established the universality of the proliferation signature identified in the rat dietary studies across four very different biological systems. Using the Genomics Portals interface, the entire process of querying and generating results in Figure 4 can be completed in less than 10 minutes. The strategy of assessing the statistical significance of enrichment by differentially expressed genes by comparing the query list p-values to the randomly selected probes is implemented to reduce the computational burden that would be imposed by retrieving data and calculating p-values for all probes. We assessed the validity of this strategy for the four datasets at hand by examining off-line the empirical cumulative distribution functions of p-values for genes in the query list (open circles) and genes probes not associated with genes in the query list (solid line) (Figure 4). For all four situations yielding statistically significant LRpath tests, the query list was obviously enriched for differentially expressed genes, which was also confirmed by the Kolomogorov-Smirnov test for differences between the two distributions. For the non-statistically significant comparison (Figure 4E), there was no difference between the empirical distributions functions for all probes (Figure 4E).
In addition to using gene expression data, we further characterized our proliferation signature using ChIP-seq data for E2F1 transcription factor (TF) [41] and by comparing histone modification marks between differentiated mouse embryonic fibroblasts (MEF) and a stem-cell "like" cell line [42]. In the original paper, an extended set of genes identified through cluster analysis was linked to regulatory domain of E2F transcription factors by examining the overlap with E2F targets established in ChIP-chip [43] and global expression profiling [44] experiments, and computationally predicted E2F targets. Here we used Genomics Portals to examine the newer ChIP-seq dataset assessing DNA binding of 15 different transcription factors, including E2F1, in mouse embryonic stem cells (Figure 5). In addition to most of the genes having a ChIP-seq peak for E2F1 within the regulatory region examined (-4 kb to +1 kb around TSS), there were several other transcription factor that seemed to have unusually many peaks for these genes. To test this hypothesis we again used the comparison to a "random" sample by LRpath. Instead of the p-values, in this situation Genomics Portals by default uses the maximum "peak intensity" calculated for each gene across its whole regulatory region. Such statistical analysis confirmed that in addition to E2F1 (p-value < 10-14), n-Myc (p-value < 10-7), Tcfp2l1 (p-value < .001), c-Myc (p-value < .01), and Klf4 (p-value < 0.01) all show signs of increased binding to regulatory regions of these genes.
Figure 5. Proliferation signature in transcription factor binding ChIP-seq data. Genomics Portals were used to examine the promoter binding events in the ChIP-seq dataset assessing DNA binding of 15 different transcription factors, including E2F1, in mouse embryonic stem cells. A) Binding patterns for all 15 transcription factors in the promoter regions (-4 kb to +1 kb around TSS) of the 85 genes constituting the rat mammary epithelium proliferation signature. B) n-Myc binding patterns in the promoters of a subset of the genes constituting the proliferation signature. This higher-resolution figure was generated by filtering-out other 14 transcription factors.
A similar analysis of two epigenomics histone marks, H3k4me3 (Figure 6A) and H3k27me3 (Figure 6B) across five human cell line at different "differentiation" stages [42] indicate that there is a subset of genes in our proliferation signature with strong tri-methylation of histone 3's lysine 4 across all 5 cell lines. On the other hand, tri-methylation of histone 3's lysine 27, in addition to differences between genes, also shows differences between different cell lines. Further examinations of functional relevance of these observations are beyond the scope of this paper.
Figure 6. Proliferation signature in epigenomics ChIP-seq data. Genomics Portals were used to examine patterns of two epigenomics histone marks, H3k4me3 (A) and H3k27me3 (B) across five human cell line at different "differentiation" stages.
By constructing Genomics Portals we sought to provide meaningful access to diverse genomics datasets currently residing in major public repositories for biomedical researchers without much technical bioinformatics expertise. There is a myriad of bioinformatics solutions that facilitate download and analysis of such datasets. For example, to process and populate our database we used R [45] and Bioconductor packages [46]. However, to acquire data and perform appropriate analyses using these tools requires a certain level of technical bioinformatics expertise. Genomics Portals allows researchers without such expertise to perform meaningful analysis of these datasets.
The general strategy of focusing on lists of functionally related genes as the basic unit for querying and analyzing genomics data is in-line with current thinking that specific functions of a living system are conferred by a coordinated action of a specific set of genes. In this paradigm, a single gene analysis is superseded by the joint analysis of all genes that are functionally related under certain conditions. The utility of such approaches has been demonstrated in numerous studies and confirmed in the case study we presented in this manuscript. Of course, a single gene can be treated as the list of size one and Genomics Portals allows queries and analysis based on the single gene as well. The second guiding idea in constructing the platform is the need to integrate functional data (gene expression), regulatory events data (epigenomics, TF binding) and the knowledge base (lists of functionally related genes).
Our decision to use R as the analytical engine within Genomics Portals provides us with virtually limitless possibilities for the analysis of data acquired in a given query. In the current implementation we sought to strike a balance between the key limiting factors such as the complexity of query interfaces and the computational complexity of the analyses performed on the data, and the usefulness of the results produced. This resulted in a highly streamlined interface with relatively few analytical options. Users who wish to perform additional off-line analyses can do so by downloading the data retrieved in the query. In future implementations we plan to "expose" more of the Bioconductor functionality through an additional "Advanced Analysis" web-interface while still keeping the current simple interface as the main option.
Other natural extensions of current infrastructure will be allowing for simultaneous queries of multiple datasets and addition of data from other organisms. The implementation of simultaneous queries of multiple datasets will be straightforward. However, query times and computational resources required to perform such queries will be additive in the number of datasets queried. This option will be included once we ascertain that our computational infrastructure is sufficiently robust to provide the current level of interactivity at such increased level of functionality. Expanding the data coverage to any other organism covered by Entrez Gene identifiers will also be rather straightforward. Since genomics data for different species always reside in different databases, adding such data will almost certainly not affect the performance of the portal.
The case study presented here is demonstrating the usage of only a small portion of the platform. Comprehensive online documentation is provided for complete description of the data and analytical options available through Genomics Portals.
Genomics Portals represents a powerful new tool for gaining knowledge from results of new genomics experiments as well as for mining a large collection of primary genome-scale datasets.
Availability and requirements
Project name: Genomics Portals
Project home page: http://GenomicsPortals.org webcite
Operating system: platform independent
Programming language: Java, MySQL, R
Other requirements: None
License: The tool is available online free of charge
Any restrictions to use by non-academics: None
Authors' contributions
KS developed the database infrastructure, tools for populating databases and web-interfaces for querying databases, MP participated in the overall design, developed the protocols and wrote R scripts for performing the analyses and producing graphical displays, MM conceived the portal, developed initial scripts for processing and uploading data and guided the development of all aspects of the portal, JF processed and analyzed a significant number of public datasets, JC developed protocols for processing and uploading, and processed most of the ChIP-chip and ChIP-seq data, QL processed and analyzed a significant portion of curated datasets, ZH computed the predicted transcription factor binding sites, VJ developed and integrated the FTreeView browser, KG assisted with scoring computationally the likelihood of transcription factors binding, JM provided important feedback through incorporating the use of the portal in several graduate courses he is teaching. All authors read and approved the final manuscript
This research was supported by grants from the National Human Genome Research Institute (R01 HG003749), National Library of Medicine (R21 LM009662) and NIEHS Center for Environmental Genetics grant (P30 ES06096).
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text:
Human Sleep and Cognition
Basic Research
Edited by
- Gerard Kerkhof, University of Amsterdam, The Netherlands
- Hans Van Dongen, Washington State University Spokane, USA
Neuroscientists, psychologists, neurologists
Book information
- Published: November 2010
- Imprint: ELSEVIER
Table of Contents
Basic aspects of sleep and cognition
1. Cognitive neuroscience of sleep - Gina R. Poe, Christine M. Walsh, Theresa E. Bjorness
2. Measurement of sleep - Bob Kemp
3. Measurement of cognition in studies of sleep deprivation - Paul Whitney, John M. Hinson
4. Sleep, memory and emotion - Matthew P. Walker
5. How treatment affects cognitive deficits in patients with sleep disorders: Methodological issues and results - Stephany Fulda, Hartmut Schulz
Sleep manipulation and impact on cognition
6. Sleep deprivation, chronic sleep restriction and sleep disruption - Amy C. Reynolds, Siobhan Banks
7. Effects of sleep deprivation on cognition - William D.S. Killgore
8. Circadian rhythms and cognition - Jim Waterhouse
9. The effects of napping on cognitive functioning - Nicole Lovato, Leon Lack
Developmental aspects of sleep and cognition
10. Sleep, intelligence and cognition in a developmental context: differentiation between traits and state-dependent aspects. Anja Geiger, Peter Achermann, Oskar G. Jenni
11. Do sleep complaints contribute to age-related cognitive decline? Ellemarije Altena, Jennifer R. Ramautar, Ysbrand D. Van Der Werf, Eus J.W. Van Someren

subset: commoncrawl


text:
hullio wrote:I beleive that capes should be able to be made and worn by all that come and play in the great adventures of Mysterious mine craft. I here by declare a petition for the Bringing up of capes as additions to skins that you can design and create a cape wear it and show it off your creation. we deserve capes just as much as the man who one 1 millionth buyer we should have to be a lucky winner or go to mine con for a cape in mine craft we should be aloud to the right of capes just like the privileged few who have them.
thank you
please wright you mine craft IGN if you support this movement
hullio wrote:v pixel we do deserve it the fact that only a small amount of people get to have capes while
the other 5,152,172 don't is unrelieved and underprivileged
Netnei wrote:hullio wrote:v pixel we do deserve it the fact that only a small amount of people get to have capes while
the other 5,152,172 don't is unrelieved and underprivileged
Underprivileged? Seriously? This is the reason why that poor guy who came up with the pigman idea got his cape taken away. Heres the Mojang team adding all this amazing new content to the game and your complaining about capes.
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text: Facts about the First Battle Of Bull Run, a Civil War Battle of the American Civil War Battle Of Bull Run Facts Location Manassas, Virginia. Fairfax County and Prince William County Dates July 21, 1861 Generals Union: Brig. Gen. Irvin McDowell Confederate: Brig. Gen. P.G.T. Beauregard & Gen. Joseph E. Johnston Soldiers Engaged Union: 28,400 Confederate: Beauregard: 21,900 Johnston: 8,900 Important Events & Figures Important Events Outcome Confederate Victory Battle Of Bull Run Casualties Union: 2,700 Confederate: 2,000 View Details Of Bull Run Casualties Battle Of Bull Run Pictures Battle Of Bull Run Images, Pictures and Photos See our Battle Of Bull Run Pictures Battle Of Bull Run Maps See our Battle Of Bull Run Maps Battle Of Bull Run Articles Explore articles from the History Net archives about the Battle Of Bull Run » See all Battle Of Bull Run Articles Battle Of Bull Run Summary: The First Battle of Bull Run (Manassas) was the first major land-based confrontation of the American Civil War. The Union army commander in Washington, Brigadier General Irvin McDowell, gave in to great pressure to begin campaigning before his men's 90-day enlistments expired, although he did not feel the army was adequately trained yet, leading to a stunning Confederate victory and ending northern hopes of a quick end to the war. Amassing An Army At Manassas On July 16, 1861, he set out with the Army of Northeastern Virginia, about 28,400 men, from Washington to attack the Confederate forces near Manassas, Virginia, just 25 miles away, and push them farther from the Northern capital. Brigadier General P.G.T. Beauregard had been massing the Confederate Army of the Potomac at Manassas since the spring of 1861 and had about 21,800 soldiers at hand. He was protecting a key rail station at Manassas Junction and had fanned his troops out along Bull Run north of town. General Joseph E. Johnston, though senior in rank to Beauregard, arrived from the Shenandoah Valley not long before the battle but being unfamiliar with the terrain, deferred to Beauregard. (Army names of the Civil War can be confusing. Not long after this battle, the largest Union army was re-named the Army of the Potomac, and after the Seven Days Battles, Robert E. Lee permanently re-named the largest Confederate army the Army of Northern Virginia.) The Battle Of Bull Run Begins After arriving in the Manassas vicinity on July 18, Brig. Gen. Daniel Tyler's Union division probed Bull Run and engaged in a skirmish at Blackburn's Ford. After losing about 150 men, Tyler withdrew. With information from additional reconnaissance, McDowell planned to use two columns to attack the Confederates left flank while a third circled to the far right flank and south to provide a distraction, cut the Confederates off from Richmond, and force them farther southeast. McDowell's plan counted in part on Union major general Robert Patterson's troops preventing General Joseph Johnston's Army of the Shenandoah in Winchester from providing reinforcements to Beauregard, ensuring the Union would outnumber the Confederates at Manassas. Unbeknownst to McDowell, on the morning of July 20, Johnston's troops had begun boarding the railroad at Piedmont Station in a steady stream to reinforce Beauregard; Patterson had waited too long to engage them. Very early in the morning on July 21, McDowell sent two divisions north toward Sudley Springs, while another division was to create a diversion by attempting to cross Bull Run at Stone Bridge. Confederate colonel Nathan Evans suspected the attack at Stone Bridge was just a diversion to conceal a larger movement and, upon receiving confirmation of this from his signal officer, redirected most of his men to Matthews Hill. They were able to slow down the Union divisions advancing from the north, but by midday they were being driven back toward Henry Hill, across the road behind them. General Thomas Jackson At Bull Run Brigadier General Thomas Jackson's Virginia brigade, Col. Wade Hampton and his Legion, and Col. J.E.B. Stuart's cavalry arrived at Henry Hill to reinforce the Confederates. Jackson set up a line of artillery on the crest of the hill, where the artillerymen would be protected. McDowell moved his artillery from Dogan Ridge to Matthews Hill, and the contesting batteries engaged in a fierce fight, during which a civilian was wounded; Mrs. Judith Henry, ill and in bed in her home on Henry Hill, would succumb to her wounds later in the day. Near the end of the artillery fight, Brig. Gen. Barnard Bee, on Henry Hill with Jackson, declared that the Union was about to break their lines, to which Jackson replied "Then we shall give them the bayonet!" To rally his troops—or perhaps in anger at Jackson coming to his aide too slowly—Bee exclaimed "Look at Jackson standing like a stonewall," bestowing the nickname "Stonewall" on Jackson. In a show of persistence and bravery, Jackson's brigade charged the Union artillery line, overran it, captured guns, and changed the course of the battle. Jackson continued to press against the Union lines, telling his men to wait until they were within 50 yards of the enemy before attacking, "And when you charge, yell like furies!" Confederate troops gave the first Rebel yell of the war. The seesaw battle was still in doubt as the afternoon wore on. When Beauregard saw a column of dusty troops coming up the road, he was uncertain which side they belonged to. The flag of both sides used red and white stripes and blue upper left corner; he couldn't be certain which flag the newcomers carried, and he contemplated ordering a retreat. Then a wind sprang up, and the Confederate national flag unfurled; Johnston's men had arrived. The Rout Of Union Troops The Union troops, attacked unexpectedly by these new arrivals, fled in a disorganized retreat that turned into a rout. The Confederates, who were also disorganized, did not pursue. The Southerners lost nearly 2,000 men, the Northerners over 2,700, over 1,200 of whom were taken prisoner or otherwise missing. In recognition of this victory, Beauregard was promoted to full general on August 31, to rank retroactively to the day of the battle. Aftermath of The Battle of Bull Run The ferocity of the fighting and the number of casualties at First Bull Run were a wake-up call to both sides. Five days after the battle, McDowell, blamed for the defeat, was replaced by Major General George B. McClellan as Union army leader. Johnston's Army of the Shenandoah and Beauregard's Army of the Potomac were combined. Not long after Gen. Robert E. Lee was placed in command of the combined force, he permanently renamed it the Army of Northern Virginia. The flag incident showed the need for a more readily identifiable Southern banner. The "Southern Cross" design, a blue X with white stars on a red banner, the flag most commonly associated with the Confederacy, was adopted as a battle flag, although it was used more in the Eastern Theater than in the West. Banner image Bull Run, Va. Federal cavalry at Sudley Ford, created by George N. Barnard, Library of Congress. Articles Featuring First Battle Of Bull Run From History Net Magazines Featured Article First Battle of Bull Run: The U.S Marines As Marine Major John G. Reynolds marched his battalion over the Potomac Long Bridge on the afternoon of July 16, 1861, he must have wondered what lay ahead for his Marines. A Mexican War veteran, Reynolds had seen Marines serve with distinction in that war 14 years earlier, and now he fully expected his command to do the same. Still, as an officer with 35 years of military service under his belt, Reynolds worried about the green troops under his command. True, they were Marines, but as they headed toward their first fight in a new war, across a small Virginia creek called Bull Run, he had some doubts that could only be answered when the bullets began to fly. The order to the commandant had been specific: 'You will be pleased to detail from the barracks four companies of eighty men each, the whole under the command of Major Reynolds with the necessary officers, noncommissioned officers and musicians for temporary field service under Brigadier General [Irvin] McDowell. The Marines were to join Union forces moving to oppose the Confederates positioned at Manassas, Va. From regiments of brand-new volunteers to U.S. Army regulars, every available Union soldier was being rushed toward the impending fray, and the Marines were no exception.One part of the Confederate Army had already occupied Manassas, a day's march of 26 miles from Washington, D.C. General P.G.T. Beauregard's Southerners, about 23,000 strong, were astride an important railroad junction and in position to threaten the capital itself. The remainder of the Confederate forces, 15,000 men under General Joseph E. Johnston, were in the vicinity of Harpers Ferry, 70 miles northwest of Manassas. The Confederate Army, split as it was into two separate wings, seemingly invited attack, and the Union commander was being pressured from all sides to take quick and decisive action. McDowell needed to act quickly to defeat the divided Confederates while he still commanded an army. Many of the 90-day Union volunteer regiments in his army, called into service in response to Confederate seizure of Fort Sumter two months earlier, were nearing the end of their enlistments, and many of the new replacement regiments were not yet combat-ready. Nevertheless, recognizing the need for urgency, the Lincoln administration rushed additional reinforcements to McDowell from all parts of the Union. Raw young recruits from New York, New England, Michigan, Wisconsin, Pennsylvania and Minnesota poured into Washington, camping in a sea of white tents visible in every direction from the Capitol dome. The arrival of new troops in Washington reflected the growing sense of panic within both the government and the Union Army. With a teeming Rebel army mere miles away, an understandable sense of urgency gripped the president, his cabinet and U.S. Army General-in-Chief Winfield Scott, an aging hero of the Mexican War. Scott, in particular, was obsessed with protecting the capital, to the point of resisting McDowell's plan for taking the war to the Confederates in Virginia and advising instead that he consider making a large-scale assault down the Mississippi River to split the Confederacy in two. McDowell, in turn, refused to consider Scott's counter suggestion, and Lincoln eventually intervened, ordering flatly that the Union Army take the offensive-immediately. Under direct orders from the president, McDowell drew up a plan for dividing his army into three columns to converge on the Rebels from three different directions north of Manassas. The plan was a good one, but it required at least twice as many men as McDowell then had at hand-hence the tumultuous influx of new recruits in Washington. The Marine Corps of 1861 reflected the turmoil of the times. Its 48 officers and 2,338 enlisted men had a wide range of experience levels, from aging veterans to raw recruits. Having grown by 25 percent between 1860 and 1861, the Corps swelled once again as the Civil War started. Indeed, the influx was so rapid that new troops at the Washington Navy Yard had to be berthed in the stables. Since many veteran Marines still served aboard ships or were deployed at U.S. shore installations throughout the world, few experienced Marines remained in Washington to help preserve the Union. As a result, untested new recruits filled out the ranks of Reynold's force. The Civil War influenced the number and quality of Marine officers available for duty, as well. Although the total number of Marine officers remained essentially the same after the war started, the experience level of the officers declined. Twenty Marine officers resigned from service, electing to join the Confederacy in the spring of 1861. More critically, among the ranks from first lieutenant to major, nearly half of the officers headed South. Thus it was that half of Reynolds' Marine officers marching to face a hostile enemy had been commissioned within the previous two months. It took time for the new Marine units to be integrated, however hastily, into the Army, and in the meantime, Lincoln, Scott and the cabinet members fretted. The target date for the offensive, July 8, passed without a whimper. McDowell, sitting in his camp, complained to his staff that he had no opportunity to test my machinery, to move it around and see whether it would work smoothly or not. Unfortunately for McDowell, he did not have the luxury of a test run. Scott, fuming at the delay, told Brig. Gen. Daniel Tyler that there was no excuse for an unfortunate result in the upcoming campaign, since McDowell had superior numbers and equipment in his favor. McDowell, however, did not see it that way. I wanted very much a little time, he said later, all of us wanted it. We did not have a bit of it. The answer was: 'You are green, it is true; but they are green, also; you are all green alike.' Finally, on July 15, final orders were given for an advance the next day. Brigadier General Samuel Heintzelman would lead one wing of the army down the Orange & Alexandria Railroad to a point below the Confederate army at Manassas. Brigadier General David Hunter would head toward Centreville, a village directly northeast of Bull Run. And Tyler would head for Vienna, where he would proceed west to block the Little River Turnpike and the Rebel line of retreat. On to Richmond! was the informal, if overly optimistic, watchword of the campaign. Reynolds formed his men into a battalion and trooped out from the Washington Navy Yard with the rest of the army on July 16. In addition to four companies of 320 privates, the battalion included 12 officers, 17 noncommissioned officers, two drummers and two fifers. None of the privates had been in the service for more than three weeks, and only 16 Marines had had significant experience. Still, the leaders were seasoned Marines. Besides Reynolds' 35 years of service, his second-in-command, Major Jacob Zeilin, had been in service for 30 years. Of the remainder, only three other officers, nine noncommissioned officers and two musicians could be considered veterans. The Marines were attached to Colonel Andrew Porter's brigade of the 2nd Division. The brigade numbered 3,700 men and included a militia regiment from New York, the 8th New York; two volunteer regiments, the 14th and the 27th New York; a battalion of Army regulars; a cavalry detachment; and an artillery battery from the 5th U.S. Artillery under Captain Charles Griffin. Porter recognized only too well the rawness of the Marines, but still complimented them: Through the constant exertions of their officers [they] had been brought to present a fine military appearance. He assigned Reynolds' battalion to support Griffin's battery. By accompanying the artillery, Griffin reasoned, the Marines might be shielded from the heaviest fire, and their inexperience might not become a factor. The Union army's march toward Manassas took twice as long as expected, with one day of travel lost when field rations were not packed as ordered. It was not until July 21 that the Federals arrived near Centreville, where the Confederates guarded the lower crossings of Bull Run. McDowell's plan was to make a feint against the Confederate positions on Bull Run with half of his army while marching the remainder of his force upstream. There, at a ford near Sudley Springs, the soldiers would cross Bull Run, presumably turning the flank of the preoccupied Confederates. The Marines and the rest of Porter's brigade would be a part of the Union flanking force. Speed was the key to the successful execution of McDowell's complex plan. Unfortunately, in 1861 there were few units in the green Union Army that could move fast enough to make it work without a total loss of organization. Still, that was the plan, and the soldiers moved out as rapidly as possible, some of them singing Dixie as they moved into Virginia.Porter's brigade and the Marines did their part, falling into marching order at 2 a.m. Despite the early start, the marchers suffered immediate delays and could not keep to the attack timetable. Columns became hopelessly disorganized, mixing with other units on the road and becoming confused in the dark; other regiments lost their way completely. It was not until midmorning on the 21st that the first Union elements actually crossed Bull Run. Even as the congestion cleared, the Marines discovered another challenge to their march. Griffin's battery contained six horse-drawn cannons that raced ahead of the brigade whenever they could to make up time. In Reynolds' own words, The battery's accelerated march was such as to keep my command more or less in double-quick time; consequently the men became fatigued or exhausted in strength. Double-quicking their way through the dark, the Marines lost much of their freshness, particularly as the morning turned into a typically hot July day in Virginia. Once the Marines crossed Bull Run, they rested half an hour while the remainder of Porter's brigade caught up. Meanwhile, the first Union brigade to cross the creek, Brig. Gen. Ambrose Burnside's, advanced toward the Confederate flank near Sudley Ford. Only a part of Colonel Nathan Shanks Evans' Confederate brigade protected the left side of Beauregard's army against the initial Union onslaught. McDowell's plan just might work. Burnside's men engaged the Confederate line while Porter moved his men up on Burnside's right. Griffin's battery pushed ahead of Porter's column, followed closely by the Marines, and started firing from a range of 1,000 yards. Griffin's six guns, four rifled 10-pounders and two howitzers, quickly silenced a Confederate battery and then continued forward, advancing to the right as the Confederates started to yield under the intense pressure. Griffin's artillery again aggressively pushed forward, with the Marines close behind, while Burnside's brigade forced the Confederates back. Still, the Confederate lines responded with intense and deadly fire as more Southern troops arrived to defend the flank. Burnside's men, tiring after several hours of vicious combat, began to lose their initiative, faltering and withdrawing on their own to replenish their ammunition. The majority of Burnside's brigade retreated to Sudley Ford and were no longer a factor on the battlefield. Porter's men, however reluctantly, now were in the Union front. Although one of Porter's regiments followed Burnside's exodus to the rear, the remainder of the brigade, including Griffin's guns and the Marines, continued to fight ferociously. Additional Union brigades began to fill gaps as the battle progressed into the afternoon and the Confederate lines shifted about a mile to the rear. The Union army, despite its late start and early confusion, surged onward, ready to carry the day. Confederate resistance stiffened, however, as reinforcements from Johnston's force arrived from Harpers Ferry by train. As they climbed down from the train, Beauregard hastily sent the new forces forward to bolster his left flank. Eventually the Confederates, under the implacable leadership of Brig. Gen. Thomas J. Jackson, rallied on Henry House Hill in a stubborn defense that earned him the nickname Stonewall, and the hill became the focal point of the battle. At 2 p.m. Griffin's battery and a second artillery battery, under Captain J.B. Ricketts, were ordered to occupy Henry House Hill, supported by infantry and Marines. Griffin's cannons were not well to the front, but they fired harmlessly over the Confederates, who were sheltered on the opposite side of the hill. Meanwhile, the Union artillery was suffering under heavy Confederate return fire. The Marines covering the artillery were exposed to the same concentrated fire and rapidly began losing men of their own. Still, they remained in support of Griffin's battery as the action grew hotter around Henry House Hill. As the fighting continued, an infantry regiment was observed closing in on Griffin's guns. At the same time, the 14th New York Infantry was moving up to protect the battery's flank, and the unknown regiment was thought to be a similar reinforcement, but from a different Union division. Griffin wanted to open fire on the blue-uniformed force, but major William F. Barry, McDowell's artillery chief, ordered him to hold his fire. Suddenly, murderous fire of musketry and rifles erupted against Griffin's men. The supposed reinforcements were the 33rd Virginia, whose commander, Colonel Arthur Cummings, had disobeyed an order from Jackson that he hold his position. Still wearing blue uniforms from prewar days, the 33rd Virginia was able to advance to within 70 yards of the Union right flank. The casualties they inflicted on the Marines were the first confirmation of their hostile intent. The Union force resisted for a short time, but the surprise attack was devastating. Both artillery batteries suffered severely, bearing the brunt of the attack, and Griffin desperately attempted to remove his guns. But the Confederates came storming on, swarming over his artillery and supporting infantry. The 14th and 27th New York regiments broke and fled, followed by other supporting regiments. The Marines, in spite of the efforts of their officers, broke as well. Without support, Griffin's battery was overrun, losing a quarter of its men and half of its horses. That was the last of us, Griffin reported later; we were all cut down. Reynolds managed to rally his Marines, and they returned to the fight. Again, however, the Confederate pressure was overwhelming, swamping the support troops and the Marines who still continued to fight. One Marine officer was killed and Major Zeilin was wounded, but action continued around the artillery battery. In all, possession of Griffin's guns was contested three times. Finally, at 4 p.m., the Marines broke one last time, contributing to the general route of the Union army. The First Battle of Bull Run (or First Manassas) had ended with a surprising Confederate victory and a humiliating Union flight. Eventually, however, most of the Marines managed to return to Washington. Reynolds caught up with the largest group of the battalion, about 70 Marines, during the withdrawal. Overall, his battalion lost nine Marines killed, 19 wounded and six missing. Reynolds gamely praised his men for their good conduct, considering their lack of combat experience, but it was an unavoidable fact that his young Marines had fled the battlefield. The Marine commandant would later report to the secretary of the navy that it is the first instance recorded in its history where any portion of [the Corps'] members turned their backs to the enemy. Historians would characterize the Marines' performance at Bull Run as a dismal-and atypical-example of battlefield panic. Surprisingly, there were no recriminations against the Marines at the time. Reynolds was promoted to lieutenant colonel a few days later and continued to serve throughout the war. One of his officers, 2nd Lt. Robert Huntington, would not only fight in other theaters in the Civil War but also would lead the attack to capture Guantanamo Bay, Cuba, in the Spanish-American War, 37 years later. Major Jacob Zeilin recovered from his wound at Bull Run and commanded the Marines during joint action against Charleston, S.C., later in the war. In 1864, he was selected as the seventh commandant of the Marine Corps, promoted ahead of four more senior Marines, including Reynolds. In 1867, Zeilin was promoted to brigadier general, becoming the first Marine to hold that rank. Although not a proud moment in Marine Corps history, the performance at Bull Run had extenuating circumstances. Three weeks of training were clearly not enough. Reynolds reported that his Marines had barely learned facing movements before they marched off to war. Although the 90-day volunteer regiments were not veterans either, even they had trained longer than three weeks. As a result, the Marines were probably the least experienced soldiers in McDowell's entire amateur army at Bull Run. Despite their brief training time, the Marines' fighting effort at Bull Run compared favorably with that of other units. At the forefront of the battle, the Marines suffered losses that were comparable to the overall 18 percent casualty rate suffered by Porter's brigade at Bull Run. Marine casualties were also nearly equal to those of the Regular Army battalion, then the most experienced unit in the Union Army, and were greater than many of the larger infantry regiments in the battle. Overall, the Marine's performance in action equaled the majority of the other units in Porter's brigade. Because of their inexperience, the Marines had been assigned as an artillery escort, supposedly to remain behind the lines. Ironically, this secure assignment led to their repeated involvement in the most severe action of the battle. Although the dismal outcome of the battle may not have been what Major Reynolds had anticipated only five days before, his own Marine battalion had fought hard and well on the shores of Bull Run. He could take some pride, at least, in that. This article was written by Kendall J. 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Guide to Materials on Latin America in the National Archives of the United States
AUTHOR: Ulibarri, George S. and Harrison, John P.
PUBLISHER: National Archives & Records Administration
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