## Sequencing depth and number of samples¶

Hart et al. (2013) provides a nice description and a set of tools for estimating your needed sequencing depth and number of samples. They provide an Excel based calculator for calculating number of samples. Their numbers are surprisingly large to me ;).

In a proposal for an exploratory effort to discover differentially expressed genes, I would suggest 3-5 biological replicates with 30-50 million reads each. More reads is usually cheaper than more replicates, so 50-100m reads may give you more power to resolve smaller fold changes.

If you do your sequencing at the MSU Core Facility, you’ll get an e-mail from them when you’re samples are ready. The e-mail will give you an FTP site, a username, and a password, as well as a URL. You can use these to download your data. For example, if you get:

hostname:       titan.bch.msu.edu

URI:            ftp://rnaseqmodel:QecheJa6@titan.bch.msu.edu


In this case, you will see a ‘testdata’ directory. If you click on that, you’ll see a bunch of fastq.gz files. These are the files that you want to get onto the HPC.

To download these files onto the HPC, log into the HPC, go to the directory on the HPC you want to put the files in, and run a ‘wget’ – for example, on the HPC:

mkdir ~/testdata
cd ~/testdata

wget -r -np -nH ftp://rnaseqmodel:QecheJa6@titan.bch.msu.edu/testdata/


This will download _all_ of the files in that directory. You can also do them one at a time, e.g. to get ‘Ath_Mut_1_R1.fastq.gz’, you would do

Even if all you plan to do is change the filenames you’re operating on, you’ll need to develop your own analysis pipeline. Here are some tips.

1. Start with someone else’s approach; don’t design your own. There are lots of partly done examples that you can find on the Web, including in this tutorial.
2. Generate a data subset (the first few 100k reads, for example).
1. Run commands interactively on an HPC dev node until you get all of the commands basically working; track all of your commands in a Word document or some such.
2. Once you have a set of commands that seems to work on small data, write a script. Run the script on the small data again; make sure that works.
3. Turn it into a qsub script (making sure you’re in the right directory, have the modules loaded, etc.)
4. Make sure the qsub script works on your same small data.
5. Scale up to a big test data set.
6. Once that’s all working, SAVE THE SCRIPT SOMEWHERE. Then, edit it to work on all your data sets (you may want to make subsets again, as much as possible).
7. Provide your scripts and raw counts files as part of any publication or thesis, perhaps via figshare.

Next: More resources

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