Bioinformatics: Where to Start?

A simplified resource list to provide a solution to the question of where/how to start regarding bioinformatics.

[ENG] Beginner friendly bioinformatics resources.

Hello,
I will soon share the resources I have briefly compiled for the advice requested from me.

The biggest problem of bioinformatics or moving to a new field may be not knowing where to start. Especially if there are a lot of resources, people get tired just thinking about it.

I prepared a resource list (beginner friendly resources pdf) for med&omics in 2022; This might give a good idea.

Although these are extensible, they are YouTube channels that appeal to everyone, from those who are new to the field to those who are engaged in more advanced analysis, and with a little confusion, it is also possible to access their github page or websites:

* chatomics (Tommy has instructional videos.)
* bioinformagician
* SIB, Swiss Institute of Bioinformatics
Additionally, I also like the Harvard Chan Bioinformatics Core pages.

Since Bilkent MBG license provides training in MATLAB, Java (both now comprehensively changed to Python) and R, we did not start from scratch in terms of coding. It is beneficial to learn the basics of coding and then immediately become familiar with working in a Unix/Linux environment (Command Line Tools, Ubuntu LTS, servers, clouds).

Using Git for version control and GitHub for maintenance also needs to be built slowly. You can even start by uploading small iteration projects you’ve made. The next stage will be to use standard workflow systems such as nextflow, snakemake (to avoid reinventing the wheel).

Again, we did not start from scratch since statistics and probability were taught in undergraduate education, but it is necessary to add to it. Josh (Starmer)’s channel will be very useful.

If you think it’s not enough for you, you can be a part of a training that you will conduct yourself as follows (most resources are open, you train yourself).

While we were learning by trial and error, there were some analyses, the Biostars community and Stack overflow in our time; now free ChatGPT version etc. It will even be very useful to you. It is necessary to try and see. Most skill sets mature with practice.

Moving forward on a project basis and with a mentor can speed up the process. Being part of a community is also nice to stay motivated. Locally operating groups such as ISCB-RSG-Türkiye and others may be useful. As a matter of fact, RSG-Turkey occasionally provides free training, I recommend you to take advantage of it: https://github.com/rsgturkey.

The field we call Bioinformatics and Computational Biology (CompBio) is actually much broader than we think. For example, my knowledge and experience in computational structural biology is almost zero. Therefore, the sources here may differ.

CompBio generally consists of 4 main groups for me.

  1. Algorithm developers
    Those in the first group are those who have mastered advanced mathematics and statistics and combine this with coding. Generally, they are Computer Science (CS) graduates and have a weak biology background.
  2.  Tool developers
    The second group is those who are more like software engineers. While combining biology-based algorithms and analyzes with a simplified user interface, they expand the usage area of ​​the tools developed by the first group and make the lives of the last two groups easier. They have a weak knowledge of biology but advanced coding knowledge. Again, it is a field dominated by CS graduates.
  3. Those who change and use tools and algorithms to the extent necessary (tool and algorithms as package users)
    The target group I mentioned above is actually the third group.Although their coding and algorithm knowledge is not as good as the first two groups, they are the ones who pioneer studies that will have their equivalent in life sciences by using the tools developed with a strong biology background in the most efficient way. Some also develop small packages rather than large innovative algorithms. Generally life sciences focused group.
  4. Users of the tools (on/off-line tool only users)
    The last group is those who carry out more experimental studies using certain tools. The majority of this group is life sciences.

There is no such sharp distinction between all these groups. However, I hope that the simplified groupings when defining the field will give those who will take the first step an idea of ​​where and how to start.

I wish success