3. Your data

You will use a dataset derived from sequencing of mRNA from zebrafish (Danio rerio) embryos in two different developmental stages. Sequencing was performed on the Illumina platform and generated 76bp pairedAend sequence data using polyAAselected RNA. Due to the time constraints of the practical you will only use a subset of the reads, but the steps that you are going to follow are the same whether you are using the whole data or only a subset. All the data can be found on the repository at the following path:

  /hove/vasilis/repository/...

(Original data can be found here: http://www.ebi.ac.uk/ena/data/view/ERR022484 and http://www.ebi.ac.uk/ena/data/view/ERR022485)

You need first to create a directory called zebrafish (or whatever other name you like) as a subdirectory of your home, to store all data and results. The sequencing data are in another folder in repository called data. You need to copy this folder into the folder you just created. In this tutorial, each step will be written explicitly, but you should be able by now to perform most of the above and the following steps using Unix commands, so try by yourself before resorting to help. Don’t worry about doing something wrong, you should not be able to do major damage. Below is the list of commands; as before, $ is the shell prompt, press enter after each command, and everything after the # is a comment ignored by the shell, and you don’t need to type it:

 # where are you
 # what’s in your home
 # create the work folder
 # and move into it
 $ pwd
 $ ls
 $ mkdir zebrafish
 $ cd zebrafish
 $ cp –r  /hove/vasilis/repository/.../tutorial/data .
 $ ls –l data              # what’s in the folder

Note that the –r parameter for the cp command instructs cp to copy entire folders. In the data folder you will find the following data files:

! 2cells_1.fastq and 2cells_2.fastq: these files are derived from pairedAend RNAAseq data of a 2Acell zebrafish embryo, the first file containing the forward reads and the other the reverse reads; ! 6h_1.fastq and 6h_2.fastq : these files are derived from pairedAend RNAAseq data of zebrafish embryos 6 hours postAfertilization, the first file containing the forward reads and the other the reverse reads.

 Let’s look at a fastq file. These are text files that you could open with any text editor (vi,  emacs,  pico,  etc.),  but  these  files  are  generally  so  large  that  trying  to  open  them  will  cause lots of problems. Remember the commands more (to look at a file one page at the  time), head (to see the file beginning) and tail (to see its end).  

In this case let’s look at the beginning of a fastq file with head <filename> (choose any file in the data folder). For example:

$ head data/2cells_1.fastq

 4 

By default, head shows the first ten rows of the file. How many complete reads are you looking at? Remember that in the fastq file each read is represented by four rows (read name, read sequence, spacer, phred scores). The file in the example contains the forward reads; let’s look at the corresponding reverse reads:

$ head data/2cells_2.fastq

Check the name of the first read in the forward file and of its mate in the corresponding reverse file. In the two files, paired reads occupy the same position within the files (e.g. the mate of the first read in the forward file is the first also in the reverse file, and so on). Hence, the two files must contain the same number of reads. How can you quickly known how many reads are in the fastq file? Run the following commands:

$ wc –l data/2cells_1.fastq $ wc –l data/2cells_2.fastq

You will get the number of rows in the two files, which must be identical. Dividing this number by four gives you the number of reads. If the number of rows in the forward and reverse files is not the same, and these numbers are not multiples of four, then something is very wrong. Can you tell which quality encoding our fastq formatted reads are in? Look at the first few reads of the file 2cells_1.fastq using the head command, and then compare the quality strings with the table found at:

http://en.wikipedia.org/wiki/FASTQ_format#Encoding

Knowing the encoding of the quality scores is required for some steps of the RNAAseq pipelines. Some tools can try to guess the encoding, while others require the user to specify it. The Wikipedia page provides lots of information on how the encodings changed over the years. You are now ready for checking how good your reads are.