Return of BioJedi
I have not run any bioinformatics analysis (except few R graphics), especially for sequencing data, for a very long time. Not sure I can be any sort of Jedi, but let’s try to train ourselves towards this goal (at least I love -including my struggles- in molecular biology and genetics, bioinformatics and after all, idea of being a scientist).
I have been working on developing few single cell assays for different contexts towards my PhD. I had lots of struggles for different aspects of the projects. Sometimes cells were so sensitive to any modification in condition, that sort of modifications were not suitable for your aim or those so impactful papers were hiding some important details that you only know by trying yourself, sometimes it was as easy as replacing the reverse transcriptase or decreasing the number of PCR cycle. It turned out that most of the solutions were much easier than previously thought . Take-away of 3years old PhD: Hard-working is important but working-smart is as important as hard-working (if not more heheheh).
I am glad that finally I could sequence some of my samples. I thought I learnt how to do sequencing when I started to learn rna-seq and following DEG analysis 6 years ago. However, I have realized that despite being a phd on single cell sequencing assay development-based assays, I hve just learnt the details of how to do actual sequencing. Take-away of 3years old PhD: Without actually doing it, some details are missing (hello mr Feynman!).
In short period of time, thanks to my super seniors and supervisor, I learnt a lot (and still learning, by doing stupid mistakes, another hello for Martin’s paper, or simple less of experience). When I started to use Flow Cytometry actively almost a year ago, I was very excited. In fact, every technique I have been learning during PhD process made me excited. Practicing sequencing is not less than that. It is even more :D since I have been working on single cell based assays. And of course, doing bioinformatics analysis of what I designed, modified and sequenced.
Yet there is little more challenge comes here. I am practically and theoretically -presumably more- aware of how to analyze the data if you combine my experience in different fields from wetlab to bulk rna sequencing data analysis during masters and machine learning courses etc. However, ongoing experience is the key. If you think you did your last analysis 2 years ago, it sometimes make you feel little overwhelmed to remember all the details of how to run all these with *less* error codes. And even human reference genome was updated during this period xD.
Nevertheless, this challenge also very exciting. If I have a chance to proceed towards repeated sequencing, following analysis of the data, I should have learn the version control properly and practice proper script writing/sharing towards open science goals rather than pieces of codes in different places after couple of trials and *error*s (and googling and correcting by using very helpful random guys’ very useful suggestion upon a very similar question asked few years ago).
Please put more flames of science fire (in a good way)here. Exciting part of life long learning part of the research never ends (yeah, even technique-wise)! I will be coming back to share more. Please wish me luck ^^
