uniprot SPLAT

We spent most of this morning working on our annotated star transcriptome in IPython, and I have to say it’s been a humbling experience. I could list off a series of unfortunate events between me and my Mac this morning, but I clearly had no idea what I was doing this morning in the midst of a flurried scripting extravaganza.

Some hard lessons learned from working on transcriptomics in computer lab today:

1) ASK/Google if you get stuck in the scripting. There are so many resources available (explainshell.com, wiki, forums, ROSALIND). Keeping this class blog and going over scripts that have been tweeted, screenshot, and posted has a huge help in retracking my steps to fix potential issues and definitely sells me on open network science.

2) Keep practicing!

3) Save your IPython notebook (which I thought I did on Day 1 but can’t find anywhere).

3) Get a good night’s sleep.

4) Keep a written list of commands until you know them. There are also some other tricks to helping keep track of your code such as ‘#’ for when you want to make a note in the middle of your script, and ‘!’ which always precedes a command (and you definitely don’t want to include a space).

Learning new languages is guaranteed to be difficult but I am amazed at how quickly it is to work with the thousands of contigs Steven assembled and can’t wait to start dipping our hands into looking at differential expression between our sea star samples. I’ve linked to a pdf to a 2012 Nature paper describing the use of software such as Cufflinks and TopHat (which you can use in Galaxy!) to assemble and analyze RNA-seq reads.

http://www.nature.com/nprot/journal/v7/n3/pdf/nprot.2012.016.pdf

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