Tutorial at http://evomics.org/learning/genomics/satsuma/

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

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

http://docpollard.org/

Tools

http://docpollard.org/resources/software/

https://bio7.org/about/

Address of the bookmark: https://bio7.org/home-2/

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.

We are looking for a highly motivated candidate with a PhD degree in
Bioinformatics or a related field. Candidates are expected to have a
strong background in evolutionary biology and/or comparative functional
genomics. Additional experiences in single cell functional genomics
analyses, statistics and computational data analyses are a plus, as is
an interest in comparative developmental (EvoDevo) questions.

We offer a dynamic and interactive research environment with state-of-the
art research facilities, good research funding and internationally
competitive salaries.

The Tschopp lab (www.evolution.unibas.ch/tschopp/research/)
studies the gene regulatory mechanisms of cell type
specification and evolution in vertebrates. See also our
preprints at https://doi.org/10.1101/2024.03.26.586769 and
https://doi.org/10.1101/2024.11.28.625862 Applications should include
a motivation letter, a CV, a list of publications, a statement about
research interests, as well as the names and contact details of at
least two referees. Applications (in the form of a single .pdf file)
should be sent to Patrick Tschopp (patrick.tschopp@unibas.ch); review
of applications will begin on January 1st 2025, and will continue until
the position is filled.

Patrick Tschopp

For more information visit: http://www.snubi.org/TBC2013/

The postdoctoral researcher will contribute to this project using
a combination of evolutionary and comparative genomics, as well as a
diverse set of bioinformatic approaches for data analysis and integration
(e.g., transcriptomics, genomics, phenotypic data). This position offers
a unique opportunity to integrate diverse state-of-the-art genomic and
phenotypic datasets across different model organisms to understand the
role of genes, molecular pathways in the origin of complex diseases.

CINV provides a highly collaborative and multidisciplinary environment
using a variety of computational and experimental approaches,
including genetically tractable animal models as well as expertise in
genetics, behavior, glia-neuron communication, metabolism, biophysics,
genomics, bioinformatics, host-microbe communication, and biomolecular
modelling. The new postdoc will be part of one of our labs which focuses
more generally on the intersection between molecular evolution and
disease biology.

Required qualifications are a PhD in evolutionary biology, computational
biology, bioinformatics, or closely related fields. Candidates must have
excellent verbal and written communication skills (working language
is English), as well as an established record of productivity (e.g.,
at least one previous peer-reviewed publication). Candidates with a
past record of publications in bioinfomatics, computational biology,
population genetics or evolutionary genomics are strongly preferred. Ideal
candidates should have experience in analyzing genomic and phenomic
data, performing comparative evolution or population genomic analyses,
as well as in collaborating with experimentalists.

Interested candidates should first contact Evandro Ferrada at
. Please include the following: (1) a cover
letter addressing your interest in the position and how your expertise
meets the position requirements, (2) a CV, (3) contact information of
at least 2 references. A short online interview will follow to discuss
specific proposals. Candidate materials will be reviewed as soon as
possible until the position is filled.

For further information, please visit:
https://cinv.uv.cl/cinv-postdoctoral-fellowship-program-2026/

Dr. Evandro Ferrada
Associate Profesor

Centro Interdisciplinario de Neurociencia (CINV)

Facultad de Ciencias, Universidad de Valpara�so.

Pasaje Harrington 287, Playa Ancha, Valpara�so, Chile.

Tel. +56 (32) 250 8453

www.cinv.cl