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.

SCHOOL OF COMPUTATIONAL AND INTEGRATIVE SCIENCES (SC&IS)

ESSENTIAL QUALIFICATION : - M.Sc./M.Tech. in Physics/ Chemistry/ Biology/ Mathematics/ Statistics/ Bioinformatics/ Computational Biology. Ph.D. in the broad areas of Bioinformatics/ Computational Biology. Candidates must have demonstrated capabilities in terms of high impact research publications in either of the above mentioned areas.

Scale of Pay : - 15600-39100/- (PB-III) AGP Rs. 6000/-

For more details on Centre/School, Specializations etc. please visit JNU website www.jnu.ac.in or contact Section Officer, Room Nos. 131-132, Recruitment Cell, Administrative Block, JNU, New Delhi – 110067, Email: recruitmentjnu2013@gmail.com The last date for the receipt of application is 15 May, 2014.

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Last Apply Date:

15 May 2014

More @ http://www.guptalab.org/

The main purpose of OSDDLinux is to provide an in silico platform for computer-aided drug design. This is a collection and compilation of large number of software and web services which will be directly or indirectly useful for researches working in the field of drug design/discovery. Overall objective of OSDDlinux is to promote open source in drug discovery, crowdsourcing and network based collobrations. Following are major features of OSDDlinux ... 

Find more @ http://osddlinux.osdd.net/index.php

With emphasis on research, the institute has large research centers with over 100 PhD & 250 MS by Research students working on wide ranging topics: Computer Science, Information Security, Software engineering, AI & Robotics, VLSI and Embedded Systems, Digital Signal Processing, Computer Aided Structural Engineering (CASE), Computational Natural Sciences, Bio informatics, Computational Linguistics, IT and social development, Exact Humanities, etc., IIIT-H provides a flexible environment which active industry collaborations for conducting research.

The institute is seeking Faculty Members at all levels in the following areas:
Computational & Theoretical Chemistry/Physics/Biology(CNS)
Bioinformatics(BI)
Computational Linguistics
Mathematics & Statistics
IT for Agriculture

We also encourage applications from other areas of national importance (such as, agriculture, health, e-governance, education, policy studies, arts, etc)wherein there is a strong need to come up with IT/ECE enabled solutions.

Academic & Research Programs at the institute:
BTech, MS by Research, PostBSc Dual Degree, MTech, MPhil and PhD.

Eligibility
Candidates should normally have a PhD and an outstanding research record and commitment to teaching. The position and level offered would be based on relevant experience in research and teaching. Candidates without a PhD but demonstrated research record can also be considered in special cases. The compensation (including HRA, LTA, Medical Reimbursement, Research Allowance, etc) is designed to reward the best talent and will not be a limiting factor for the right person.

International Institute of Information Technology
Gachibowli, Hyderabad - 500 032.
Fax:+91-40-6653 1413; e-mail: facultysearch@iiit.ac.in

Lectureship Program : Outstanding candidates with B.Tech/MCA or M.Tech and desirous of joining faculty can apply for lectureship position and enroll for PhD at the same time. Applications for this program can be sent to facultysearch@iiit.ac.in .

More at http://oldwww.iiit.ac.in/content/faculty-openings/

C. Titus Brown http://video.open-bio.org/video/1/a-history-of-bioinformatics-in-the-year-2039

More at http://bigd.big.ac.cn/databasecommons/stat

The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB