Last updated 2022-04-06

Selected pieces of advice from this tweet. Advice that is trusted either by repetition or self-evidence is not attributed to the original authors. Pull request accepted to keep it up to date: gh jmonroynieto/bioinfo_advice

Be scientific

• Remain skeptical.
• Positive and negative controls.
• Think about the question.
• "Think about project structure and reproducibility from day [one]" @jessenleon
• "Every finding is an artifact until proven otherwise" @thomasliuksiala.

Explore your data

• Visualize everything

Read and understand (as much as you can)

• Read the documentation.
• Read the tutorials.
• Read the papers.
• Read the logs.
• Read the code.
• Understand the theory of the tools you use [enough to be able to be critical and competent].
• "Check your assumptions and those of the methods you use" @MDMorgan_cam
• "Don't be afraid to ask questions and engage with others in the field. The more prospective [sic] about how others do things, the more you learn" @wessidepraxis

Document

• Write it down.
• Take notes
• Make memos.
• Explain.
• Comment your code.
• Log your activities, inquiries, and reading.
• Save your commands.
• Did you write that down?
• Fingerprint inputs.
• Write down everything about your datasets.
• Make it easy for future self to understand what you are doing.
• NO! You will NOT remember.
• Documenting code is also software development.
• Notes are your friends.
• Can you write more? Please?
• There better be a note open while you work.
• Write it down.
• Unwritten results are immaterial; they shall become inconsequential and will require rework. You have been warned.
• Take notes, please.
• Start with a README.md
• Just write it!

Technical

• First and foremost, learn string manipulation (sed, awk,)
• No special characters in names.
• Make scripts generic to be run on different machines.
• Use workflow languages (@Nextflow seems to be the favorite in this crowd...mine too).
• Back up your data.
• Make original data read-only.
• Set specific seeds to ensure reproducibility.
• Use reproducible environments (i.e. containers or conda environments).
• Write tests for your code/analyses.
• Don't trust any tool blindly. Not all limitations are documented.
• Learn a bit of sysadmin, and learn how to install things without privileges.
• GO categories are based on lab work that might not be conclusive (paraphrasis) @HansonM90
• "You don't have enough sanity checks, [sic] add more." @constantAmateur
• "Don't trust the sample manifests, [sic] genotype everything yourself." @constantAmateur
• "An hour eyeballing raw data can save 10 hours of coding." @ppgardne
• "If you are doing work for someone else, make sure you get them to write a project summary and sample list before you do [absolutely] anything. This will preemptively solve a lot of problems." @mdziemann
• "Be a good accountant" @watsonhaigh [i.e. Track your read counts] "Then ask, is that what I expected" ibid. Cor: This also applies to samples/processes and other countable.

Coding A.K.A mental health

• git
  • small commits
• If something else is published and it works, start there.
• Beware of ordered data structures: They might start at 0 or 1. (Read the documentation).
• Think well before using rm -rf, rename, or mv.
• Write tests for your code. So that you can trust basic functions
• Become an expert in one language and barely competent in the other ones you use.
• Factors (numerical representation of categories) are a necessary danger.
• "Installing software, making sure it works, creating pipelines etc. isn't the stuff you do to get to the bioinformatics. It is the bioinformatics. Doing it well is time well-spent, even if it can be frustrating" @jgreener64

Integrity

• Don't allow external pressure to turn you into a P-hacker.
• The perfect is the enemy of the good enough + delivered.
• You don't become a bioinformatician because you code but because you analyze. It's okay not to write tools. (See: learn Nextflow)
• Say no regularly.

Carrer

• In academia, you are evaluated on the writing you produce; in other spaces, the quality of your code might be important.
• Your career should fit your life, not the other way around.
• If you want to make money get out of academia.
• "Lab work sucks when you are pregnant. if you want to start a family and want flexibility, join us on the dark side of tech!" @Jillianerowe
• "Have your ideas and work in the open. [...] Demonstrate how you think through problems. Repost to other publications. [...]" @jillianerowe
• "Find your superpower and exploit it mercilessly" @jillianerowe
• "Never let your employer take over your life. Always have an exit plan." @jillianerowe
• For further advice on motherhood, fair labor, and life/work balance, see other replies by @jillianerowe

Resources:

• Statistical rethinking (FREE) https://xcelab.net/rm/statistical-rethinking/