HPG Aligner is an ultrafast and highly sensitive Next-Generation Sequencing (NGS) mapper which supoprts both DNA and RNA alignment
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README.md

HPG Aligner README

Welcome to HPG Aligner!

HPG Aligner is an ultrafast and highly sensitive Next-Generation Sequencing (NGS) read mapping.

COMPONENTS

hpg-aligner - The executable file to map DNA/RNA sequences

CONTACT

You can contact any of the following developers:

* Joaquín Tárraga (jtarraga@cipf.es)
* Héctor Martínez (martineh@icc.uji.es)
* Ignacio Medina (imedina@cipf.es)

DOWNLOAD and BUILDING

HPG Aligner has been opened to the community and released in GitHub, so you can download by invoking the following commands:

$ git clone https://github.com/opencb/hpg-aligner.git
Cloning into 'hpg-aligner'...
remote: Reusing existing pack: 1441, done.
remote: Total 1441 (delta 0), reused 0 (delta 0)
Receiving objects: 100% (1441/1441), 1.85 MiB | 122 KiB/s, done.
Resolving deltas: 100% (882/882), done.

$ cd hpg-aligner

$ git submodule update --init
Submodule 'lib/hpg-libs' (https://github.com/opencb/hpg-libs.git) registered for path 'lib/hpg-libs'
Cloning into 'lib/hpg-libs'...
remote: Reusing existing pack: 7735, done.
remote: Total 7735 (delta 0), reused 0 (delta 0)
Receiving objects: 100% (7735/7735), 26.82 MiB | 79 KiB/s, done.
Resolving deltas: 100% (4430/4430), done.
Submodule path 'lib/hpg-libs': checked out '962f531ef0ffa2a6a665ae6fba8bc2337c4351a9'

You can see the available HPG Aligner releases by using the command:

$ git tag
v2.0.0
v2.0.1

Select the HPG Aligner relesase you want to use, for instance,

$ git checkout v2.0.1

Before building the HPG Aligner, you must install on your system the following packages: git, gcc, scons, zlib, curl, xml, curses, gsl and check.

In Ubuntu and Debian distributions, you can install them by using theses commands:

$ apt-get install git 
$ apt-get install gcc
$ apt-get install scons
$ apt-get install zlib1g-dev libcurl4-gnutls-dev libxml2-dev libncurses5-dev libgsl0-dev check

And in Centos and Fedora distributions:

$ yum install git 
$ yum install gcc
$ yum install scons
$ yum install zlib-devel libcurl-devel libxml2-devel ncurses-devel gsl-devel check-devel

Finally, use Scons to build the HPG Aligner application:

$ scons

RUNING

For command line options invoke:

$ ./bin/hpg-aligner -h

In order to map DNA sequences:

1) Create the HPG Aligner index based on suffix arrays by invoking:

  $ ./bin/hpg-aligner build-sa-index -g <ref-genome-fasta-file> -i <index-directory>

  Example:

  $./bin/hpg-aligner build-sa-index -g /hpg/genome/human/GRCH_37_ens73.fa -i /tmp/sa-index-human73/ 

2) Map by invoking:

  $./bin/hpg-aligner dna -i <index-directory> -f <fastq-file> -o <output-directory>

  Example:

  $./bin/hpg-aligner dna -i /tmp/sa-index-human73/ -f /hpg/fastq/simulated/human/DNA/GRCh37_ens73/4M_100nt_r0.01.bwa.read1.fastq 
  ----------------------------------------------
  Loading SA tables...
  End of loading SA tables in 1.03 min. Done!!
  ----------------------------------------------
  Starting mapping...
  End of mapping in 1.40 min. Done!!
  ----------------------------------------------
  Output file        : hpg_aligner_output/alignments.sam
  Num. reads         : 4000000
  Num. mapped reads  : 3994693 (99.87 %)
  Num. unmapped reads: 5307 (0.13 %)
  ----------------------------------------------

In order to map RNA sequences:

A) Map Based on Burrows-Wheeler Transform method for slow memory consumption
   1) Create the BWT HPG Aligner index

     $ ./bin/hpg-aligner build-bwt-index -g <ref-genome-fasta-file> -i <index-directory> -r <compression-ratio>

 Example:

 $ ./bin/hpg-aligner build-bwt-index -g /home/user/Homo_sapiens.fa -i /home/user/INDEX/  -r 8

   2) Map by invoking:

     $ ./bin/hpg-aligner rna -i <index-directory> -f <fastq-file> -o <output-directory>

     Example:

     $ ./bin/hpg-aligner rna -i /home/user/INDEX/ -f reads.fq
     +===============================================================+
     |                           RNA MODE                            |
     +===============================================================+
     |      ___  ___  ___                                            |
     |    \/ H \/ P \/ G \/                                          |
     |    /\___/\___/\___/\                                          |
     |      ___  ___  ___  ___  ___  ___  ___                        |
     |    \/ A \/ L \/ I \/ G \/ N \/ E \/ R \/                      |
     |    /\___/\___/\___/\___/\___/\___/\___/\                      |
     |                                                               |
     +===============================================================+

     WORKFLOW 1
     [|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||]  100%

     WORKFLOW 2
     [|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||]  100%

     WORKFLOW 3
     [|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||]  100%

     +===============================================================+
     |        H P G - A L I G N E R    S T A T I S T I C S           |
     +===============================================================+
     |              T I M E    S T A T I S T I C S                   |
     +---------------------------------------------------------------+
      Loading time                            :  5.69 (s)
      Alignment time                          :  438.09 (s)
      Total time                              :  443.79 (s)
     +---------------------------------------------------------------+
     |            M A P P I N G    S T A T I S T I C S               |
     +---------------------------------------------------------------+
      Total reads process                     :  10000000
      Total reads mapped                      :  9977505 (99.78%)
      Total reads unmapped                    :  22495 (0.22%)
      Reads with a single mapping             :  9532383 (95.32%)
      Reads with multi mappings               :  445122 (4.45%)
      Reads mapped with BWT phase             :  6441691 (64.42%)
      Reads mapped in workflow 1              :  9737937 (97.38%)
      Reads mapped in workflow 2              :  149381 (1.49%)
      Reads mapped in workflow 3              :  90187 (0.90%)
     +---------------------------------------------------------------+
     |    S P L I C E    J U N C T I O N S    S T A T I S T I C S    |
     +---------------------------------------------------------------+
      Total splice junctions                  :  2556653
      Total cannonical splice junctions       :  2535847 (99.19%)
      Total semi-cannonical splice junctions  :  20806 (0.81%)
     +---------------------------------------------------------------+

B) Map Based on SA method for more speed

   1) Create the SA HPG Aligner index

     $ ./bin/hpg-aligner build-sa-index -g <ref-genome-fasta-file> -i <index-directory>

 Example:

 $./bin/hpg-aligner build-sa-index  -g /home/user/Homo_sapiens.fa -i /home/user/INDEX/

   2) Map by invoking:

     $ ./bin/hpg-aligner rna -i <index-directory> -f <fastq-file> -o <output-directory> --sa-mode

     Example:

     $ ./bin/hpg-aligner rna  -i /home/user/INDEX/ -f reads.fq -o /home/user/sa_output --sa-mode

DOCUMENTATION

You can find more info about HPG Aligner project at:

https://github.com/opencb/hpg-aligner/wiki