In fact, there are huge demands for expert biological data analyst. It’s a fairly new  (not exactly) hot area, these bioinformatician are invaluable because they know and understand the significance of biological data for your research and how you can use it for better understanding of biological problems.

The bioinformatics can discover biological patterns and stories in genomic and proteomics data. They can develop the pipeline needed to properly collect, store and analyse it.

Once your research group is ready to make a larger investment and hire a bioinformatician to gain a competitive edge, there are several key traits to seek out in potential candidates. The best bioinformatician are:

1. Highly Skilled - programming skills, experience with the biological software and tools.

The biological data won’t illuminate much if the scientist analysing it doesn’t possess practical programming skills, experience with the biological software and tools and a thorough understanding of basic biological stuff. A solid background in mathematics and statistics is also an indispensable trait.

2. Insight - Real vision, robust understanding and deep insight.

In order to hire the best bioinformatics and computational biologist scientist for your needs, it is always recommended and mostly practiced by the recruiters, to ask each contender to write and develop a sample script/presentation based on a specific set of data you provide. Then, explore the approaches used to deal with data provided and pick up those candidates who convey real vision, robust understanding and deep insight.

3. Energetic – Curiosity to explore

Mostly natural curiosity and enthusiasm for solving big biological problems coupled with an ability to transform data into a scientific stories may place one candidate above the rest. In addition to achieve that, the bioinformatician should be agile enough to quickly modify their methods to suit changes within a particular research.

4. Researcher – Publications

Look for someone who has a keen sense and understanding of concern biological problems. You can judge it by looking at previously published papers and data. It is always recommended to have a look at GitHub and other repository for codes written by her/him.

5. Impressive communicator - Insight that can’t be expressed is worthless.

Good bioinformatics scientists are able to uncover biological patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. In other words, they should must have good scientific writing skills. A computational biologis/bioinformatician  should know how to present the data and tell a scientific story through numbers/images.

Under   a   NEPAD   initiative,   the   Biosciences   Eastern   and   Central   Africa   (BECA)  (www.biosciencesafrica.org) was established at ILRI. BECA consists of a hub, regional nodes, and  other affiliated laboratories and partner institutes. A state of the art joint Bioinformatics Platform  (www.becabioinfo.org), whose overall goal is to provide a coherent and powerful bioinformatics  infrastructure for use by all scientists in East and central Africa. The Platform goal requires both  physical and intellectual developments that together provide researchers with access to diverse  infrastructure in a wide­area network, thereby addressing four important aspects of bioinformatics: 

1) Science: bioinformatics tools for data integration and visualization, standardization of data  formats and data analysis strategies, and distribution of analysis tasks over local­ and widearea networks are in development; 

2)  Bioinformatics Support Facility: provides assistance and custom programming to projects  and those unable to establish a bioinformatics support function intrinsic to their project due  to shortage of qualified personnel or lack of funding; 

3) Hardware Platform: provide a powerful high performance computing platform capable of  handling the largest analysis needs for projects; 

4) Bioinformatics Training for East and central African scientists: While many Web­based  tools are available to the wet­lab researcher, the Web is not well suited for tasks beyond  single­sequence annotation. Researchers need to become productive in a server­based Unix  environment with its wealth of scripting and automation tools. Even at an entry­level, this  can be an intimidating task if proper guidance is not available.

International Centre of Insect Physiology and Ecology (ICIPE): ICIPE’s research focus is on insect biology, in order to improve the wellbeing of the peoples of the  tropics through insect science. There is a commitment to utilise contemporary science in order to  limit the impact of disease vectors, and agricultural pests. The understanding of the mechanisms  associated with behaviour (e.g. attraction and repellency) is crucial. ICIPE seeks to enhance its  bioinformatics capacity in order to support data from various EST projects designed to gain insights  into the insect ecology and plant pathogen interactions though studies of metabolic pathways  associated with production of all elochemicals. 

Long­term training activities:

Kenyatta University: An introductory course in Bioinformatics is offers to MSc Biotechnology  students. This comprises of 35 hours of lectures and practicals.

University of Nairobi: A centre for Biotechnology and Bioinformatics (CEBIB), which will offer  postgraduate training (diplomas, MSc and PhD) in areas of biotechnology and bioinformatics has  recently been launched. Other universities in Kenya, including Egerton, Maseno and the Jomo Kenyatta University of  Agriculture and Technology offer introductory courses to undergraduates in biomedical sciences. In addition, under the BECA platform MSc and PhD fellowships are being made available for  Bioinformatics students. ILRI is forging links with Universities in South Africa and the United  Kingdom to provide access to courses and training material. 

Research Interest and Activities:

The following are the present areas of research interest: 1. EST clustering 2. Genome sequencing and annotation 3. Functional genomics and proteomics (including key tropical pathogens) 4. Structural bioinformatics 5. Development of Bioinformatics Data Management Systems 6. Gene Mining 7. High Throughput Genotyping 8. Microarray data management and analysis 9. Metagenomics 10. Immunoinformatics 11. Host­pathogen interaction 12. High performance computing and grid development 13. Parasite transfection technologies 14. Cell cycle regulation 15. Population genetics 16. Vector genomics 17. Drug, vaccine and diagnostic target discovery

More at Web site and links:

http://www.ilri.cgiar.org/

http://www.icipe.org/    

http://www.uonbi.ac.ke/cebib

Address of the bookmark: http://www.bioapp.org/ewas/

Genome Workbench can display sequence data in many ways, including graphical sequence views, various alignment views, phylogenetic tree views, and tabular views of data. It can also align your private data to data in public databases, display your data in the context of public data, and retrieve BLAST results.

Genome Workbench is built on the NCBI C++ ToolKit and uses cross-platform APIs for graphics. It runs on your local machine, and is available for Windows 2000/XP, Linux, MacOS X, and various flavors of Unix.

NCBI Genome Workbench is an integrated application for viewing and analyzing sequence data. Genome Workbench was developed entirely in-house at NCBI and makes use of the NCBI C++ ToolKit. The C++ ToolKit provides a convenient and flexible cross-platform API for managing system internals, database connections, network sockets, and the NCBI data model. In addition, the C++ ToolKit provides the Object Manager, which abstracts handling of sequences and sequence-related objects.

 New Features in Genome Workbench 2.7.15

• Multiple Alignment View: implemented adaptive feature display when zooming in
• Active Objects Inspector replaces Selection Inspector. New View should offer an improved selection context examination. See Using Active Objects Inspector tutorial for more details.
• Binary packages for Linux OpenSUSE 13.1 are now available

Bug Fixes and Improvements in Genome Workbench 2.7.15

• Fixed major issue with OpenGL overlay/scrolling. Could cause crashes or view scrolling irregularities
• Multiple Pane View: fixed crash on loading BLAST results
• Graphical Sequence View: fixed crash on zooming in and out, related to SNP track
• Graphical Sequence View: fixed Go To Position dialog to give better diagnostics in case of a user error
• Graphical Sequence View: PDF export fixed rendering of Markers with commas in the name
• Text View / Flat File: fixed Mac OS rendering issues
• Text View / Flat File: performance optimization, extended capabilities of real-time rendering of molecules to tens of thousands
• File Import: optimization improvement to speed up load of files containing multiple project items
• File Import: remapping stage now shows accession.version and description of molecules, instead of plain GI numbers
• Mac OS: improved tooltips for toolbar buttons
• Phylogenetic Tree Builder Tool: improved diagnostics of errors
• Multiple Alignment View: optimizations to avoid main GUI freezes
• Open Dialog: removed duplicate elements in table of genomes (load Genome)
• PDF export: fixed issue with XREF table errors
• Tree View: fixed issues with showing Force Layout progress on Mac OS
• Tree View: PDF export fixed issues for showing labels of collapsed nodes
• Tree View: added an option to stop layout
• Tree View: broadcasting mechanism fixed not to accumulate selected nodes

Reference:

NCBI news

http://www.ncbi.nlm.nih.gov/tools/gbench/

Address of the bookmark: http://www.cytoscape.org/

For the full list of features, improvements and fixes, see the release notes:https://ncbi.nlm.nih.gov/tools/gbench/releasenotes

New Features

• BLAST Tool: added support for local custom BLAST databases
• Graphical Sequence View: added log scaling option for graph tracks
• Generic Table View: new tutorial added

Bug Fixes and Improvements

• Project Tree View: Genomic Collections/Assemblies now show accessions, not just names
• Tree View: layout updated to better accommodate nodes of different sizes
• Table Import Dialog (MacOS): fixed issue with table visibility
• Fixed bug where different molecules IDs in GenBank could resolve to the same sequence
• Graphical Sequence View: fixed issue where sequence track was not shown for some sequences
• Graphical Sequence View: fixed protein coloration methods
• Graphical Sequence View: improved rendering of Markers to better indicate boundaries and produce higher quality PDF images
• Create Gene Model tool: fixed scenario when gene model tool failed with local sequences
• Search View: ORF Finder – fixed incorrect protein lengths
• Fixed bug with not opening project file (.gbp) on a click
• Fixed issues in GVF import
• Fixed BLAST Search tool against NCBI databases not working
• Fixed tblastn (protein BLAST) not working in standalone mode
• Fixed GTF export failure

Address of the bookmark: https://cran.r-project.org/web/packages/vcfR/vignettes/visualization_1.html

Address of the bookmark: https://github.com/marbl/Krona/wiki

https://www.youtube.com/watch?v=WgufqYMHCvM

Address of the bookmark: http://www.atgc.org/PhyloGrapher/PhyloGrapher_Welcome.html