3

gggenes is a ggplot2 based solution and looks very appealing. Plus, it has a nice introduction with examples. library(ggplot2) library(gggenes) ggplot(example_genes, aes(xmin = start, xmax = end, y = molecule, fill = gene)) + geom_gene_arrow() + facet_wrap(~ molecule, scales = "free", ncol = 1) + scale_fill_brewer(palette = "Set3") +...


3

Many thanks for raising this issue. The web service is now working. Only the glyph related web service calls were affected due to restructuring folders at the SBOL-Visual GitHub repository. I now revived the glyphs using the sbol-visual-ontology GitHub repository. Over the summer, we will develop the web service further so that you can request different ...


2

I think one of the reasons you struggle is that clustering DNA sequences is not a clearly defined task. In general intention of clustering is to reconstruct, or approximate the relatidness of the DNA seqeunces. If all these sequences are homologous you can do a multiple sequence alignment (using MAFFT or Clustal) and build a phylogenetic tree (using ...


2

The length of the bars and their placement on the x-axis represents the length of the peptide and mapping to the source protein, respectively. The coloring represents the average difference in peptide MS intensity between the control and two experiments.


2

So you can install PyMOL as a standalone, but you can also install it as a bona fide Python 3 module via conda: conda install -c schrodinger -c conda-forge pymol-bundle It can be installed in other ways —apt-get or brew or even compiled, but the latter is excruciatingly painful. In your python notebook you can do: import pymol2 with pymol2.PyMOL() as pymol: ...


1

It is a glitch in PyMOL. Caveat. I would say that, whereas cmd.label is great for adding labels en-mass for internal figures, it is not great for figures for dissemination (which require few strategically placed labels possibly with a faint white outer glow —cf. your D330): most figures in papers I would say are Powerpoint or Photoshop labelled. You called ...


1

Pseudotime is the order of cells along a trajectory which in the simplest case is a minimum-spanning tree based on a (low-dimensional) manifold such as PCA or UMAP. Without a defined trajectory you have no pseudotime, and therefore UMAP is not pseudotime, but in fact can be used to define the trajectory.


1

It seems like the ggbio object already contains the corresponding ggplot object. You should be able to extract the ggplot from the ggbio_obj@ggplot slot and extract your gtables. Small example: library(ggbio) library(ggplot2) library(patchwork) fl.bam <- system.file("extdata", "wg-brca1.sorted.bam", package = "biovizBase") ...


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