1. ElasticBLAST can handle much LARGER queries! 

ElasticBLAST can search query sets that have hundreds to millions of sequences and against BLAST databases of all sizes.

2. ElasticBLAST is FASTER

ElasticBLAST distributes your searches across multiple cloud instances to process them simultaneously. The ability to scale resources in this way allows you to process large numbers of queries in a shorter time than you could with BLAST+.

3. ElasticBLAST is EASY to run on the cloud

ElasticBLAST is easy to set up using our step-by-step instructions (Amazon Web Services (AWS), Google Cloud Platform (GCP)) and allows you to leverage the power of the cloud. Once configured, it manages the software and database installation, handles partitioning of the BLAST workload among the various instances, and deallocates cloud resources when the searches are done.

ElasticBLAST also selects the instance (i.e., machine) type for you based on database size. Of course, you can also choose the instance type manually if you prefer. 

Address of the bookmark: https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/

unzip blatSrc35.zip

to unpack the archive. This gives a directory blatSrc now move into that directory.

#cd blatSrc

before you begin read the README file that comes with the source code.

One thing about building blat is that you need to set the MACHTYPE variable so that the BLAT sources know what type of machine you are compiling the software on.

on most *nix machines, typing

echo $MACHTYPE

will return the machine architecture type.

On my CentOS 6 based system this gave:

x86_64-redhat-linux-gnu

However, what BLAT requires is the 'short value' (ie the first part of the MACHTYPE). To correct this, in the bash shell type (change this to the correct MACHTYPE for your system)

MACHTYPE=x86_64
export MACHTYPE

now running the command:

echo $MACHTYPE

should give the correct short form of the MACHTYPE:

x86_64

now create the directory lib/$MACHTYPE in the source tree. ie:

mkdir lib/$MACHTYPE

For my machine, lib/x86_64 already existed, so I did not have to do this, but this is not the case for all architectures.

The BLAT code assumes that you are compiling BLAT as a non-privileged (ie non-root) user. As a result, you must create the directory for the executables to go into:

mkdir ~/bin/$MACHTYPE

If you are installing as a normal user, edit your .bashrc to add the following (change the x86_64 to be your MACHTYPE):

export PATH=~/bin/x86_64::$PATH

For me, though, this was not good enough. I wanted the executables in /usr/local/bin where all my other code goes. As a result I did some hackery...

There is a master make template in the inc directory called common.mk and I edited this file with the command:

vi inc/common.mk

I replaced the line

    BINDIR=${HOME}/bin/${MACHTYPE}

with

    BINDIR=/usr/local/bin

saved and quit (as this is in my path, I do not need to do anything else)

All the preparation is now done and you can create the blat executables by going into the toplevel of the blat source tree (for me it was /usr/local/src/blat/blatSrc, but change to wherever you unpacked blat into).

Now simply run the command:

make

to compile the code.

Blat installed cleanly and the executables were all neatly placed in /usr/local/bin/x86_64, just like I wanted.

now simply running the command:

blat

on the command line gives me information on blat and sample usage.

Blat is installed and it's installed properly in my system code tree!!!

When the query file format is fasta, you can specify many threads to process it. It can reduce run time linearly, and use almost equal memory as the original lastz program. This is useful when you lastz a big query file to a huge reference like human whole genome sequence.

The program is an extension on the original lastz program which was written by Bob Harris (the LASTZ guy).

Address of the bookmark: https://github.com/jnarayan81/parallelLastz

Address of the bookmark: https://github.com/AdamCicherski/AlfaPang

Address of the bookmark: https://github.com/chilleo/ALPHA

Address of the bookmark: https://github.com/walaj/svaba

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

https://github.com/jkimlab/IMAP

Address of the bookmark: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0221858