Summary some good habit in our research. I have been hit by the project badly since some bad habit, such as:
1, Record everything in a project in one systemic page, such as
Evernote, so that you can check them easily. Never try to remember everything if you put them everywhere.
2, Save all the data which you were used to make the figure, since sometimes boxplot will be change to
violin plot or heatmap plot or
bee swarm plot. You will never know which is the prefer for your boss or reviewer. If you
don’t save them, maybe you need to re-built the data again.
3, Keep the figure as
4, Use Adobe illustrator, Never Never Never use Photoshop.
5, Learn to use ggplot2, it would be more fast to prepare Figures if you master it compared with R plot.
6, Build your own function (Perl, R, Python)
library/packages. Compile and Use them for next time. Don't write them again and again.
7, Upload the code to
gitlab, share with yourself and others.
8, record all the method, idea, process, procedure and pipelines in
mediawiki and shared with your lab-mates
9, Save the fastq to SRA/GEO or wig to UCSC so that we don't need spend extra money after we complete the project
10, The code or script by non-professional stuff/student would be horrible, Majority of them will have some bugs, be careful, asking help for
code review from colleagues would be good habit.
11, how to
prepare your manuscript and the efficiency: link: the best habit to prepare manuscript
Management Strategies and Advice for Bioinformaticians: Link here
13, Build your own bioinformatics server and assemble all the platform your need and your own pipeline.
14, Arial for font in the Fiugre, never use red-green combination, never use rainbow color scale, Font size:8pt
15, Never never make your script running for 12 hours (especially in PBS), split them into many pieces within 2 hours. You boss will be in the trouble if you meet bugs for several times.
16, try to use
Anaconda data science platform and assemble the tools what you prefer as a uniform platform.
fork and help to make your frequent software more powerful in
18, check the positive and negative control for each computational analysis, so that find all bugs in the beginning.
19, maintain your blog/make md5sum label for each your own database