https://bioalgorithms.ucsd.edu/research4.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.

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

Pine Biotech, Inc., a US-based startup working with the Tauber Bioinformatics Research Center is offering a full curriculum online preparing students without any technical background for real-life challenges with large scale biomedical data. Workshops on processing, analysis and biomedical interpretation of Next Generation Sequencing data cover important up-to-date algorithms and machine learning approaches. The most important thing is that there are virtually no pre-requisites such as coding, biostatistics or advanced medical skills. If you know what gene is and how the genes are expressed, you are ready to take the courses or join our workshops. Learn more: https://edu.t-bio.info/workshops/

The objective of this conference is two-fold:

Understanding different algorithms of MD and their relevant applications.
Hands-on practical exercises using NAMD.

More at http://ccbb.jnu.ac.in/advmdbio/

Stipend / Fellowship (consolidated)
Post-doctoral = Rs 50,000 per month
Post Masters = Rs 30,000 per month.

Duration of Fellowships:
Both Post-Doctoral and Post-Masters fellowships are for two years extendable to one more year depending on the progress of the project and decision of the technical committee.

The University Innovation Clusters established at the five Universities have their broad scientific areas under which BIRAC Innovation Fellows will be selected. The qualification and eligibility of each UIC has been mentioned below.

Who can apply?
BIRAC along with five UICs invites research proposals from:

 Applicants who have completed their Master/ Ph.D. on and after 1st January 2014 for
Post Masters and Post-Doctoral BIRAC Innovation Fellowships

Applicants will have to submit an online application in the prescribed format. Each application will be reviewed by an expert committee at each UIC, which applicant has chosen. Applications will be identified on the identified criteria. Selected applicants will be called for a detailed project presentation and personal interview in front of an expert committee for final selection of the BIRAC Innovation Fellows.

Mere fulfilling the eligibility criterion does not entitle applicants to be called for interview.

More at http://birac.nic.in/webcontent/UIC_Fellowships_Qualification_Eligibility.pdf

The Academy has an International Exchange Programme with Academies/Organizations in the countries: Brazil, China, France, Hungary, Iran, Israel, Nepal, Philippines, Poland, Scotland, Slovak Republic, Republic of Slovenia, Sudan and Taiwan.

Applications are invited from Indian Nationals for consideration by the Academy for the next calendar year.

• The applicant should be a scientist holding a regular (permanent) position in a recognized S & T Institution/University and actively engaged in research work in frontline areas.
• He/She should not have been abroad during the last 3 years under any INSA Programme.
• The scientist should have been accepted to work in an Institute/Laboratory in the country to be visited and this should be supported by a letter of invitation from the host abroad.
• Those who wish to visit abroad for three months should submit a detailed programme of their collaborative research work to be conducted.

All applications duly completed should be forwarded to the academy through proper channel by the employer/head of the Institute.

• Scientists selected for deputation abroad would be provided 100% travel support (by only Air India excursion class airfare, through shortest route from the place of duty in India to the nearest airport of host Institute and back) by INSA.
• Medical Insurance purchased in India.
• Visa fee (if any).
• The receiving Academy/Organization would provide local hospitality including internal travel abroad.

Contact for detail at 

www.insaindia.res.in

INDIAN NATIONAL SCIENCE ACADEMY
Bahadur Shah Zafar Marg, New Delhi – 110 002.
Telephone: 91-11-23221931 – 23221950 (EPABX),
Fax: 91-11- 23235648, 23231095