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.

1. Make friends with local bioinformatics groups
2. Talk to your computing group
3. Obtain clear expectations
4. Rewrite your job description
5. Papers
6. Attend bioinformatics meetings
7. Try first, ask later

Address of the bookmark: http://biomickwatson.wordpress.com/2013/04/23/a-guide-for-the-lonely-bioinformatician/

Address of the bookmark: http://pasapipeline.github.io/

The Job detail

Advancing the SNP-discovery and polymorphism assessment work across several germplasm panels representing global genetic diversity
Population genetic and genomic analyses, testing the hypothesis related to adaptation in multiple geographic regions
Develop SNP assays from large scale GBS and other re-sequencing data for several target traits utilizing available phenotyping data
Combined analyses of genotypic and phenotypic data for discovery of marker-trait associations, and conducting GWAS
Processing, analyzing, and archiving large-scale genomic data sets, assessing data quality, conducting analyses, interpreting findings, and communicating findings to others including preparation of reports, presentations, posters and journal articles
Providing support to MSc and PhD students on topic related to its major core of research
Any other work assigned by the supervisor

The Person:

PhD in bioinformatics, genetics, computational biology preferably with 1 to 2 years of experience;
familiar with standard bioinformatics tools and scripting languages and emerging and evolving software platforms relevant to bioinformatics and computational biology;
ability to create new analytical pipelines; experience with handling large data sets;
ability to program in at least two of the following: C++, PERL, Python, R, Java.
will use next-generation sequencing technologies to generate marker data for genetic mapping and transcriptome data for expression QTL mapping, and will be responsible for data generation as well as data analysis.

Period and Remuneration: The assignment is for a period of two years, and can be extended for another year depending on performance. ICRISAT pays a very attractive all inclusive lump sum assignment fee payable in Indian Rupees.

How to Apply: Please send your application by email to icrisatjobs@cgiar.org, stating the job title (Special project Scientist-Sorghum Genomics) clearly in the subject column, addressed to the Director, Human Resources and Operations, ICRISAT, Patancheru, Andhra Pradesh 502 324, India, latest by 10 June 2014. The application should include an up-to-date Curriculum Vitae, a short statement of competencies and experience for the position, and the names and addresses (including phone/e-mail) of three referees. Only short-listed candidates will be contacted.

More at: http://www.icrisat.org/careers/Special-Project-Scientist-Sorghum-Genomics.htm

Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you. 

Address of the bookmark: http://revigo.irb.hr/

Webpage : https://www.scilifelab.se/about-us/
Opportunity: https://www.scilifelab.se/about-us/career/

https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

Address of the bookmark: https://github.com/wanyuac/GeneMates

Walk- in- Test cum Interview (based on test) for the selection of Research Associate

under the scheme “Distributed Information Sub Centre –DISC” & Research Assistant under scheme “Phytophthora, Fusarium and Ralstonia diseases of Horticultural and Field Crops” will be held at this Institute as per details indicated below.

WALK -IN- TEST CUM INTERVIEW

Name of the post : Research Associate

Date of Interview : 21-05-2014 at 10.00 AM

No. of posts : One

Qualifications : a)Essential

Ph.D Degree in Bioinformatics OR : Masters degree in Bioinformatics with a minimum of
60% marks or equivalent OGPA with at least two years research experience as evidenced from fellowship/ associateship/training/published papers etc.

b)Desirable: Experience in NGS data analysis.

Emoluments : Rs. 23,000/- per month + HRA (Masters Degree Holders)

Rs. 24,000/- per month + HRA (Ph.D Degree Holders)

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till 31-03-2017.

Title of Assigment : Research Assistant (on contract basis)

No. of vacancy : One

Qualification : Essential : Post Graduation in Bioinformatics and Minimum one year experience in NGS data analysis

Desirable : Experience in Perl/Python/R

Remuneration : Rs. 20,000/- per month (consolidated)

Scope of work :

1. Analysis of different file formats and their conversions.

2. Assessing the quality of data and filtering of raw reads.
3. Assembling the raw reads-de novo as well as reference mapping.
4. Compression of aligned reads using Jam tools
5. RNA-seq. Analysis
6. Differential expression testing involving Normalization, Statistical testing, heat map generation & hierarchical clustering
7. Annotating the assembled genome and geneet testing and their validation
8. Metabolic pathway analysis
9. Comparative genomics
10. Setting up of genome browsers.

Period of Assigment : Initially for six months.

Date & Venue of Interview : 21-05-2014 at IISR, Kozhikode at 10.00 AM

More at http://www.spices.res.in/pdf/disc-advtmnt.pdf

Address of the bookmark: https://github.com/dglemay/G-NEST