(Jetstream startup instructions HERE.)
---
You should now be logged into your Jetstream computer! You should see something like this:
ubuntu@ip-172-30-1-252:~$
this is the command prompt.
Run:
sudo apt-get -y update && \ sudo apt-get -y install trimmomatic fastqc python-pip \ samtools zlib1g-dev ncurses-dev python-dev
Install anaconda:
curl -O https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh bash Anaconda3-4.2.0-Linux-x86_64.sh
Then update your environment and install khmer:
source ~/.bashrc pip install -U setuptools pip install -U pip pip install -U Cython pip install https://github.com/dib-lab/khmer/archive/master.zip
Let's also run a Jupyter Notebook. First, configure it a teensy bit more securely, and also have it run in the background:
jupyter notebook --generate-config cat >>~/.jupyter/jupyter_notebook_config.py <<EOF c = get_config() c.NotebookApp.ip = '*' c.NotebookApp.open_browser = False c.NotebookApp.password = u'sha1:5d813e5d59a7:b4e430cf6dbd1aad04838c6e9cf684f4d76e245c' c.NotebookApp.port = 8000 EOF
Now, run!
jupyter notebook &
On Jetstream, you can get the Web page address by executing:
echo http://$(hostname):8000/
Note, the password is 'davis'.
Note
If your network blocks port 8000 (e.g. cruznet at UCSC), you can run:
ssh -N -f -L localhost:8000:localhost:8000 username@remotehost
to tunnel the remote Jupyter notebook server over SSH.
We're going to be using a subset of data from Hu et al., 2016. This paper from the Banfield lab samples some relatively low diversity environments and finds a bunch of nearly complete genomes.
(See DATA.html for a list of the data sets we're using in this tutorial.)
We've loaded subsets of the data onto an Amazon location for you, to
make everything faster for today's work. We're going to put the
files on your computer locally under the directory ~/data:
mkdir ~/data
Next, let's grab part of the data set:
cd ~/data curl -O -L https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/SRR1976948_1.fastq.gz curl -O -L https://s3-us-west-1.amazonaws.com/dib-training.ucdavis.edu/metagenomics-scripps-2016-10-12/SRR1976948_2.fastq.gz
Let's make sure we downloaded all of our data using md5sum.:
md5sum SRR1976948_1.fastq.gz SRR1976948_2.fastq.gz
You should see this:
37bc70919a21fccb134ff2fefcda03ce SRR1976948_1.fastq.gz 29919864e4650e633cc409688b9748e2 SRR1976948_2.fastq.gz
Now if you type:
ls -l
you should see something like:
total 346936 -rw-rw-r-- 1 ubuntu ubuntu 169620631 Oct 11 23:37 SRR1976948_1.fastq.gz -rw-rw-r-- 1 ubuntu ubuntu 185636992 Oct 11 23:38 SRR1976948_2.fastq.gz
These are 1m read subsets of the original data, taken from the beginning of the file.
One problem with these files is that they are writeable - by default, UNIX makes things writeable by the file owner. This poses an issue with creating typos or errors in raw data. Let's fix that before we go on any further:
chmod u-w *
We'll talk about what these files are below.
First, make a working directory; this will be a place where you can futz around with a copy of the data without messing up your primary data:
mkdir ~/work cd ~/work
Now, make a "virtual copy" of the data in your working directory by linking it in --
ln -fs ~/data/* .
These are FASTQ files -- let's take a look at them:
less SRR1976948_1.fastq.gz
(use the spacebar to scroll down, and type 'q' to exit 'less')
Question:
- where does the filename come from?
- why are there 1 and 2 in the file names?
Links:
We're going to use FastQC to summarize the data. We already installed 'fastqc' on our computer for you.
Now, run FastQC on two files:
fastqc SRR1976948_1.fastq.gz fastqc SRR1976948_2.fastq.gz
Now type 'ls':
ls -d *fastqc.zip*
to list the files, and you should see:
SRR1976948_1_fastqc.zip SRR1976948_2_fastqc.zip
Inside each of the fatqc directories you will find reports from the fastqc. You can download these files using your Jupyter Notebook console, if you like; or you can look at these copies of them:
Questions:
- What should you pay attention to in the FastQC report?
- Which is "better", file 1 or file 2? And why?
Links:
There are several caveats about FastQC - the main one is that it only calculates certain statistics (like duplicated sequences) for subsets of the data (e.g. duplicate sequences are only analyzed for the first
Now we're going to do some trimming! We'll be using Trimmomatic, which (as with fastqc) we've already installed via apt-get.
The first thing we'll need are the adapters to trim off:
curl -O -L http://dib-training.ucdavis.edu.s3.amazonaws.com/mRNAseq-semi-2015-03-04/TruSeq2-PE.fa
Now, to run Trimmomatic:
TrimmomaticPE SRR1976948_1.fastq.gz \
SRR1976948_2.fastq.gz \
SRR1976948_1.qc.fq.gz s1_se \
SRR1976948_2.qc.fq.gz s2_se \
ILLUMINACLIP:TruSeq2-PE.fa:2:40:15 \
LEADING:2 TRAILING:2 \
SLIDINGWINDOW:4:2 \
MINLEN:25
You should see output that looks like this:
... Input Read Pairs: 1000000 Both Surviving: 885734 (88.57%) Forward Only Surviving: 114262 (11.43%) Reverse Only Surviving: 4 (0.00%) Dropped: 0 (0.00%) TrimmomaticPE: Completed successfully
Questions:
- How do you figure out what the parameters mean?
- How do you figure out what parameters to use?
- What adapters do you use?
- What version of Trimmomatic are we using here? (And FastQC?)
- Do you think parameters are different for RNAseq and genomic data sets?
- What's with these annoyingly long and complicated filenames?
- why are we running R1 and R2 together?
For a discussion of optimal trimming strategies, see MacManes, 2014 -- it's about RNAseq but similar arguments should apply to metagenome assembly.
Links:
Run FastQC again on the trimmed files:
fastqc SRR1976948_1.qc.fq.gz fastqc SRR1976948_2.qc.fq.gz
And now view my copies of these files:
Let's take a look at the output files:
less SRR1976948_1.qc.fq.gz
(again, use spacebar to scroll, 'q' to exit less).
Questions:
- is the quality trimmed data "better" than before?
- Does it matter that you still have adapters!?
Optional: :doc:`kmer_trimming`
Next: :doc:`assemble`