Common tools for generating results and visualizations for bioinformatics and genomics publications

Question

I am a computational scientist with background in computer science and engineering however very new to the field of bioinformatics and computational genomics. Looking at papers in nature, PLOS, Cell, PNAS etc. within the topic of computational genomics, I see a very similar set of very sophisticated graphical results, ranging from clustered/sorted heat map representation of gene expressions to log2 fold changes, volcano plots, circular graph network visualizations etc. Coming from an engineering background I would often generate customized tools for visualizing results myself as part of each paper, albeit not at the sophistication level of results I see in bioinformatics. But here, it seems there is a set of tools standardized where many are using them to display their results. Is there a summary of these result generation/visualization tools along with the references to their implementation/usage (opensource I assume)? Something like that could be very helpful to newcomers like myself. Thanks in advance.

Below is an example /elaboration based to guide the question further into specifics:

Consider the example paper:

https://doi.org/10.1016/j.cell.2018.04.018

This paper contains a principle set of results/visualizations I see almost universally in many nature/cell computational genomics papers. I am wondering if there exists an integrated library either in python, R (or Matlab for example when they create toolboxes and integrate a commonly used subset of tools in an area) that everyone taps into for generating these type of results. The "principal set" that I am referring to include:

1. Genome browser track snippets: Figure 1A
2. log2Fold x-y scatter plots : Figure 1B
3. Hierarchically clustered/sorted gene exp heatmaps: Figure 1C,1F
4. Venn diagrams : Figure 4F
5. Hi-C type square ligation heat-maps: Figure 6A

Answer 1

Figure 1A: These genome browser snippets are either plain screenshots from applications like IGV or created from within R with software like gviz.

Figure 1B: This looks like base R plotting routine. Like plot(x,y) and then some coloring and legending. No particular "tool" here other than really just base R or ggplot2.

Figure 1C/F: Heatmap packages include ComplexHeatmap and pheatmap. I personally use the former as it is versatile with extensive documentation.

Figure 4F: Venn diagrams, just google "Venn" and "R" for several packages that do it in R. Same for other languages. There are plenty of packages. No preference.

Figure 6A: These plots are often returned by specialized HiC software such as HiCexplorer which is powerful with lots of documentation.