Simple mRNAseq: mapping reads to gene sets and doing quantile normalization

We’re going to walk through a real, although simple, mRNAseq analysis for lamprey. This is probably truer-to-life for many of you than using something like mouse: lamprey is an “emerging model organism”, so the genome is not yet available, and we don’t have a good gene set for lamprey. So we’re going to have to do it all from scratch!

So, let’s do this!

Start up a new EC2 server according to Renting a computer from Amazon. (You can use either a small or a large EC2 instance (as in Short Read Assembly). Also install the bowtie mapping software based on the instructions at the top of Mapping with bowtie.

Extracting counts from mRNAseq data

The first step in our simplified mRNAseq analysis is to map all of the reads to some set of mRNA sequences. This can be whatever cDNA set you have, even something assembled from your existing mRNAseq; we’ll show you how to connect it to annotations a bit later.

Downloading the data sets

Download the mRNA reference set and the first two mRNAseq data sets for lamprey from the private data set page and uncompress them:

%% mkdir /mnt/lamprey
%% cd /mnt/lamprey
(put curl commands here)

%% gunzip *.gz

You should now have three files in /mnt/lamprey:

%% ls

should give you

lamprey-mrnaseq-a.fa  lamprey-mrnaseq-b.fa  lamprey-rnatigs.fa

Mapping

Now, build the bowtie index for the lamprey mRNAseq reference set:

%% bowtie-build lamprey-rnatigs.fa lamprey-rnatigs

Run the mapping:

%% bowtie -p 2 -f lamprey-rnatigs lamprey-mrnaseq-a.fa > lamprey-mrnaseq-a.map
%% bowtie -p 2 -f lamprey-rnatigs lamprey-mrnaseq-b.fa > lamprey-mrnaseq-b.map

Here the ‘-p 2’ should only be used if you have a large (two-core) instance, and ‘-f’ is because these mRNAseq data files are in FASTA format rather than FASTQ (bowtie’s default assumption).

Counting

Now we want to count the number of times each contig in the mRNA reference set had a gene mapped to it. To do this, we can use the ‘count-genes’ script in the ngs-course scripts; you can look at the script here. To run it, just feed it the mapping file and redirect the output:

%% python ~/ngs-course/mrnaseq/count-genes.py lamprey-mrnaseq-a.map > lamprey-mrnaseq-a.count
%% python ~/ngs-course/mrnaseq/count-genes.py lamprey-mrnaseq-b.map > lamprey-mrnaseq-b.count

If you look at these files, you will see a bunch of lines like this:

2 E90KYJQ01DDEO8
137 CL1Contig379
117 CL1Contig376
2 CL1Contig370
382 CL697Contig1

where the first number in each line represents the number of times the contig name (second field) is seen.

Quantile normalization

Our analysis script, above, took the mRNAseq mapping and output a file that contained a set of counts for each gene or rnatig. In order to compare gene counts across data sets, we need to normalize them. This eliminates variability due to different amounts of input mRNA, different numbers of total reads, etc.

Quantile normalization is a procedure that redistributes the data in one data set to match the distribution of another data set. There’s a simple script to do this in the ngs-course repository, under ngs-course/mrnaseq/, file ‘qnormalize.py’ (view online). I’ve also included two example data files, example-data-1.txt and example-data-2.txt.

To run the script on these example data files, type:

%% python ~/ngs-course/mrnaseq/qnormalize.py ~/ngs-course/mrnaseq/example-data-{1,2}.txt

You should see

0 d
5 b
10 a
15 f
20 c

which represents the newly normalized second data set. Here, gene ‘d’ has prevalence 0 because it’s not present at all in the second data set. The other genes have been ordered from lowest to highest expression level from data set 2, and then reassigned the expression numbers from data set 1.

Try running this on your counts from the lamprey mRNAseq, above, and plot the new distribution:

%% python ~/ngs-course/mrnaseq/qnormalize.py lamprey-mrnaseq-{a,b}.count > lamprey-mrnaseq-b.norm.count
%% python ~/ngs-course/mrnaseq/plot-counts.py lamprey-mrnaseq-a.count lamprey-mrnaseq-b.norm.count

You should get a file ‘counts.png’ that looks like this:

../_images/mrnaseq-counts.png

Not terribly informative yet, but a step in the right direction – now you can do some simple expression analysis!

Naive expression analysis

Likit will show you a better way to do expression analysis, but I always like doing a rough cut first. A simple way to do this is in the plot-ratio-dist.py script, which takes the log ratio of counts adjusted to avoid divide-by-zero and low-count problems. Run it like so:

%% python ~/ngs-course/mrnaseq/plot-ratio-dist.py lamprey-mrnaseq-a.count lamprey-mrnaseq-b.norm.count

This will create a file ‘ratio-dist.png’ that looks like this:

../_images/mrnaseq-ratio-dist-1.png

This distribution is the distribution of log2 ratios of gene expression counts, so a value of 0 means no change, a value of 1 means a ratio of 2, etc.

The challenge now is that of genes with identical expression patterns dominate – hence the massive spike at 0! If you adjust the axes by uncommenting the second-to-last line (the ‘axis’ line) in the script ~/ngs-course/mrnaseq/plot-ratio-dist.py you can see the distribution of changed expression levels more clearly:

../_images/mrnaseq-ratio-dist-2.png

Et voila! You can apply your cutoff as you wish, and you have your genes for further investigation.

Concluding comments

We’ll show you how to examine putative function by connecting genes to mouse proteins and to GO in a later tutorial.

A challenge for you

There are four lamprey mRNAseq data sets available here – a, b, c, and d. Two are from one tissue, and two are from a different tissue. Which data sets are from the same tissue?


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