Algorithms scripts @ https://github.com/jschendel/bioinformatics-algorithms-coursera

Address of the bookmark: https://github.com/jorvis/biocode

WALK IN INTERVIEW

A walk-in Interview for the following position tenable at the Bioinformatics Infrastructure Facility (BIF), Department of Biotechnology, Alagappa University will be held at the Department of Biotechnology, Alagappa University, Karaikudi 630 003 on 15.05.2014 (Thursday) at 01:00 PM. This national facility is funded by the Department of Biotechnology, Ministry of Science and Technology, Government of India, New Delhi. The main objectives of the Centre involve teaching and research activities in bioinformatics/biotechnology.

RA (One Post):

Salary : Rs. 11000 p.m. plus admissible HRA

Qualification: M.Sc., in Bioinformatics/Biotechnology/Biophysics/Biochemistry/ Life Sciences

Interested candidates are encouraged to send their Curriculum Vitae by email to “sk_pandian@rediffmail.com” in advance. On the day of interview, the candidates must produce original certificates in proof of their educational qualification and experience and a recommendation letter from the Head of the Department/Institution where last studied/worked. Candidates who have already passed the required Degree alone are eligible to appear for interview. No TA&DA will be given for attending the interview.

Advertisement: http://www.alagappabiotech.org/Walk%20in%20interview.pdf

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.

Pay scale: Pay Band of Rs. 37400-67000 with AGP of Rs. 9000.

i. Educational Qualification: Good academic record with a Ph.D. Degree in the concerned/allied/relevant disciplines.

ii. A Master's Degree with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed).

iii. A minimum of eight years of experience of teaching and/or research in an academic/research position equivalent to that of Assistant Professor in a University, College or Accredited Research Institution/industry excluding the period of Ph.D. research with evidence of published work and a minimum of 5 publications as books and/or research/policy papers.

iv. Contribution to educational innovation, design of new curricula and courses, and technology - mediated teaching learning process with evidence of having guided doctoral candidates and research students.

v. A minimum score as stipulated in the Academic Performance Indicator (API) based Performance Based Appraisal System (PBAS), set out in UGC Regulation.

Download application form from website: http://www.allduniv.ac.in/

Send your application to the Registrar, University of Allahabad, Allahabad-211002 (U.P.) on or before 30th April 2014

For more details: http://www.allduniv.ac.in/images/adv/backlog/advt-details.pdf OR http://www.allduniv.ac.in/images/news/extension-notice.pdf

Last Apply Date: 30 May 2014

Research Area

We are interested in the development of the statistics and computational methods for the analysis of this data in breast cancer.
We have worked on probabilistic models for subcellular localization, protein-protein interactions, and problems related to chemical genomics.
We are interested in the development of bioinformatics/biostatistical methodology in the analysis of epigenetic/epigenomic data.
We are interested in integrative bioinformatics approaches to learn the gene, gene products, interactions, and regulatory mechanisms involved in mental retardation.

Link @ http://www.mcgill.ca/mcb/

The next step after reading genetic code is writing a script to analyse and explore the hidden information. This tutorial is aimed to introduce you new biological programming languages with their packages/libraries, and assist in your scripting work.

Navigate the sub-section of this page [ see right hand side of the page for it ]

1. Cancer Genomics

2. Microbial Genetics and Metagenomics

3. Human Infective Diseases

4. Computational Drug Design

Interested candidates may apply to Dr. Ranjith N. Kumavath, Assistant Professor & Head, Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Padannakad (PO), Nileshwar, Kasaragod-671328,Kerala. Email: RNkumavath@gmail.com

17 Nov 2013 -22 Nov 2013

More at http://events.embo.org/13-large-scale-data/

Online Registration: https://www.conference-service.com/pc13-47/welcome.cgi

The tutorial is divided in three main parts:

• In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
• The Structure part is about molecular visualization, homology modeling and structural domain prediction.
• In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html