Spack Set Up

Set Up

If you haven't already, log into sphinx. Stay in your home directory.

ssh [email protected]

Finding software

Many software packages are available through the spack system. Spack manages scientific software (usually pretty well but not always!) This is a way for a computer system to manage many software packages and make them available to users. For you to use spack, we need to add some information to a file in your home directory called .bash_profile. This is a hidden file that you normally don't have to worry about too much. It contains commands that run every time you log in, and it's usually for configurations.

To make spack work, we need to add a bit of code to this file.

cd ~
nano .bash_profile

(Need a nano review? Here's a video on how to use nano and covers the basics.)

To the bottom of the .bash_profile file, add:

export SPACK_ROOT=/pickett_shared/spack
PATH=$PATH:$HOME/bin:$SPACK_ROOT/bin
$SPACK_ROOT/share/spack/setup-env.sh

Now save and exit nano. For this session only (you don't ever have to do this again!) run this command:

source .bash_profile

This will happen automatically next time you log in.

You MAY get this error message:

==> Error: `spack load` requires spack's shell support.
 To set up shell support, run the command below for your shell.
 For bash/zsh/sh:
  . /pickett_shared/spack/share/spack/setup-env.sh
  
 For csh/tcsh:
  source /pickett_shared/spack/share/spack/setup-env.csh
  
 For fish:
  source /pickett_shared/spack/share/spack/setup-env.fish
  
 Or, if you do not want to use shell support, run one of these instead:
  
   eval `spack load --sh ` /mupb4i5 # bash/zsh/sh
   eval `spack load --csh ` /mupb4i5 # csh/tcsh
   eval `spack load --fish` /mupb4i5 # fish

In this case, follow the suggestion for sh and run:

. /pickett_shared/spack/share/spack/setup-env.sh

That should fix the problem.

Lets see what cool software we can play with! This is everything spack can install:

spack list

okay, but what is actually installed on sphinx?

spack find

We know we want blast, so we can ask if it is available directly

spack find blast

Okay, so it seems like it isn't installed, but that is misleading. We've chosen a difficult package to work with for your first package, but only because it is common and there are many versions. Let's do some digging

First, lets search for "spack blast". Yes, internet searches are your best friend when it comes to Linux.

Okay now lets look for blastplus: spack find blast-plus

Ug still not working but we're getting closer. We've got too many blast-plus installs! Let's try picking one specific package by using its "hash". Hashes must be preceded by a forward slash: spack load /ssttaxu

Is it working?

blastn -version
blastp -help

BLAST

BLAST is one of the most common bioinformatics tools out there, and many biology students learn to run BLAST for individual or small sets of sequences via a web interface: you can find a quick tutorial of web BLAST here. The most popular is probably NCBI BLAST. However, you can perform many more BLAST computations on an HPC.

BLAST has a number of possible programs to run depending on whether you have nucleotide or protein sequences:

• nucleotide query and nucleotide db - blastn
• nucleotide query and nucleotide db - tblastx (includes six frame translation of query and db sequences)
• nucleotide query and protein db - blastx (includes six frame translation of query sequences)
• protein query and nucleotide db - tblastn (includes six frame translation of db sequences)
• protein query and protein db - blastp

Get Data

Make a directory to hold our blast lesson.

	mkdir blast_examples
	cd blast_examples

Let's start by downloading some data. Grab some cow and human RefSeq proteins from NCBI:

	wget ftp://ftp.ncbi.nih.gov/refseq/B_taurus/mRNA_Prot/cow.1.protein.faa.gz
	wget ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/mRNA_Prot/human.1.protein.faa.gz

This is only part of the human and cow protein files. check out how big they are:

         ls -lh

Running BLAST - Protein vs Protein on the command line

Lets go back to our blast directory. The database files are both gzipped, so lets unzip them

        cd blast_examples
	gunzip *gz
	ls

Take a look at the head of each file:

	head cow.1.protein.faa 
	head human.1.protein.faa 

Note that the files are in fasta format, even though they end if ".faa" instead of the usual ".fasta". This NCBI's way of denoting that this is a fasta file with amino acids instead of nucleotides.

How many sequences are in each one?

	grep -c '^>' cow.1.protein.faa
	grep -c '^>' human.1.protein.faa 

---

Grep? Grep is short hand for "globally search a regular expression and print". So what exactly is a regular expression? Well, its a formal language to define search patterns. In this case, we are asking to search for lines that begin with (the ^ symbol) a greater than sign. Regular expressions (also known as regex) are extremely powerful, and if you are going to be doing a lot of file manipulation and data science, worth learning more about!

---

This grep command uses the c flag, which reports a count of lines with a match to the pattern. In this case, the pattern is a regular expression, meaning match only lines that begin with a >.

This is a bit too big, let's take a smaller set for practice. Let's take the first two sequences of the cow proteins, which we can see are on the first 6 lines

	head -n 16 cow.1.protein.faa > cow.small.faa

Now we can blast these two cow sequences against the set of human sequences. First, we need to tell blast about our database. BLAST needs to do some pre-work on the database file prior to searching. This helps to make the software work a lot faster. Use the makeblastdb command:

	makeblastdb -in human.1.protein.faa -dbtype prot
	ls

Note that this makes a lot of extra files, with the same name as the database plus new extensions (.pin, .psq, etc). To make blast work, these files, called index files, must be in the same directory as the fasta file.

Now we can run the blast job. We will use blastp, which is appropriate for protein to protein comparisons.

	blastp -query cow.small.faa -db human.1.protein.faa -out cow_vs_human_blast_results.txt

Take a look at the results in nano. Note that there can be more than one match between the query and the same subject. These are referred to as high-scoring segment pairs (HSPs).

Lets also take a look at the help pages. Unfortunately, there are no man pages (those are usually reserved for shell commands, but some software authors will provide them as well), but there is a text help output

	blastp -help

To scroll through slowly

	blastp -help | less

To quit the less screen, press the q key.

Parameters of interest: -evalue ( Default is 10?!?) and -outfmt

Lets get only very meaningful matches with a different output format:

	 blastp \
	 -query cow.small.faa \
	 -db human.1.protein.faa \
	 -out cow_vs_human_blast_results.tab \
	 -evalue .1 \
	 -outfmt 7

Whoa!? What are those slashes? In a practical sense, these tell the shell "I'm not done with this command yet". In a literal sense, the forward slash is an "escape" character, and it's telling the shell to interpret the newline as just a newline, not a "submit" of the command.

Check out the results with nano.

You MAY get an error message such as Segmentation fault when running nano. If so, load it from spack:

spack load nano

Tab-delimited has these default columns:

	qseqid 		Query sequence ID
	sseqid		Subject (ie DB) sequence ID
	pident		Percent Identity across the alignment
	length 		Alignment length
	mismatch 	# of mismatches
	gapopen 	Number of gap openings
	qstart 		Start of alignment in query
	qend 		End of alignment in query 
	sstart 		Start of alignment in subject
	send		End of alignment in subject
	evalue 		E-value
	bitscore	Bit score

I find it very useful to add the subject sequence description to the tabular output. If you go through help some more, you will find that you can decide which types of output you would like in the tab-delimited file and what order they should be in. For example:

	blastp \
	-query cow.small.faa \
	-db human.1.protein.faa \
	-out cow_vs_human_blast_results.tab \
	-evalue .1 \
	-outfmt "7 std stitle" 

Lets try a medium sized data set next

	head -n 500 cow.1.protein.faa > cow.medium.faa

What size is this db?

	grep -c '^>' cow.medium.faa 

Lets run the blast again, but this time lets not have the comment lines, and lets return only the best hit for each query.

	blastp \
	-query cow.medium.faa \
	-db human.1.protein.faa \
	-out cow_vs_human_blast_results.tab \
	-evalue .1 \
	-outfmt "6 std stitle" \
	-max_target_seqs 1

I picked "max target seqs" for a reason! It's quite a misleading flag - it finds the number of target sequences but they may not be the best matches. See this blog post for more details..

This is still very inefficient compared to what we can do. Lets imagine needing to BLAST 50,000 sequences against a database of 500,000 proteins (a surprisingly common task!). A single command can do that.