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.

http://www.jnu.ac.in/Career/

http://www.jnu.ac.in/Career/ADVTNo_RC_48_2014.pdf
Last Apply Date:

15 May 2014

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.

Research Area:
Analysis of Next Generation sequence data in cancer
Methods for analysis of structural variation in cancer genomes
Next Generation sequencing in malaria
Computational comparative genomics
Sensitive genomic sequence search techniques using hidden Markov models
Tasmanian devil facial tumour disease

Link @ http://www.wehi.edu.au/faculty_members/dr_tony_papenfuss

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

The importance of transcriptional control has been explored in a burgeoning line of research over several decades; nevertheless, we are still far from having a complete picture of the regulatory mechanisms of genes and non-coding RNAs, and their influences on different phenotypes and disease states of a cell. Recent shifts towards large-scale analyses of transcriptional regulation on a sequence and epigenetic level are at the forefront of research, mainly due to sequencing technology advancements and a deeper understanding of the fundamental regulatory processes involved.

Arriving at a better understanding of the influence of specific parts of the overall regulatory machinery on disease states is a high priority of the group’s research agenda.

We are seeking an enthusiastic student to join the group as a PhD student. Applicants must have a BSc(Hons) or MSc degree in a relevant discipline and a willingness to learn and apply new techniques and work in a team. Both local and international students are encouraged to apply.

The studentship covers all university fees and an annual tax-exempt stipend of NZ$22,000 for three years.

Sebastian Schmeier recently joined Massey University and started his own research group in Auckland, New Zealand, a city regularly ranked one of the most livable in the world. This is your chance to experience the amazing Auckland lifestyle and the excitement of joining a young new science team, while staying connected to world class scientific networks.

To apply for the post, please send a cover letter stating your interest in the position and why you think you would be a good candidate, a Curriculum Vitae, a copy of your academic transcript, a sample of your written scientific work, and the names of three referees. Applications will be accepted until the position is filled.

Enquiries and applications to Sebastian Schmeier (s.schmeier@massey.ac.nz).

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

Biocon, India's largest publicly-held biotechnology firm, launched its second novel 'lab-to-market' molecule, Alzumab, to treat chronic plaque psoriasis at a cost 50 per cent lower than the existing one.

Biocon is bringing Alzumab (a biologic) in the form of a vial after working on it for nearly a decade. The work had initially started in a joint effort with the Center of Molecular Immunology, Havana, but Biocon took control of the programme soon after and also bought out its partner a few years ago. Biocon tell to the media that genotypic played a critical role in functional studies and clinical trial Genomics.

The Biocon drug, at around ₹ 7,500 a vial, will cost half as much as the currently available drugs - from Pfizer and J&J - to treat psoriasis, a skin disease that causes rough red areas where the skin comes off in small pieces. A patient is usually prescribed to consume more than 40 vials in a 24-week course.

No.NITW/BT/2014/adhoc

APPLICATIONS ARE INVITED FOR THE APPOINTMENT OF ADHOC FACULTY ON CONTRACT BASIS IN THE DEAPARTMENT OF BIOTECHNOLOGY

Period of Contract: Initially the appointment is for one semester i.e., from July 2014 up to December 2014 only.

Essential Qualifications:

i) B. Tech or equivalent in Biotechnology/ Industrial Biotechnology/ Biochemical Engineering / Chemical Engg. Or M. Sc in Microbiology/ Botany/ Zoology/ Biochemistry/Biotechnology and ii) M. Tech or equivalent in Biotechnology/Industrial Biotechnology/Bioinformatics

Or

Integrated M. Tech in Biotechnology/Industrial Biotechnology/ Bioinformatics

Candidates must possess First class (60% aggregate marks or 6.5 CGPA) at B. Tech/ M. Sc and M. Tech.

Desirable: Ph. D Pay Package: All selected candidates shall be eligible for a consolidated pay of Rs.30, 000/- per month. Candidates with Ph. D shall be eligible for an additional amount of Rs.5, 000/- per month.

How to apply : Applications on plain paper with attested photocopies of certificate and bio data along with justification for eligibility should reach to the Head, Department of Biotechnology, National Institute of Technology, Warangal AP 506004 in the form of soft or hard copy on or before 21st June 2014 email : biotech_hod@nitw.ac.in

Intimation: No separate call letters will be sent to the candidates. All the eligible candidates will be notified in the institute web site on 23rd June 2014. All the eligible candidates are requested to report for the interview to the Head, Department of Biotechnology at 9:00 AM on 27th June 2014

Joining: Selected candidates will be informed and they are expected to join immediately.

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