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My experience with it has been very positive when using it to put together a presentation, but when it comes to the actual output, PowerPoint falters. When hooked up to a projector, the preview pane on the computer that is being used in my case a current model MacBook Pro running OS X When using WebEx, the attendees see the slide show, but are 3 to 4 slides behind the current slide being discussed. Don't buy this if you need to do presentations. Wait until Microsoft releases a few updates. Works great for putting together presentations. Doesn't work for an actual presentation.

I'd have to give it a 2 star rating overall because it doesn't do what it is intended to do. What do you think about Microsoft Powerpoint ? Do you recommend it? With built-in ad blocker, battery saver, Messenger and extensions. Almost ready. To start the journey with Opera. Run the downloaded file and perform installation. It's so easy to use, that you can create a presentation from scratch without any View full description.

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All the data sets used in this section are available online as part of the tutorial, which presents step by step instructions of example applications. At the global genomic level, the distribution of protein binding with respect to known features provides a fundamental description of binding behavior. PAPST analyzes peak binding in the promoter, gene body, exon, intron, and Intergenic regions quickly with a single mouse click.

The summary distribution information is reported as a percentage of total peaks as well as the number of significant peaks that fall into the genomic region. The results using bp promoter are shown in Fig 1B. Most analysis packages use a one-size-fits-all promoter size while PAPST allows the user to interactively consider multiple settings. Many TFs drive gene regulation by interacting with the upstream promoter of target genes.

Although the current understanding of gene regulation has evolved to include distal TF-to-target gene regulation, assignment to the nearest TSS provides the baseline preliminary analysis that offers insight to TF behavior [ 16 ]. This feature is common to nearly all ChIP-Seq analysis packages. PAPST allows any result table generated to be exported to a spreadsheet format. The complete gene assignment for Oct4 peaks is provided in S1 Table. PAPST also offers batch gene assignment functionality that will calculate the gene assignments for multiple peak sets and place the results into separate files.

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Labs studying specific TFs are typically interested in discovering which genes could be direct regulatory targets. PAPST offers customizable search capabilities that allow users to quickly determine which genes have particular TF binding patterns. The powerful filters feature allows users to create sets of criteria for returning genes of interest. A simple but common task would be to find all genes where a specific TF binds in the promoter.

To accomplish this, a PAPST user would simply create a filter, select the promoter option modify the length if desired , and press the search button. The S2 Table contains the search results for genes having Oct4 in their upstream promoters. A sample of these search results is shown in Fig 1D. The genes are placed in a sortable table in the results window allowing the user to prioritize genes based on total tag coverage.

The above example represents the most basic application of the search features. A natural extension to the promoter search example described above would be to include the activating histone mark H3K4me3 and filter out lower confidence peaks by tag count. To accomplish this a second filter is added to the search that operates on significant peaks derived from an H3K4me3 ChIP-Seq experiment, and the threshold of the Oct4 filter is adjusted to filter out peaks with fewer than 20 reads.

PAPST will process all filters and return only genes that meet all the specified criteria those with both Oct4 and H3K4me3 binding at the promoter and at least 20 reads for the Oct4 peaks. Fig 1E shows a sample of the results returned using these search criteria the complete list is available in S3 Table. Creative application of an arbitrary number of peaks and filters provides the user with a robust means of specifying complex regulatory and epigenetic patterns to select interesting genes for further analysis.

This powerful feature, unique to PAPST, generalizes the multiple criteria based search function to any set of genomic regions enabling peak-centric co-localization analysis. In the previous example, a search was constructed to find genes that met multiple criteria across different experiments. This feature allows complex search criteria to be applied to any set of arbitrary genomic regions of interest. As the current understanding of gene regulation evolves, gene-distal regulatory regions will become increasingly important.

PAPST provides easy to use tools to explore protein co-localization patterns at any user-defined set of genomic regions. The Example Applications section below describes a case study that utilizes this feature to detect enhancers. Another example of peak-based search features is presented in the tutorial at https: In addition, we have also implemented specific features to increase its usability and enhance its ability for efficient data handling.

To demonstrate its efficient performance and ease of use, we have timed a complete analysis session in which we loaded Oct4, Sox2, and Nanog peaks, determined regions of co-localized binding, loaded the literature derived normal and super enhancer regions [ 16 ], and calculated their agreement with PAPST derived regions in terms of overlapping peaks.

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PAPST completed the entire 7-step analysis session within 19 seconds wall clock time , from data loading to result generation. The detailed data are given in Table 1 , including the timings for each specific step and the number of mouse clicks needed. The following sections describe case applications of PAPST, with pubic ChIP-Seq datasets, to demonstrate its typical uses and its great potential in cutting-edge research.

The results as compared to those published also validate the algorithms and approaches specifically implemented in PAPST. ESC enhancers are characterized by the co-occupancy of master transcription factors Oct4, Sox2, and Nanog [ 17 ]. The enhancers were identified through the calculation of genomic regions co-localized by Oct4, Sox2, and Nanog. The gene-centric co-localization analysis can be particularly useful to address specific questions related to gene regulation see the tutorials for more examples.

PAPST can generate quantitative data for extended co-localization analysis.

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ESC super enhancers have been shown to have higher levels of active enhancer epigenetic mark H3K27Ac and binding of key TFs as compared to typical enhancers [ 16 ]. The comparative results are shown in Fig 3 , which indicate significantly higher levels of these key factors in the super enhancer associated peaks than those associated with the typical enhancers p-values are: Oct4 2. We also used PAPST to quickly generate the co-localization data showing a significantly higher percentage of super enhancers are occupied by H3K27Ac and Med1 compared to typical enhancers Fig 4. In these rapid applications of PAPST see the Performance and Usability section above for the timings , co-localized peaks are not only easily identified, but they can also be investigated quantitatively.

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Peak signals are expressed as normalized read counts. The complete assignment results are presented in the supplemental materials S5 Table. PAPST was compared to two others gene assignments for super enhancer regions. The discrepancy with the Hnisz et al. PAPST has implemented a novel algorithm for symmetric peak overlap calculation, determining the total number of overlapping peaks for all possible pairwise comparisons of TFs and EMs included in an analysis, with one single mouse click see Methods for details.

The symmetric overlapping relationships table may then be used for binding profile based clustering. The results were used to cluster these factors see S1 Appendix for details. The clustering patterns obtained with PAPST generated data Fig 6 are virtually identical to those published, which were created with a different set of mostly command-line based tools and algorithms [ 17 ].

The heatmap shows the association of the 3 key reprogramming factors Oct4, Sox2, and Nanog. These groupings closely match those of Chen et al. Our results, which were obtained with PAPST on the scale of seconds, are highly consistent with those presented in the original research publications, validating the specific computational algorithms implemented in PAPST.

PAPST has been developed based on our extensive experience in ChIP-Seq data analysis [ 18 — 21 ], which has helped us to realize the need for an easy to use yet powerful software tool for post peak-calling ChIP-Seq data analysis that is more accessible to the researchers in the field, including bench scientists. And as such, PAPST has been designed to offer a focused set of powerful and convenient features that are very easy to use for researchers with or without computational expertise.

A co-localization calculation of 50 factors took only 1. As most functions of PAPST may be completed basically as fast as one can click the mouse Table 1 , and the analysis results can be easily reformatted into an input file within the package for a new round of analysis, PAPST can serve as a powerful and efficient tool for data-driven exploratory research.

This is particularly true when combined with the flexible feature of PAPST that allows easy and highly customizable parameter adjustment. PAPST offers both gene centric and peak centric features in a single package, capable of analyzing ChIP-Seq peaks both on gene-defined genomic regions promoters, exons, introns, gene bodies and on peak-defined genomic regions, making it a true genome-wide data analysis platform.

Its peak-centric feature can be creatively and easily extended to include the co-localization analysis of TFs and EMs on any set of user-defined genomic regions of interest, such as the ENCODE determined locations of regulatory sequences [ 22 ], regions of evolutionary conservation [ 23 ], or non-coding RNAs [ 24 ].

Those co-localized regions identified by PAPST can either be used for extended co-localization analysis or for further downstream analysis such as motif and composition analysis.

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  • In addition, as the core analysis performed by PAPST is genomic interval based, it may also potentially find its applications in any genome data analysis that involves genomic interval based calculations. Wer surft auf Ihrer Leitung mit? Google verschenkt Datei-Manager: Samsung-Browser beliebter als Firefox: Netflix-Fans werden es lieben: Schutz gegen Webcam-Spionage: Ministerium bietet Gratis-Aufkleber an Testbericht. Download-Ticker Best of Downloads.

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