More at http://compbio.mit.edu

http://genome.crg.es/courses/Bioinformatics2003_genefinding/

Address of the bookmark: http://genome.crg.es/courses/Bioinformatics2003_genefinding/

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.  

Please enter a list of Gene Ontology IDs below, each on its own line. The GO IDs may be followed by p-values or another quantity which describes the GO term in a way meaningful to you. 

Address of the bookmark: http://revigo.irb.hr/

Address of the bookmark: https://github.com/dglemay/G-NEST

The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.

The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.

Reference:

http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690

http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms

Paper:

http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html

Address of the bookmark: https://sourceforge.net/projects/glean-gene/

Address of the bookmark: https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf

• Freyman, W.A., Johnson, M.G., and C.J. Rothfels. 2022. Homologizer: phylogenetic phasing of gene copies into polyploid subgenomes. bioRxiv 2020.10.22.351486v4

homologizer is implemented in RevBayes. Please see http://revbayes.com to download and install RevBayes. For users without previous RevBayes experience, we recommend the tutorials at http://revbayes.com.

Address of the bookmark: https://github.com/wf8/homologizer

1. Update new things continuously
As per my personal experience, it’s not always easy to work as a bioinformatician!  There are couple of reasons to say that; First computational part of biology make our life’s a little harder compared to other professional categories. The fact - for instance - that the technology cycle in the bioinformatics world is very short, the actual knowledge becomes outdated in a few months or years. Therefore, we need to learn continuously - new things get important. Second, to stay on top of things we really need the strong will to be good at our job. That's probably the most important characteristic to bioinformatician. They are usually an excellent knowledge worker with great technical abilities, and have the will to be that over decades!

2. Avoid the sentence "I did not know what to do!"
In our computational biology lab, we generally face lots of technical problems. But as you know, it's impossible to know everything to do the computational biology jobs ( Yup.. because you need diverse and multidisciplinary knowledge to understand biological problems and resolve their respective solutions), therefore it's absolutely necessary that a bioinformatician finds its way through a new topic. How I typically do that is I use google and I talk to other experts in our laboratory or online biostar community to find out what they think. "I did not know what to do!" should not be an argument for us.

3. To make oneself useful
Several time it does happen, you finished our task earlier than expected; in such cases if you have some time left then: Take a coffee and play chess; reversi, etc. In my case I take a rest. Afterwards I think about what I could do that helps the team to achieve its targets, 'cause some of my team mates probably didn't finish! (at least if I didn't met them at coffee bar !!)

4. Care for all
During my rigorous research duration; I attended several workshop organized by my University departments. I had a discussion with other research fellow, professors; I generally ask … what it really takes to make a team successful or to be a successful research leader. They always said: "Well, you need some caring people!" I think there is a lot truth in that statement. If we do not care about quality, timelines, good team culture, respectful communication (!!), clean code, if all this doesn’t matter to us, then I believe the probability is higher that we fail in research and analysis.

5. Be good with people
Because bioinformatician and computational biologist jobs typically involves to work in a (most wanted J cross-departmental!) team, therefore it's important that we're (more or less) good in dealing with other individuals. Everyone have their own strengths and weaknesses, just like us. It's important to treat all the research team mates with respect, regardless of their technical competence or contributions. Of course, sometimes people deserve a clear statement (!!!), but try to do these things one-on-one. Make sure nobody loses his face. Attend the meetings at the coffee bar; be good at table top soccer and go out once in a while to have a beer with your team. You know what I'm talking about.

At the end of a week I look back and I ask myself what I have produced. This could be paperwork, community days or (best!!) programming code. Always remember there is always a solution to a problem. Most of the times there are at least three solutions. So, don’t just blame, suggest a solution.

That's it. I am looking forward to your thoughts and comments!