Address of the bookmark: https://bioinformaticsworkbook.org/dataAnalysis/GenomeAssembly/GeneticMaps/creating-genetic-maps.html

1. Heatmaps: Exploring Patterns in High-Dimensional Data

Heatmaps are a go-to visualization for representing high-dimensional datasets, such as gene expression or metabolomics data. They use color gradients to display data intensity, making patterns and clusters easily detectable.

• Applications: Gene expression analysis, pathway enrichment, methylation studies.

• Tools: Seaborn (Python), ComplexHeatmap (R), Morpheus (web-based).

Tip: Add dendrograms to visualize clustering of rows and columns for hierarchical relationships.

2. Volcano Plots: Highlighting Differential Features

Volcano plots are indispensable for identifying significantly differentially expressed genes or proteins. They plot the log2 fold change against –log10(p-value), making it easy to spot statistically significant changes.

• Applications: RNA-seq, proteomics, and metabolomics.

• Tools: ggplot2 (R), EnhancedVolcano (R), Plotly (Python).

Tip: Use color to highlight significant features and label key genes or proteins.

3. PCA Plots: Reducing Complexity with Principal Component Analysis

Principal Component Analysis (PCA) plots are used to reduce dimensionality and uncover trends or clusters in data. They provide insights into sample variability and grouping.

• Applications: Transcriptomics, metabolomics, microbiome studies.

• Tools: scikit-learn + Matplotlib (Python), prcomp (R), ClustVis (web-based).

Tip: Annotate clusters with metadata to enhance interpretability.

4. Manhattan Plots: Genome-Wide Association Studies

Manhattan plots visualize p-values across the genome, making it easy to identify significant associations in genome-wide studies. They resemble city skylines, with the highest peaks indicating loci of interest.

• Applications: GWAS, QTL mapping.

• Tools: qqman (R), Matplotlib (Python).

Tip: Use alternating colors for chromosomes and highlight significant SNPs for clarity.

5. Circular Plots (Circos): Visualizing Genomic Relationships

Circular plots are ideal for visualizing relationships across the genome, such as structural variations, gene duplications, or synteny.

• Applications: Comparative genomics, structural variation studies.

• Tools: Circos (standalone), Rcircos (R), pyCircos (Python).

Tip: Keep the plot clean and avoid overcrowding to maintain readability.

6. Sankey Diagrams: Tracking Data Flows

Sankey diagrams visualize flows or relationships between categories, often used to track changes in gene expression or pathway enrichment across conditions.

• Applications: Pathway analysis, gene set enrichment analysis.

• Tools: Plotly (Python), networkD3 (R).

Tip: Use gradients or distinct colors to highlight key transitions.

7. Network Graphs: Mapping Interactions

Network graphs represent relationships between entities, such as protein-protein interactions or gene regulatory networks. Nodes represent entities, and edges represent relationships.

• Applications: Systems biology, interactomics.

• Tools: Cytoscape (standalone), igraph (R), NetworkX (Python).

Tip: Use edge thickness or node size to represent interaction strength or centrality.

8. Violin Plots: Visualizing Data Distribution

Violin plots combine a boxplot with a density plot, showing the distribution and variability of data.

• Applications: Single-cell RNA-seq, quantitative trait analysis.

• Tools: Seaborn (Python), ggplot2 (R).

Tip: Split violins by groups for side-by-side comparisons.

9. Time-Series Plots: Monitoring Changes Over Time

Time-series plots display changes in variables across time points, useful for tracking gene expression dynamics or metabolic fluxes.

• Applications: Time-course experiments, cell cycle studies.

• Tools: Matplotlib (Python), ggplot2 (R).

Tip: Smooth the data to highlight trends while avoiding overfitting.

10. Genome Tracks: Visualizing Genomic Features

Genome tracks display multiple layers of genomic data, such as gene annotations, sequencing coverage, and epigenetic marks.

• Applications: ChIP-seq, ATAC-seq, whole-genome sequencing.

• Tools: IGV (standalone), pyGenomeTracks (Python).

Tip: Stack related tracks for direct comparisons.

11. UpSet Plots: Visualizing Set Intersections

UpSet plots are a powerful alternative to Venn diagrams for visualizing intersections between multiple datasets.

• Applications: Overlap analysis for gene sets, pathways, or variants.

• Tools: UpSetR (R), ComplexUpset (Python).

Tip: Use bar plots to represent the size of each intersection for added clarity.

12. Ridge Plots: Comparing Distributions

Ridge plots visualize the distributions of multiple datasets, stacked for easy comparison.

• Applications: Transcriptomics, single-cell RNA-seq.

• Tools: ggridges (R), Matplotlib (Python).

Tip: Use transparency and consistent scaling for better readability.

13. Chord Diagrams: Visualizing Connections Between Groups

Chord diagrams illustrate relationships between categories, such as shared genes between pathways or overlaps in regulatory elements.

• Applications: Pathway overlap, synteny, co-expression networks.

• Tools: Circlize (R), Holoviews (Python).

Tip: Use distinct colors for each group to emphasize relationships.

14. Treemaps: Hierarchical Data Representation

Treemaps visualize hierarchical data as nested rectangles, with area proportional to data size.

• Applications: Ontology enrichment, pathway analysis.

• Tools: Treemapify (R), Plotly (Python).

Tip: Use colors to represent additional variables, like significance or enrichment scores.

15. T-SNE/UMAP Plots: Dimensionality Reduction for Clustering

T-SNE and UMAP plots are great for visualizing high-dimensional data in two dimensions while preserving local or global structure.

• Applications: Single-cell transcriptomics, clustering analyses.

• Tools: scikit-learn (Python), Seurat (R).

Tip: Combine with metadata annotations for better cluster interpretation.

Bringing It All Together

The choice of visualization can significantly impact the insights gained from bioinformatics data. By selecting plots tailored to your data type and analysis goals, you can effectively communicate your findings and make your research more impactful. Whether you’re a seasoned bioinformatician or a beginner, mastering these visualizations will elevate your analyses and presentations.

In fact, there are huge demands for expert biological data analyst. It’s a fairly new  (not exactly) hot area, these bioinformatician are invaluable because they know and understand the significance of biological data for your research and how you can use it for better understanding of biological problems.

The bioinformatics can discover biological patterns and stories in genomic and proteomics data. They can develop the pipeline needed to properly collect, store and analyse it.

Once your research group is ready to make a larger investment and hire a bioinformatician to gain a competitive edge, there are several key traits to seek out in potential candidates. The best bioinformatician are:

1. Highly Skilled - programming skills, experience with the biological software and tools.

The biological data won’t illuminate much if the scientist analysing it doesn’t possess practical programming skills, experience with the biological software and tools and a thorough understanding of basic biological stuff. A solid background in mathematics and statistics is also an indispensable trait.

2. Insight - Real vision, robust understanding and deep insight.

In order to hire the best bioinformatics and computational biologist scientist for your needs, it is always recommended and mostly practiced by the recruiters, to ask each contender to write and develop a sample script/presentation based on a specific set of data you provide. Then, explore the approaches used to deal with data provided and pick up those candidates who convey real vision, robust understanding and deep insight.

3. Energetic – Curiosity to explore

Mostly natural curiosity and enthusiasm for solving big biological problems coupled with an ability to transform data into a scientific stories may place one candidate above the rest. In addition to achieve that, the bioinformatician should be agile enough to quickly modify their methods to suit changes within a particular research.

4. Researcher – Publications

Look for someone who has a keen sense and understanding of concern biological problems. You can judge it by looking at previously published papers and data. It is always recommended to have a look at GitHub and other repository for codes written by her/him.

5. Impressive communicator - Insight that can’t be expressed is worthless.

Good bioinformatics scientists are able to uncover biological patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. In other words, they should must have good scientific writing skills. A computational biologis/bioinformatician  should know how to present the data and tell a scientific story through numbers/images.

https://bsapubs.onlinelibrary.wiley.com/doi/pdf/10.1002/aps3.1207

Address of the bookmark: http://eyechrom.com:3838/EyeChrom/

Address of the bookmark: http://sysomics.com/deephic

Address of the bookmark: https://www.encodeproject.org/data-standards/reference-sequences/

Use the Terminal or an Anaconda Prompt for the following steps.

1. To create an environment:

   conda create --name myenv
   

   NOTE: Replace myenv with the environment name.

2. When conda asks you to proceed, type y:

   proceed ([y]/n)?
   

This creates the myenv environment in /envs/. This environment uses the same version of Python that you are currently using, because you did not specify a version.

To create an environment with a specific version of Python:

conda create -n myenv python=3.4

To create an environment with a specific package:

conda create -n myenv scipy

OR:

conda create -n myenv python
conda install -n myenv scipy

To create an environment with a specific version of a package:

conda create -n myenv scipy=0.15.0

OR:

conda create -n myenv python
conda install -n myenv scipy=0.15.0

To create an environment with a specific version of Python and multiple packages:

conda create -n myenv python=3.4 scipy=0.15.0 astroid babel

TIP: Install all the programs that you want in this environment at the same time. Installing 1 program at a time can lead to dependency conflicts.

To automatically install pip or another program every time a new environment is created, add the default programs to the create_default_packages section of your .condarc configuration file. The default packages are installed every time you create a new environment. If you do not want the default packages installed in a particular environment, use the --no-default-packages flag:

conda create --no-default-packages -n myenv python

TIP: You can add much more to the conda create command. For details, run conda create --help.

➜ redundans git:(master) ✗ conda create --name py27 python=2.7
Solving environment: done

==> WARNING: A newer version of conda exists. <==
current version: 4.5.0
latest version: 4.5.2

Please update conda by running

$ conda update -n base conda

## Package Plan ##

environment location: /home/urbe/anaconda3/envs/py27

added / updated specs:
- python=2.7

The following packages will be downloaded:

package | build
---------------------------|-----------------
wheel-0.31.0 | py27_0 61 KB
python-2.7.15 | h1571d57_0 12.1 MB
certifi-2018.4.16 | py27_0 142 KB
sqlite-3.23.1 | he433501_0 1.5 MB
setuptools-39.1.0 | py27_0 582 KB
openssl-1.0.2o | h20670df_0 3.4 MB
pip-10.0.1 | py27_0 1.7 MB
ca-certificates-2018.03.07 | 0 124 KB
------------------------------------------------------------
Total: 19.6 MB

The following NEW packages will be INSTALLED:

ca-certificates: 2018.03.07-0
certifi: 2018.4.16-py27_0
libedit: 3.1-heed3624_0
libffi: 3.2.1-hd88cf55_4
libgcc-ng: 7.2.0-hdf63c60_3
libstdcxx-ng: 7.2.0-hdf63c60_3
ncurses: 6.0-h9df7e31_2
openssl: 1.0.2o-h20670df_0
pip: 10.0.1-py27_0
python: 2.7.15-h1571d57_0
readline: 7.0-ha6073c6_4
setuptools: 39.1.0-py27_0
sqlite: 3.23.1-he433501_0
tk: 8.6.7-hc745277_3
wheel: 0.31.0-py27_0
zlib: 1.2.11-ha838bed_2

Proceed ([y]/n)? y

Downloading and Extracting Packages
wheel 0.31.0: #################################################################################################################################################################################################### | 100%
python 2.7.15: ################################################################################################################################################################################################### | 100%
certifi 2018.4.16: ############################################################################################################################################################################################### | 100%
sqlite 3.23.1: ################################################################################################################################################################################################### | 100%
setuptools 39.1.0: ############################################################################################################################################################################################### | 100%
openssl 1.0.2o: ################################################################################################################################################################################################## | 100%
pip 10.0.1: ###################################################################################################################################################################################################### | 100%
ca-certificates 2018.03.07: ###################################################################################################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use:
# > source activate py27
#
# To deactivate an active environment, use:
# > source deactivate
#

➜ redundans git:(master) ✗ source activate py27

• While there are many other tree drawing programs, slacTree was originally written in 2009 to fill a need for producing publication quality figures of circular trees with more than 1000 taxa with custom annotations
• Because it is a single Perl script with very few dependencies, it is easy to run, and easy to further customize
• SVG is used because it is a scalable format allowing for very small representations of entire trees or highly magnified regions with unlimited resolution
• Circular and radial trees are more compact than linear representations

Address of the bookmark: https://github.com/mccrowjp/slacTree

Address of the bookmark: http://samstat.sourceforge.net/