Dr Altan Kara is a Bioinformatics specialist at the faculty of Gene Engineering and Biotechnology Institute at TUBITAK MAM Research Center. His research interest revolves around the cancer informatics and computational aided-drug design. I applaud Dr Altan for clearly setting out both his expectations of people that join his lab/university in addition to listing his responsibilities to his research members at TUBITAK MAM Research Institüte. Hopefully, this interview will prove useful to others in the field, especially to those who are just starting their bioinformatics careers.

You can find out more about Dr Altan by visiting his (well documented) lab page (http://gmbe.mam.tubitak.gov.tr/en) and BOL page http://bioinformaticsonline.com/profile/altan . And now, on to the BOL:“Tryst with a Bioinformatician” interview series ...

• What push you to join Computational Biology/Bioinformatics?

According to me, bioinformatics is the center of modern biological research and if a researcher wants to discover new biological insights by evaluating the globally produced biological data to derivate unified solutions for specific biological problems, learning bioinformatics is the only way to achieve this goal.

• What fascinates you about Computational Biology/Bioinformatics?

It's flexibility. As well known, there are highly diverse and complex biological questions are waiting to be enlightened and it's impossible to bring solutions to this diversity by using similar approaches. Thus, the employed method has to be unique for the targeted biological problem and by using bioinformatics tools this can be easily achieved. 

• What is the one word you would use to describe yourself?

Bioinformatician. :)

• Can you please describe your research work in a nutshell for BOL users.

At my current Institute, I am working in the field of cancer bioinformatics. Briefly, the overall aim of the project which I am working for (AKMARK (Project CODE:5153403)) is, applying a bioinformatics-supported genome, transcriptome, proteome, and metabolome analysis to reveal the molecular profile of the disease through an integrated approach, and to develop an early diagnosis and scanning kit based on this profile. Alterations in the gene, transcript, protein, and metabolite profiles between normal tissue, normal tissue adjoined to the tumor (reactive stroma), tumor tissue, lymph node metastasis, and blood samples taken from the same patient and the reflection of these changes in some other selected body fluids will be revealed within the scope of the project. The molecular structures involved in the development and progression of NSCLC will be determined and relations with the clinical, tumor-node-metastasis (TNM) staging and histology will be made. The development of a diagnostic kit for immediate clinical purposes and an electrochemical biosensor for quick on-site applications are targeted through the development of a number of antibody and aptamer formed against the most specific biomarker selected from the panel.

• Is there anything else we should know about you and your research?

Besides AKMARK, I am also in preparation of having a side project that aims for the development of a computational method to design inhibitors for prokaryotic two-component systems. In this project, I will be in collaboration with Prof. Maria Kontoyianni, SIUE: Southern Illinois University Edwardsville, School of Pharmacy.

• What was your greatest scientific disappointment in life till now?

So far I do not experience any memorable scientific disappointment in my life. :)

• What major research challenges and problems did you face yet? How did you handle them?

The major challenge which I faced so far in my scientific career was predicting the interaction between the prokaryotic two-component proteins. To be able to accurately predict the interactions between these proteins, I create a meta-predictor by using a support vector machine. By using this technique I integrated six different protein-protein interaction methods in a way to cover disadvantage of one method with the advantage of another one. The meta-predictor which I developed during this work is accessible via http://metapred2cs.ibers.aber.ac.uk/ and for more detailed information about the system the articles with the PMID IDs; PMID: 27378293 and PMID: 26384938 can be read.

• What's your all-time favourite bioinformatics package, and why?

For me, the best bioinformatics package is R/Bioconductor. The reason why I like this package is, it provides lots of useful tools for comprehensive analysis and comparison of high-throughput experimental data in an integrated manner and besides lots of the packages it provides, it is open source and also open for development. As a result, it provides strong and flexible ways to do science.

• In bioinformatics, do you see yourself in which of the following roles-scientist, analyst, developer, engineer or pure academician?

Scientist / Developer.

• What will you like to accomplish in next five years / ten years?

For my current research, I would like to design a pipeline to automatically integrate and analyse omics data for cancer research which will be specifically aiming for biomarker and novel drug target discovery. In addition to this, I also like to develop another pipeline for prokaryotic TCS protein structure prediction and inhibitor design.

• When you will be retired, what would you tell next generation bioinformaticians?

Bioinformatics is not all about scripting and researchers who study in this field should never expect a tool to do their analyses for them. Besides computational skills, a bioinformatician must have a strong biological background in his/her research area which will allow them to understand if anything went wrong during their run by only looking at the results instead of just blindly trusting the output of the bioinformatics tools.

• What you always miss in bioinformatics when you will no longer working in this field?

Bioinformatics is open to doing multi-discipliner research with scientists all around the world. As a result, while I studying in this field I can interactively learn a lot from wide range research community. I think this is the one thing which I will miss the most.

• If there will be bioinformatics company owned by you in future, What are your company focus and aim?

With the increasing amount of data in databases, there is already a massive need for effective methods to eliminate the manipulated data and reach to clean/useful information. As days pass, the requirement of data mining will be the first step of any research project. For this reason, the major goal of my bioinformatics company will be developing effective tools to eliminate manipulated datasets and information that exist in the literature and provide trustworthy clean information/datasets for researchers.

• How much bioinformatics change in 2050, according to your wild imagination?

Bioinformatics is a field that constantly and dynamically changes. As the bioinformatics progress, new tools and methods become available and they provide a better application of existing methods or totally new methods that offer an alternative solution to various biological problems. A long with these updates, developers also provide easy to use GUIs for most of the tools. Considering this, if the field carries on developing like this, every single researcher with a strong biological background can be able to perform bioinformatics analyses by him/herself without needing a professional help. As a result, almost all of the bioinformaticians will be responsible just for development of new methods/tools.

• What would one piece of advice you give someone who's trying to reinvent themselves and enter into bioinformatics sector?

Bioinformatics is a wide field with a lot of career options. Thus, if a researcher likes to step into this field first he/she should be clear about the branch of the bioinformatics they like to study in. Following to this decision they should first learn at least one programing language and investigate the ways of how other researcher employed that language in their researches and WHY? A researcher, in this field, should never create and use copy paste scripts but always must understand WHY the other researcher worked in that way. Knowing the answer of this question is the only way to learn bioinformatics. Besides, a researcher in the field of bioinformatics (from any branch) must always be good about the environmental control. In other words, one should always easily control input output directories, modify files or directories, annotate and modify employed scripts during the research and should not allow any confusion during the different stages of the research. Finally, they should not blindly trust the output of a tool/software but do a benchmarking test for each of the tools which they decided to utilise in their research. In addition to this, even if the tools pass the benchmarking, researchers should have a good biological background in their field to tell if anything when wrong during the process by only looking the output(s) of the employed pipelines/packages/tools.  

Address of the bookmark: https://github.com/b-brankovics/grabb

Essential Qualifications: Ph.D. (Bioinformatics/ Biophysics/ Biotechnology or any other stream of biological/ physical sciences) with a minimum of two publications in reputed peer reviewed journals in the area of structural bioinformatics or biophysics or biomolecular modeling/ simulation.

Job description: Development of bioinformatics tools and algorithms/software for structure based analysis of biomolecular systems. Programmatic access to major biomolecular databases using APIs Knowledge based prediction and analysis of biomolecular structure, function and interactions. Docking/simulations for inhibitor design.

Desirable Qualifications (Research Associate/s): i) Strong computer programming skills (in Python/PERL/PHP or C++ or object oriented database management systems like MySQL etc or scripting languages under LINUX/UNIX environment).

ii) Extensive experience in computational analysis of biomolecular structure/interactions and usage of advanced biomolecular simulation softwares. iii) Adequate knowledge of major databases, webservers and softwares in the area of biomolecular structure/function and drug design. iv) Familiarity with Parallel Programming environments and experience in usage of high-end HPC clusters.

The candidates must highlight their experience in above mentioned fields/topics in their CV. Initial appointment will be for a period of 1 year, subject to extension after review of performance.

Emoluments: As per DST, GOI norms and commensurate with experience.

More at https://www.iisc.ac.in/positions-open/

The fellowship will be for a period of 3 years, or for a period of 4 years, with 25 % compulsory work (e.g. teaching responsibilities at the department) contingent on the qualifications of the candidate and the teaching needs of the department.

Starting date no later than October 1, 2021.

More at https://www.jobbnorge.no/en/available-jobs/job/199984/phd-research-fellow-in-genomics-and-bioinformatics

Affiliation
The selected candidate will be affiliated to one U.S. host institution.

USIEF strongly recommends all applicants to identify institutions with which they wish to be affiliated, and to correspond, in advance, with potential host institutions. If the applicant has secured a letter of invitation from a U.S. institution, it should be included as a part of the online application. The letter of invitation should indicate the duration of visit, preferably with dates.

Grant Benefits

These fellowships provide J-1 visa support, a monthly stipend, Accident and Sickness Program for Exchanges per U.S. Government guidelines, round-trip economy class air travel between India and the U.S., a modest settling-in allowance, and a professional allowance.

Subject to availability of funds, a dependent allowance and international travel may be provided for one accompanying eligible dependent provided the dependent is with the grantee in the U.S. for at least 80 per cent of the grant period. Flex grantees are not eligible for dependent benefits.

Eligibility Requirements

In addition to the General Prerequisites:

Applicants must have a Ph.D. degree within the past four years. S/he must have obtained a Ph.D. degree between September 15, 2018 and September 14, 2022. The applicants are required to upload his/her Ph.D. degree certificate/provisional Ph.D. certificate on the online application;
Applicants must have a publication in reputed journals and demonstrate evidence of superior academic and professional achievement. S/he must upload a recent significant publication (copy of paper/article) on the online application (not exceeding 30 pages); and
If applicant is employed, please follow the instructions carefully regarding employer’s endorsement. If applicable, obtain the endorsement from the appropriate administrative authority on the Letter of Support from Home Institution. The employer must indicate that leave will be granted for the fellowship period. The applicant can download the Letter of Support from Home Institution from the USIEF website. Candidates working under government-funded projects are also required to get endorsement from their affiliating institutions in India.

How to Apply

Applications must be submitted online at: https://apply.iie.org/fvsp2023/

1. Big Data and Bioinformatics

The exponential growth in biological data, driven by advancements in sequencing technologies and high-throughput experiments, has made bioinformatics an indispensable tool. By 2030, we anticipate:

• Petabyte-Scale Data Management: Enhanced storage solutions and cloud computing platforms will allow researchers to handle the vast amounts of data generated from omics studies, including genomics, transcriptomics, and proteomics.

• AI and Machine Learning Integration: Sophisticated algorithms will uncover patterns and relationships in large datasets, enabling predictions about gene function, disease susceptibility, and therapeutic outcomes.

2. Personalized Medicine and Genomics

Bioinformatics will play a pivotal role in tailoring healthcare to individual patients. Key developments include:

• Whole-Genome Sequencing in Clinics: The decreasing cost of sequencing will make it routine in medical diagnostics, enabling personalized treatment plans based on an individual’s genetic makeup.

• Drug Repurposing and Development: Computational tools will identify potential new uses for existing drugs, accelerating the development of targeted therapies.

3. Advancing Computational Tools

The future will see the development of more user-friendly and powerful bioinformatics tools:

• Graph-Based Approaches: Enhanced algorithms for analyzing complex biological networks, such as protein-protein interaction maps.

• Visualization Tools: Intuitive software for visualizing multi-dimensional data, enabling researchers to interpret findings more effectively.

4. Synthetic Biology and Systems Biology

Bioinformatics will continue to drive progress in synthetic and systems biology by:

• Gene Circuit Design: Leveraging computational models to design and simulate synthetic biological systems.

• Understanding Cellular Pathways: Integrating multi-omics data to model cellular processes with unprecedented accuracy.

5. Bioinformatics in Agriculture and Environmental Science

Beyond healthcare, bioinformatics will revolutionize agriculture and environmental conservation:

• Crop Improvement: Genomic studies will help develop high-yield, disease-resistant, and climate-resilient crops.

• Microbial Ecology: Metagenomics will enhance our understanding of microbial communities, aiding in bioremediation and ecosystem management.

6. Democratization of Bioinformatics

Open-source software and accessible education will broaden participation in bioinformatics research:

• Community-Driven Projects: Collaborative platforms like GitHub will continue to foster innovation in tool development.

• Education and Training: Online courses and workshops will bridge skill gaps, enabling researchers from diverse backgrounds to contribute.

Challenges and Ethical Considerations

While the future is bright, challenges remain. Data privacy and ethical concerns surrounding genetic information require careful navigation. Furthermore, addressing the digital divide is critical to ensuring equitable access to bioinformatics resources globally.

Conclusion

The future of bioinformatics is boundless, with opportunities to revolutionize our understanding of life and improve human health. As technologies evolve and collaborations flourish, bioinformatics will undoubtedly remain at the forefront of scientific discovery, unlocking the secrets of life one dataset at a time.

Louisiana Biomedical Research Network (LBRN) announces registration for it's Summer 2019 Bioinformatics Training Program. The program will be focused on processing, analysis and interpretation of next generation sequecning data for biologists. Learn more:

https://edu.t-bio.info/louisiana-biomedical-research-network-summer-2019-lbrn-bioinformatics-training-program/

Address of the bookmark: https://bioinformatik.de/en/bioinformatics-in-germany/research/research-groups.html

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

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full