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

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/

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 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

ancestor -- Any organism, population, or species from which some other organism, population, or species is descended by reproduction.

apomorphy -- specialized (=derived) characters of an organism.

basal group -- The earliest diverging group within a clade; for instance, to hypothesize that sponges are basal animals is to suggest that the lineage(s) leading to sponges diverged from the lineage that gave rise to all other animals.

biological classification -- The orderly arrangement of organisms in hierarchical system that ideally reflects evolutionary history.

cDNA -- Complementary DNA; DNA that is synthesized, by reverse transcriptase, from a Messenger RNA template ( Messenger RNA contains the coded information for protein synthesis).

character -- Heritable trait possessed by an organism.

character state -- characters are usually described in terms of their states, for example: "hair present" vs. "hair absent," where "hair" is the character, and "present" and "absent" are its states.

clade -- A monophyletic taxon; a group of organisms which includes the most recent common ancestor of all of its members and all of the descendants of that most recent common ancestor. From the Greek word "klados", meaning branch or twig.

cladogenesis -- The development of a new clade; the splitting of a single lineage into two distinct lineages; speciation.

cladogram -- A diagram, resulting from a cladistic analysis, which depicts a hypothetical branching sequence of lineages leading to the taxa under consideration. The points of branching within a cladogram are called nodes. All taxa occur at the endpoints of the cladogram.

convergence -- Similarities which have arisen independently in two or more organisms that are not closely related. Contrast with homology. 

crown group -- All the taxa descended from a major cladogenesis event, recognized by possessing the clade's synapomorphy. See: stem group.

derived -- Describes a character state that is present in one or more subclades, but not all, of a clade under consideration. A derived character state is inferred to be a modified version of the primitive condition of that character, and to have arisen later in the evolution of the clade. For example, "presence of hair" is a primitive character state for all mammals, whereas the "hairlessness" of whales is a derived state for one subclade within the Mammalia.

diversity -- Term used to describe numbers of taxa, or variation in morphology. 

evolution -- Darwin's definition: descent with modification. The term has been variously used and abused since Darwin to include everything from the origin of man to the origin of life.

evolutionary tree -- A diagram which depicts the hypothetical phylogeny of the taxa under consideration. The points at which lineages split represent ancestor taxa to the descendant taxa appearing at the terminal points of the cladogram.

expressed sequence tag (EST) -- A partial coding sequence isolated at random from a cDNA library, used for identification and mapping of coding sequences, for discovery of new genes and (by reference to sequence data banks) for discovery of identities with other genes.

extinction -- When all the members of a clade or taxon die, the group is said to be extinct.

genetic marker -- A DNA sequence that can be recognized and thus used to characterize the larger DNA sequence and the chromosome in which it occurs. 

homolog -- A feature that appears similar in two or more taxa with a common ancestor that also possessed that feature.

homology -- Two structures are considered homologous when they are inherited from a common ancestor which possessed the structure. This may be difficult to determine when the structure has been modified through descent.

hypothesis -- A concept or idea that can be falsified by various scientific methods.

ingroup -- In a cladistic analysis, the set of taxa which are hypothesized to be more closely related to each other than any are to the outgroup.

lineage -- Any continuous line of descent; any series of organisms connected by reproduction by parent of offspring.

monophyletic -- Term applied to a group of organisms which includes the most recent common ancestor of all of its members and all of the descendants of that most recent common ancestor. A monophyletic group is called a clade.

outgroup -- In a cladistic analysis, any taxon used to help resolve the polarity of characters, and which is hypothesized to be less closely related to each of the taxa under consideration than any are to each other.

paraphyletic -- Term applied to a group of organisms which includes the most recent common ancestor of all of its members, but not all of the descendants of that most recent common ancestor.

parsimony -- Refers to a rule used to choose among possible cladograms, which states that the cladogram implying the least number of changes in character states is the best.

phylogenetics -- Field of biology that deals with the relationships between organisms. It includes the discovery of these relationships, and the study of the causes behind this pattern.

phylogeny -- The evolutionary relationships among organisms; the patterns of lineage branching produced by the true evolutionary history of the organisms being considered.

plesiomorphy -- A primitive character state for the taxa under consideration.

polarity of characters -- The states of characters used in a cladistic analysis, either original or derived. Original characters are those acquired by an ancestor deeper in the phylogeny than the most recent common ancestor of the taxa under consideration. Derived characters are those acquired by the most recent common ancestor of the taxa under consideration.

polyphyletic -- Term applied to a group of organisms which does not include the most recent common ancestor of those organisms; the ancestor does not possess the character shared by members of the group.

primitive -- Describes a character state that is present in the common ancestor of a clade. A primitive character state is inferred to be the original condition of that character within the clade under consideration. For example, "presence of hair" is a primitive character state for all mammals, whereas the "hairlessness" of whales is a derived state for one subclade within the Mammalia.

radiation -- Event of rapid cladogenesis, believed to occur under conditions where a new feature permits a lineage to move into a new niche or new habitat, and is then called an adaptive radiation.

rank -- In traditional taxonomy, taxa are ranked according to their level of inclusiveness. Thus a genus contains one or more species, a family includes one or more genera, and so on.

relatedness -- Two clades are more closely related when they share a more recent common ancestor between them than they do with any other clade.

repetitive DNA -- Sequences of DNA that are found to be repeated, sometimes thousands of times over.  

reticulation -- Joining of separate lineages on a phylogenetic tree, generally through hybridization or through lateral gene transfer. Fairly common in certain land plant clades; reticulation is thought to be rare among metazoans.

selection -- Process which favors one feature of organisms in a population over another feature found in the population. This occurs through differential reproduction -- those with the favored feature produce more offspring than those with the other feature, such that they become a greater percentage of the population in the next generation.

sister group -- The two clades resulting from the splitting of a single lineage.

stem group -- All the taxa in a clade preceding a major cladogenesis event. They are often difficult to recognize because they may not possess synapomorpies found in the crown group.

sympleisiomorphy – A ancestral character shared by the taxa under consideration

synapomorphy -- A character which is derived, and because it is shared by the taxa under consideration, is used to infer common ancestry (shared derived state).

synteny -- Portions of chromosomes in which gene order is conserved. 

systematics -- Field of biology that deals with the diversity of life. Systematics is usually divided into the two areas of phylogenetics and taxonomy.

taxon -- Any named group of organisms, not necessarily a clade

taxonomy -- The science of naming and classifying organisms. 

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

Address of the bookmark: http://learn.genetics.utah.edu/

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

Chettinad Academy of Research and Education (CARE) invites applications from eligible and translational research-oriented candidates to the posts of Professor/Associate Professor/ Assistant Professor Computational Biology, Bioinformatics, and Pharmaceutical Chemistry.

Emoluments: As per UGC norms (Adequate Compensation for Postdoctoral/Teaching experience)

Candidates fulfilling the eligibility criteria as per the UGC norms can send their full CV with copies of certificates and reference letters to the following address by post or by e-mail on or before 31st May 2014

The Registrar,
Chettinad Academy of Research and Education,
Chettinad Health City
Kelambakkam, Chennai 603 103
Tamil Nadu
T +91 (0)44 4741 1000
F +91 (0)44 4741 1011
Email: jobs @chettinadhealthcity.com

Advertisement: http://182.73.176.163/chc/ads2014.pdf

http://www.eva.mpg.de/genetics/bioinformatics/overview.html?Fsize=0%2C%20%40%2F%27

Kelso Group
Department of Evolutionary Genetics
Max Planck Institute for Evolutionary Anthropology
Deutscher Platz 6
04103 Leipzig
Germany
Phone: +49 341 3550 500

Job:
http://www.eva.mpg.de/genetics/bioinformatics/jobs.html?Fsize=0%2C%2B%40