http://molbiol-tools.ca/Convert.htm

Address of the bookmark: http://molbiol-tools.ca/Convert.htm

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.  

• the Gene Ontology (GO), which provides the logical structure of the biological functions (‘terms’) and their relationships to one another, manifested as a directed acyclic graph
• the corpus of GO annotations, evidence-based statements relating a specific gene product (a protein, non-coding RNA, or macromolecular complex, which we often refer to as ‘genes’ for simplicity) to a specific ontology term

Together, the ontology and annotations aim to describe a comprehensive model of biological systems. Currently, the GO knowledgebase includes experimental findings from over 140 000 published papers, represented as over 600 000 experimentally-supported GO annotations. These provide the core dataset for additional inference of over 6 million functional annotations for a diverse set of organisms spanning the tree of life.

In addition to this core knowledgebase, GOC resources also include software to edit and perform logical reasoning over the ontologies, web access to the ontology and annotations, and analytical tools that use the GO knowledgebase to support biomedical research.

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

PANNZER2 processes bacterial proteomes in minutes and eukaryotic proteomes in an hour. You can use AAI-profiler to summarize a proteome's species neighbors and reveal taxonomic identity or contamination.

http://ekhidna2.biocenter.helsinki.fi/sanspanz/

IterPro is for the beginners

https://www.ebi.ac.uk/interpro/

You can find other comparative info at https://academic.oup.com/view-large/118391389

Address of the bookmark: http://ekhidna2.biocenter.helsinki.fi/sanspanz/

Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to maarten.reijnders@unil.ch or create an issue.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075

Address of the bookmark: https://gitlab.com/mreijnders/crowdgo

Features

• Linear representation of several segments of DNA
• Comparisons represented by areas between the segments (like Artemis, for example)
• Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
• Plot several subsegments of the same segment on the same line, separated by a //
• Automatic or manual placement of the segments on the plot
• Add annotations to all the lines
• Create smart, automatic annotations for genomes, based on gene names
• Add a user-generated tree
• Add a global scale or a scale to each line
• Use user-defined graphical functions to represent genes

Address of the bookmark: http://genoplotr.r-forge.r-project.org/

1) A cell line expressing or lacking single TF,

2) Breast tumors divided along PAM50 designations

3) Whole brain samples from WT mice or mice lacking a single TF in a particular neuronal subtype

4) Single cell RNAseq analysis of neurons divided by Immediate Early Gene expression levels.

In summary, MAGIC is a standalone application that produces meaningful predictions of TFs and cofactors in transcriptomic experiments.

More at https://uwmadison.app.box.com/s/8j90e5h2rjrsz3bacaxnq8kor2o64vyg

Address of the bookmark: https://github.com/asroopra/MAGIC

Address of the bookmark: https://github.com/DessimozLab/OMArk

Address of the bookmark: https://bioinfo.noble.org/GWASPRO/

https://github.com/IGS/twinblast

https://mra.asm.org/content/8/35/e00842-19

Address of the bookmark: https://github.com/IGS/twinblast