get_homologues-est manual

Bruno Contreras-Moreira (1) and Pablo Vinuesa (2)
1. Estación Experimental de Aula Dei-CSIC
2. Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México


1 Description

This document describes GET_HOMOLOGUES-EST, a fork of get_homologues for clustering homologous gene/transcript sequences of strains/populations of the same species. The source code and issue manager can be found at This algorithm has been designed and tested with plant transcripts and CDS sequences, and uses BLASTN to compare DNA sequences. The main tasks for which this was conceived are:

The core algorithms of get_homologues-est have been adapted from get_homologues, and are therefore explained in manual_get_homologues.pdf. This document focuses mostly on EST-specific options.

When obtaining twin DNA and peptide CDS files, the output of GET_HOMOLOGUES-EST can be used to drive phylogenomics and population genetics analyses with the kin pipeline GET_PHYLOMARKERS.

This table lists features developed for get_homologues-est which were not available in the original get_homologues release, although most have been backported.

Table 1: List of novel scripts/features in get_homologues-est.
name description Script to extract coding sequences CDS from raw transcripts by combining Transdecoder and BLASTX.
redundant isoform calling get_homologues-est can handle redundant isoforms which otherwise will degrade clustering performance.
ANI matrices get_homologues-est can compute Average Nucleotide Identity (ANI) matrices which summarize the genetic distance among input genotypes. Produces a non-redundant pangenome matrix by comparing all nucleotide/peptide clusters to each other. Script to test whether a set of sequence clusters are enriched in some Pfam domains. Produces a multiple alignment view of the supporting local BLAST alignments of sequences in a cluster. It can also annotate Pfam domains and find private sequence variants private to an arbitrary group of sequences.

2 Requirements and installation is a Perl5 program bundled with a few binary files. The software has been tested on 64-bit Linux boxes, and on Intel MacOSX systems. Therefore, a Perl5 interpreter is needed to run this software, which is usually installed by default on these operating systems.

In order to install and test this software please follow these steps:

  1. Download a bundled release from
  2. Unpack the software with: $ tar xvfz get_homologues_X.Y.tgz
  3. $ cd get_homologues_X.Y
  4. $ perl
    Please follow the indications in case some required part is missing.

  5. Type $ ./ -v which will tell exactly which features are available.
  6. Test the main Perl script, named, with the included sample input folder

    sample_transcripts_fasta by means of the instruction:
    $ ./ -d sample_transcripts_fasta . You should get an output similar to the contents of file sample_transcripts_output.txt.

  7. Optionally modify your $PATH environment variable to include Please copy the following lines to the .bash_profile or .bashrc files, found in your home directory, replacing [INSTALL_PATH] by the full path of the installation folder:
    export GETHOMS=[INSTALL_PATH]/get_homologues_X.Y
    export PATH=${GETHOMS}/:${PATH}
    This change will be effective in a new terminal or after running: $ source ~/.bash_profile

If you prefer a copy of the software that can be updated in the future you can get it from the GitHub repository with:

  1. $ git clone
  2. $ perl
    You would then be able to update it at anytime with:
  3. $ cd get_homologues
  4. $ git pull

Finally, you can also install the software from bioconda as follows:

$ conda activate bioconda
$ conda create -n get_homologues -c conda-forge -c bioconda get_homologues
$ conda activate get_homologues

# only if you want to install Pfam or SwissProt db
$ perl

The rest of this section might be safely skipped if installation went fine, it was written to help solve installation problems.

2.1 Perl modules

A few Perl core modules are required by the script, which should be already installed on your system: Cwd, FindBin, File::Basename, File::Spec, File::Temp, FileHandle, List::Util, Getopt::Std, Benchmark and Storable.

In addition, the Bio::Seq, Bio::SeqIO, Bio::Graphics and Bio::SeqFeature::Generic modules from the Bioperl collection, and modules Parallel::ForkManager, URI::Escape are also required, and have been included in the get_homologues-est bundle for your convenience.

Should this version of BioPerl fail in your system (as diagnosed by it might be necessary to install it from scratch. However, before trying to download it, you might want to check whether it is already living on your system, by typing on the terminal:
$ perl -MBio::Root::Version -e 'print $Bio::Root::Version::VERSION'

If you get a message Can't locate Bio/Root/Version... then you need to actually install it, which can sometimes become troublesome due to failed dependencies. For this reason usually the easiest way of installing it, provided that you have root privileges, it is to use the software manager of your Linux distribution (such as synaptic/apt-get in Ubuntu, yum in Fedora or YaST in openSUSE). If you prefer the terminal please use the cpan program with administrator privileges (sudo in Ubuntu):
$ cpan -i C/CJ/CJFIELDS/BioPerl-1.6.1.tar.gz

This form should be also valid:
$ perl -MCPAN -e 'install C/CJ/CJFIELDS/BioPerl-1.6.1.tar.gz'
Please check this tutorial if you need further help.

2.2 Required binaries

In order to properly read (optionally) compressed input files, get_homologues-est requires gunzip and bunzip2, which should be universally installed on most systems.

The Perl script, already mentioned in section 2, checks whether the included precompiled binaries for hmmer, MCL and BLAST are in place and ready to be used by get_homologues-est. This includes also COGtriangles, which is used only by prokaryotic get_homologues. However, if any of these binaries fails to work in your system, perhaps due a different architecture or due to missing libraries, it will be necessary to obtain an appropriate version for your system or to compile them with your own compiler.

In order to compile MCL the GNU gcc compiler is required, although it should most certainly already be installed on your system. If not, you might install it by any of the alternatives listed in section 2.1. For instance, in Ubuntu this works well: $ sudo apt-get install gcc . The compilation steps are as follows:

$ cd bin/mcl-14-137;
$ ./configure`;
$ make

Regarding BLAST, get_homologues-est uses BLAST+ binaries, which can be easily downloaded from the NCBI FTP site. The packed binaries are blastp and makeblastdb from version ncbi-blast-2.14.0+. If these do not work in your machine or your prefer to use older BLAST versions, then it will be necessary to edit file lib/ First, environmental variable $ENV{'BLAST_PATH'} needs to be set to the right path in your system (inside subroutine sub set_phyTools_env).
Variables $ENV{'EXE_BLASTP'} and $ENV{'EXE_FORMATDB'} also need to be changed to the appropriate BLAST binaries, which are respectively blastall and formatdb.

2.3 Optional software dependencies

It is possible to make use of get_homologues-est on a high-performance computing (HPC) cluster invoking the program with option -m cluster. Three job managers are currently supported: gridengine, LSF and Slurm.

By default a gridengine cluster is expected. In particular we have tested this feature with versions GE 6.0u8, 6.2u4, 2011.11p1. For this to work in your environment you might need to create a file named HPC.conf in the same location as tayloring your queue configuration and paths. The default values can be inspected at module lib/ To find out the installation path of your SGE installation you might try the next terminal command: $ which qsub
In case you have access to a multi-core computer you can follow the next steps to set up your own Grid Engine cluster and speed up your calculations:

### Debian 11 install (updated 08112021)
### (explained in Spanish at 

   # this also creates user sgeadmin
   sudo apt install gridengine-master gridengine-qmon gridengine-exec

   # edit /etc/hosts localhost.localdomain localhost master master myhost
   # give yourself provileges
   sudo -u sgeadmin qconf -am myuser

   # and to a userlist
   qconf -au myuser users

   # Add a submission host
   qconf -as myhost

   # Add an execution host, you will be prompted for information about the execution host
   qconf -ae
   # Add a new host group
   qconf -ahgrp @allhosts

   # Add the exec host to the @allhosts list
   qconf -aattr hostgroup hostlist myhost @allhosts

   # Add and configure queue, set the slots matching your CPU/cores
   qconf -aq all.q

   # Add the host group to the queue
   qconf -aattr queue hostlist @allhosts  all.q

   # Make sure there is a slot allocated to the execd
   qconf -aattr queue slots "[myhost=1]" all.q

### Ubuntu install from SourceForge (updated 08112021)

# 1) go to , 
# create user 'sgeadmin' and download the latest binary packages
# (Debian-like here) matching your architecture (amd64 here):

wget -c
wget -c
wget -c

sudo useradd sgeadmin
sudo dpkg -i sge-common_8.1.9_all.deb 
sudo dpkg -i sge_8.1.9_amd64.deb
sudo dpkg -i sge-dbg_8.1.9_amd64.deb
sudo apt-get install -f

# 2) set hostname to anything but localhost by editing /etc/hosts so that 
# the first line is something like this (localhost or 127.0.x.x IPs not valid):
#   yourhost

# 3) install Grid Engine server with defaults except cluster name ('yourhost') 
# and admin user name ('sgeadmin'):
sudo su
cd /opt/sge/
chown -R sgeadmin sge
chgrp -R sgeadmin sge

# 4) install Grid Engine client with all defaults:

# 5) check the path to your sge binaries, which can be 'lx-amd64'
ls /opt/sge/bin

# 6) Set relevant environment variables in /etc/bash.bashrc [can also be named /etc/basrhc] 
# or alternatively in ~/.bashrc for a given user
export SGE_ROOT=/opt/sge
export PATH=$PATH:"$SGE_ROOT/bin/lx-amd64" 

# 7) Optionally configure default all.q queue:
qconf -mq all.q

# 8) Add your host to list of admitted hosts:
qconf -as yourhost

If your computer farm is managed by LSF, you should create a file named HPC.conf modifiying the provided template sample.HPC.conf and adding a full path if necessary:

# cluster/farm configuration file, edit as needed (use spaces or tabs)
# comment lines start with #
# PATH might be empty or set to a path/ ending with '/', example:
#PATH	/lsf/10.1/linux3.10-glibc2.17-x86_64/bin/
TYPE	lsf
CHKEXE	bjobs
DELEXE	bkill
If your computer farm is managed by Slurm instead, you should create a configuration file named HPC.conf similar to this:
TYPE	slurm
SUBEXE	sbatch
CHKEXE	squeue
DELEXE	scancel

For cluster-based operations three bundled Perl scripts are invoked:, and .

It is also possible to invoke Pfam domain scanning from get_homologues-est. This option requires the bundled binary hmmscan, which is part of the HMMER3 package, whose path is set in file lib/ (variable $ENV{'EXE_HMMPFAM'}). Should this binary not work in your system, a fresh install might be the solution, say in /your/path/hmmer-3.1b2/. In this case you'll have to edit file lib/ and modify the relevant:

if( ! defined($ENV{'EXE_HMMPFAM'}) )
	$ENV{'EXE_HMMPFAM'} = '/your/path/hmmer-3.1b2/src/hmmscan --noali --acc --cut_ga '; 
The Pfam HMM library is also required and the script should take care of it. However, you can manually download it from the appropriate Pfam FTP site. This file needs to be decompressed, either in the default db folder or in any other location, and then it should be formatted with the program hmmpress, which is also part of the HMMER3 package. A valid command sequence could be:
$ cd db;
$ wget .;
$ gunzip Pfam-A.hmm.gz;
$ /your/path/hmmer-3.1b2/src/hmmpress Pfam-A.hmm
Finally, you'll need to edit file lib/ and modify the relevant line to:
if( ! defined($ENV{"PFAMDB"}) ){ $ENV{"PFAMDB"} = "db/Pfam-A.hmm"; }

In order to reduce the memory footprint of get_homologues-est it is possible to take advantage of the Berkeley_DB database engine, which requires Perl core module DB_File, which should be installed on all major Linux distributions. If DB_File is not found within a conda environment you might have to run conda deactivate before. Should manual installation be required, this can be done as follows:

$ yum -y install libdb-devel         # Redhat and derived distros

$ sudo apt-get -y install libdb-dev  # Ubuntu/Debian-based distros, and then cpan below

$ cpan -i DB_File                    # requires administrator privileges (sudo)

The accompanying script should work out of the box, but the more efficient requires the installation of module Inline::CPP, which in turn requires Inline::C and g++, the GNU C++ compiler. The installation of these modules is known to be troublesome in some systems, but the standard way should work in most cases:

$ yum -y install gcc-c++ perl-Inline-C perl-Inline-CPP  # Redhat and derived distros

$ sudo apt-get -y install g++             # Ubuntu/Debian-based distros, and then cpan below

$ cpan -i Inline::C Inline::CPP           # will require administrator privileges (sudo)

This script may optionally use Diamond instead of BLASTX. The bundled linux binary should work out of the box; in case the macOS binary does not work in your system you might have to re-compile it with:

cd bin/diamond-0.8.25/
cd ../../..

The accompanying scripts,,,, require the installation of the statistical software R, which usually is listed by software managers in all major Linux distributions. In some cases (some SuSE versions and some Redhat-like distros) it will be necessary to add a repository to your package manager. R can be installed from the terminal:

$ sudo apt-get -y install r-base r-base-dev      # Ubuntu/Debian-based distros

$ yum -y install R                               # RedHat and derived distros

$ zypper --assume-yes R-patched R-patched-devel  # Suse

Please visit CRAN to download and install R on macOSX systems, which is straightforward.

In addition to R itself, and require some R packages to run, which can be easily installed from the R command line with:

> install.packages(c("ape", "gplots", "cluster", "dendextend, "factoextra"), dependencies=TRUE)

Finally, the script might require the installation of program PARS from the PHYLIP suite, which should be already bundled with your copy of get_homologues.

3 User manual

This section describes the available options for the get_homologues-est software.

3.1 Input data

This program takes input sequences in FASTA format, which might be GZIP- or BZIP2-compressed, contained in a directory or folder containing several files with extension '.fna', which can have twin .faa files with translated amino acid sequences for the corresponding CDSs (expected to be in same order). File names matching the tag 'flcdna' are handled as full-length transcripts, and this information will be used downstream in order to estimate coverage. Global variable $MINSEQLENGTH controls the minimum length of sequences to be considered; the default value is 20.

3.2 Program options

Typing $ ./ -h on the terminal will show the basic options:

-v print version, credits and checks installation
-d directory with input FASTA files (.fna , optionally .faa),  (use of pre-clustered sequences
   1 per sample, or subdirectories (subdir.clusters/subdir_)    ignores -c)
   with pre-clustered sequences (.faa/.fna ). Files matching
   tag 'flcdna' are handled as full-length transcripts.
   Allows for files to be added later.
   Creates output folder named 'directory_est_homologues'

Optional parameters:
-o only run BLASTN/Pfam searches and exit                      (useful to pre-compute searches)
-i cluster redundant isoforms, including those that can be     (min overlap, default: -i 40,
   concatenated with no overhangs, and perform                  use -i 0 to disable)
   calculations with longest
-c report transcriptome composition analysis                   (follows order in -I file if enforced,
                                                                with -t N skips clusters occup<N [OMCL],
                                                                ignores -r,-e)
-R set random seed for genome composition analysis             (optional, requires -c, example -R 1234)
-m runmode [local|cluster|dryrun]                              (default: -m local)
-n nb of threads for BLASTN/HMMER/MCL in 'local' runmode       (default=2)
-I file with .fna files in -d to be included                   (takes all by default, requires -d)

Algorithms instead of default bidirectional best-hits (BDBH):
-M use orthoMCL algorithm (OMCL, PubMed=12952885)

Options that control sequence similarity searches:
-C min %coverage of shortest sequence in BLAST alignments      (range [1-100],default: -C 75)
-E max E-value                                                 (default: -E 1e-05 , max=0.01)
-D require equal Pfam domain composition                       (best with -m cluster or -n threads)
   when defining similarity-based orthology
-S min %sequence identity in BLAST query/subj pairs            (range [1-100],default: -S 95 [BDBH|OMCL])
-b compile core-transcriptome with minimum BLAST searches      (ignores -c [BDBH])

Options that control clustering:
-t report sequence clusters including at least t taxa          (default: t=numberOfTaxa,
                                                                t=0 reports all clusters [OMCL])
-L add redundant isoforms to clusters                          (optional, requires -i)
-r reference transcriptome .fna file                           (by default takes file with
                                                                least sequences; with BDBH sets
                                                                first taxa to start adding genes)
-e exclude clusters with inparalogues                          (by default inparalogues are
-F orthoMCL inflation value                                    (range [1-5], default: -F 1.5 [OMCL])
-A calculate average identity of clustered sequences,          (optional, creates tab-separated matrix,
 uses blastn results                                            [OMCL])
-P calculate percentage of conserved sequences (POCS),         (optional, creates tab-separated matrix,
 uses blastn results, best with CDS                             [OMCL])
-z add soft-core to genome composition analysis                (optional, requires -c [OMCL])

Figure 1: Flowchart of get_homologues-est.
Image flow-est

The only required option is -d, which indicates an input folder, as seen in section 3.1. It is important to remark that in principle only files with extensions .fna / .fa / .fasta and optionally .faa are considered when parsing the -d directory. By using .faa input files protein sequences can be used to scan Pfam domains and included in output clusters.

The use of an input folder or directory (-d) is recommended as it allows for new files to be added there in the future, reducing the computing required for updated analyses. For instance, if a user does a first analysis with 5 input genomes today, it is possible to check how the resulting clusters would change when adding an extra 10 genomes tomorrow, by copying these new 10 .fna input files to the pre-existing -d folder, so that all previous BLASTN searches are re-used.

All remaining flags are options that can modify the default behavior of the program, which is to use the bidirectional best hit algorithm (BDBH) in order to compile clusters of potential orthologous DNA sequences, taking the smallest genome as a reference. By default nucleotide sequences are used to guide the clustering, thus relying on BLASTN searches.

Perhaps the most important optional parameter would be the choice of clustering algorithm (Table 2):

Table 2: List of available clustering algorithms. Note that the COG triangles algorithm is not supported.
name option  
BDBH default Starting from a reference genome, keep adding genomes stepwise while storing the sequence clusters that result of merging the latest bidirectional best hits.
OMCL -M OrthoMCL v1.4, uses the Markov Cluster Algorithm to group sequences, with inflation (-F) controlling cluster granularity, as described in PubMed=12952885.

The remaining options are now reviewed:

3.3 Accompanying scripts

The following Perl and shell scripts are included in each release to assist in the interpretation of results generated by See examples of use in manual_get_homologues.pdf manual_get_homologues.pdf:

Apart from these, auxiliar script is bundled to assist in the analysis of transcripts. In particular, this script can be used to annotate potential Open Reading Frames (ORFs) contained within raw transcripts, which might be truncated or contain introns. This script uses TransDecoder, BLASTX and SWISSPROT, which should be installed by running: $ ./$

Ths program supports the following options:

usage: ./ [options] <input FASTA file(s) with transcript nucleotide sequences>

-h this message
-p check only 'plus' strand                                  (optional, default both strands)
-l min length for CDS                                        (optional, default=50 amino acid residues)
-g genetic code to use during translation                    (optional, default=1, example: -g 11)
-d run blastx against selected protein FASTA database file   (default=swissprot, example: -d db.faa)
-E max E-value during blastx search                          (default=1e-05)
-n number of threads for BLASTX jobs                         (default=2)
-X use diamond instead of blastx                             (optional, much faster for many sequences)

-G show available genetic codes and exit

The main output of this script are two files, as shown in section 4.1, which contain inferred nucleotide and peptide CDS sequences. These FASTA files contain in each header the evidence supporting each called CDS, which can be blastx, transdecoder or a combination of both, giving precedence to blastx in case of conflict. Note that we have observed that the output of TransDecoder might change if a single sequence is analyzed alone, in contrast to the analysis of a batch of sequences. The next table shows the rules and evidence codes used by this script in order to call CDS sequences by merging BLASTX (1) and TransDecoder (2) predictions. The rules are mutually exclusive and are tested hierarchically from top to bottom. Sequences from 1 and 2 with less than 90 consecutive matches (30 amino acid residues) are considered to be non-overlapping (last rule). Note that the occurrence of mismatches are checked as a control:

graphical summary         evidence             description

  1---------- 	           blastx	             no transdecoder	

  2----------	          transdecoder	         no blastx	

1----------          blastx.transdecoder       inferred CDS overlap with no 
     2-----------                              mismatches and are concatenated
     1----------     transdecoder.blastx       inferred CDS overlap with no 
2-----------                                   mismatches and are concatenated

1-----------------   blastx<transdecoder       blastx CDS includes transdecoder CDS
     1----------     transdecoder<blastx       transdecoder CDS includes blastx CDS

1--------C--         blastx-mismatches         blastx CDS is returned as sequences  
     2---T------                               have mismatches

1-----               blastx-noover             blastx CDS is returned as transdecoder 
        2---                                   CDS does not overlap

Our benchmarks suggest that 78 to 92% of deduced CDS sequences match the correct peptide sequences. :

Table 3: Fraction of correct peptide sequences in deduced CDS obtained by combining BLASTX and TransDecoder.
evidence Arabidopsis thaliana [Col-0] n Hordeum vulgare [Haruna Nijo] n
blastx 0.787 960 0.654 1,657
transdecoder 0.914 8,194 0.662 9,026
blastx.transdecoder 0.930 4,678 0.843 5,939
transdecoder.blastx 0.959 15,700 0.859 8,903
blastx<transdecoder 0.620 324 0.674 218
transdecoder<blastx 0.966 6,581 0.872 11,999
blastx-mismatches 0 1   0
blastx-noover 0.232 835 0.426 2,211
overall 0.923   0.783  

The results obtained with DIAMOND instead of BLASTX are very similar:

Table 4: Fraction of correct peptide sequences in deduced CDS obtained by combining DIAMOND and TransDecoder.
evidence Arabidopsis thaliana [Col-0] n Hordeum vulgare [Haruna Nijo] n
blastx 0.800 929 0.655 1,598
transdecoder 0.914 8,166 0.663 8,980
blastx.transdecoder 0.929 4,685 0.844 5,951
transdecoder.blastx 0.958 15,698 0.859 8,890
blastx<transdecoder 0.615 325 0.671 216
transdecoder<blastx 0.967 6,583 0.872 11,999
blastx-mismatches   0   0
blastx-noover 0.270 833 0.452 2,190

4 A few examples

This section presents typical ways of running and the accompanying scripts with provided sample input data. Please check file manual_get_homologues.pdf for more examples, particularly for the auxiliary scripts, which are not explained in this document.

4.1 Extracting coding sequences (CDS) from transcripts

This example takes the provided sample file sample_transcripts.fna to demonstrate how to annotate coding sequences contained in those sequences by calling Note that is significantly faster, but requires an optional Perl module (see 2.3).

This is an optional pre-processing step which you might not want to do, as the software should be able to properly handle any nucleotides sequences suitable for BLASTN. However, coding sequences have the advantage that can be translated to amino acids and thus used to scan Pfam domains further down in the analysis (see option -D).

A simple command would be, which will discard sequences less than 50b long, and will aligned them to SWISSPROT proteins in order to annotate coding regions. In case of overlap, Transdecoder-defined and BLASTX-based coding regions are combined provided that a $MINCONOVERLAP=90 overlap, with no mismatches, is found; otherwise the latter are given higher priority:

./ -n 10 sample_transcripts.fna

The output should look like this (contained in file sample_transcripts_output.txt):

# ./ -p 0 -m  -d /path/get_homs-est/db/swissprot -E 1e-05 -l 50 -g 1 -n 10 -X 0
# input files(s):
# sample_transcripts.fna

## processing file sample_transcripts.fna ...
# running transdecoder...

# parsing transdecoder output (_sample_transcripts.fna_l50.transdecoder.cds.gz) ...
# running blastx...
# parsing blastx output (_sample_transcripts.fna_E1e-05.blastx.gz) ...
# calculating consensus sequences ...
# input transcripts = 9
# transcripts with ORFs = 7
# transcripts with no ORFs = 2
# output files: sample_transcripts.fna_l50_E1e-05.transcript.fna , 
# sample_transcripts.fna_l50_E1e-05.cds.fna , 
# sample_transcripts.fna_l50_E1e-05.cds.faa , 
# sample_transcripts.fna_l50_E1e-05.noORF.fna

The resulting CDS files can then be analyzed with

Apart from the listed output files, which include translated protein sequences, temporary files are stored in the working directory, which of course can be removed, but will be re-used if the same job is re-run later, such as
_sample_transcripts.fna_l50.transdecoder.cds.gz or

By default the script uses BLASTX (in combination with Transdecoder), which might take quite some time to process large numbers of sequences. For this reason the DIAMOND algorithm is also available (upon calling option -X), which in our benchmarks showed comparable performance and was several orders of magnitude faster when using multiple CPU cores.

CDS sequences can be deduced for a collection of transcriptomes and put in the same folder, so that they can all be analyzed together with Such files support calling option -D, which will annotate Pfam domains contained in those sequences, and can then also be used to calculate enrichment as explained in manual_get_homologues.pdf.

4.2 Clustering orthologous sequences from FASTA files, one per cultivar/ecotype/strain

This example takes the sample input folder sample_transcripts_fasta, which contains automatically assembled transcripts (Trinity) of three Hordeum vulgare strains (barley), plus a set of full-length cDNA collection of cultivar Haruna Nijo, to show to produce clusters of transcripts.

The next command uses the OMCL algorithm to cluster sequences, produces a composition report, including the soft-core, and finally computes an Average Nucleotide Identity matrix on the produced clusters. Note that redundant isoforms are filtered, keeping only the longest one (you can turn this feature off with -i 0):

$ ./ -d sample_transcripts_fasta -M -c -z -A

The output should look like this (contained in file sample_transcripts_output.txt):

# results_directory=/path/sample_transcripts_fasta_est_homologues

# checking input files...
# Esterel.trinity.fna.bz2 5892  median length = 506
# Franka.trinity.fna.bz2 6036  median length = 523
# Hs_Turkey-19-24.trinity.fna.bz2 6204  median length = 476
# flcdnas_Hnijo.fna.gz 28620 [full length sequences] median length = 1504

# 4 genomes, 46752 sequences

# taxa considered = 4 sequences = 46752 residues = 63954041

# mask=Esterel_alltaxa_algOMCL_e0_ (_algOMCL)

# re-using previous isoform clusters
# 42 sequences
# 65 sequences
# 61 sequences
# 2379 sequences

# creating indexes, this might take some time (lines=2.08e+05) ...

# construct_taxa_indexes: number of taxa found = 4
# number of file addresses/BLAST queries = 4.4e+04

# genome composition report (samples=20,permutations=24,seed=0)
# genomic composition parameters: MIN_PERSEQID_HOM=70 MIN_COVERAGE_HOM=50 SOFTCOREFRACTION=0.95

# file=sample_transcripts_fasta_est_homologues/
genomes	mean	stddev	|	samples
0	8559	6614	|	4665	4665	4665	...
1	1113	737	|	496	432	2007	...
2	255	101	|	84	308	347	...
3	66	0	|	66	66	66	...

# file=sample_transcripts_fasta_est_homologues/
genomes	mean	stddev	|	samples
0	8559	6614	|	4665	4665	4665	...
1	3491	2311	|	2428	2195	8108	...
2	2170	1017	|	765	3460	2145	...
3	645	101	|	816	592	553	...

# looking for valid sequence clusters (n_of_taxa=4)...

# number_of_clusters = 66
# cluster_list = sample_transcripts_fasta_est_homologues/Esterel_alltaxa_algOMCL_e0_.cluster_list
# cluster_directory = sample_transcripts_fasta_est_homologues/Esterel_alltaxa_algOMCL_e0_

# average_nucleotide_identity_matrix_file = # [...]/

Notice that both core and soft-core sampling experiments are reported, considering sequences found in all strains and in 95% strains, respectively. The produced Average Nucleotide Identity matrix looks like this:

genomes Esterel Franka HsTurkey flcdnasHnijo
Esterel 100 98.29 98.04 99.33
Franka 98.29 100 98.25 98.90
HsTurkey 98.04 98.25 100 98.41
flcdnasHnijo 98.33 98.90 98.41 100

Provided that optional R modules described in manual_get_homologues.pdf are installed, this matrix can be plotted with the following command:

./ -i sample_[...]/ \
  -t "clusters=66" -k "Average Nucleotide Identity" -o pdf -m 28 -v 35 -H 9 -W 10

Figure 5: Heatmap of Average Nucleotide Identity.
Image Esterel_Avg_identity_heatmap

If the previous command is changed by adding option -t -2 only transcripts present in at least two strains will be considered, which are output in folder:
This second command produces a significantly different pan-genome composition matrix, which changes from:

# file=sample_transcripts_fasta_est_homologues/
genomes	mean	stddev	|	samples
0	8559	6614	|	4665	4665	4665	...
1	14830	6425	|	8937	9002	21292	...
2	21384	4866	|	13004	24283	23358	...
3	26380	468	|	27019	26209	25652	...


# file=sample_transcripts_fasta_est_homologues/
genomes	mean	stddev	|	samples
0	2860	1172	|	2262	2262	2262	...
1	4270	490	|	4110	3828	4196	...
2	4953	424	|	5475	4767	4294	...
3	4954	424	|	5475	4768	4296	...

Both matrices can be plotted with script, with a command such as:
./ -i sample_transcripts_fasta_est_homologues/ -f pan

Figure 6: Pan-transcriptome size estimates (-t 0, left) and (-t 2, right) based on random samples of 4 transcriptome sets. As the left example illustrates, four strains are usually not enough to fit a Tettelin-like function.
Image pan_genome_algOMCL
Image pan_genome_2taxa_algOMCL

The next figure shows a similar analysis but now using genomic data instead of transcript sets. The example shows pan-genome size estimates of Whole Genome Sequence assemblies of 19 Arabidopsis thaliana ecotypes, downloaded from and described in PubMed=21874022.

Figure 7: Core-genome, soft-core-genome and pan-genome CDS composition analysis of WGS assemblies of 19 A.thaliana ecotypes. Note that the pan-genome simulation was done with all clusters (left) and with all clusters found in at least three genomes (right), illustrating the effect of option -t 3, which might be useful to remove low confidence sequences. Red numbers correspond to fitted values generated by
Image pangenomet

Script can also be called with flag -a:

./ -i sample_transcripts_fasta_est_homologues/ \
  -f pan -a snapshots+

This will create and store a in folder snapshots/ a series of GIF images that can be used to animate pan-genome simulations. The next Figure show some of this snapshots:

Figure 8: Four snapshots of the pan-genome simulation carried out in the previous figure, generated by
Image snapshot

4.3 Clustering sequences on a multicore Linux box, not a cluster

This example takes the sample input folder sample_transcripts_fasta, and demonstrates how you could run a large analysis on a multicore Linux box, not a computer cluster. This example requires command-line tool parallel, which in Ubuntu can be installed with sudo apt-get -y install:

# 1) run BLASTN (and HMMER) in batches
./ -d sample_transcripts_fasta -o

# 2) run in -m dryrun mode
./ -d sample_transcripts_fasta -m dryrun
# EXIT: check the list of pending commands at sample_transcripts_fasta_est_homologues/dryrun.txt
parallel < sample_transcripts_fasta_est_homologues/dryrun.txt

# repeat 2) until completion
./ -d sample_transcripts_fasta -m dryrun
# ...

4.4 Producing a nucleotide-based pangenome matrix

The clusters obtained in the previous section with option -t 2 can be used to compile a pangenome matrix without singletons with this command:
./ -d sample_[...]/Esterel_2taxa_algOMCL_e0_ -o outdir -n -m

# number of input cluster directories = 1

# parsing clusters in sample_transcripts_fasta_est_homologues/Esterel_2taxa_algOMCL_e0_ ...
# cluster_list in place, will parse it (sample_[...]/Esterel_2taxa_algOMCL_e0_.cluster_list)
# number of clusters = 5241

# intersection output directory: outdir

# intersection size = 5241 clusters

# intersection list = outdir/intersection_t0.cluster_list

# pangenome_file = outdir/ (and transposed)
# pangenome_genes = outdir/ (and transposed)
# pangenome_phylip file = outdir/pangenome_matrix_t0.phylip 
# pangenome_FASTA file = outdir/pangenome_matrix_t0.fasta
# pangenome CSV file (Scoary) = outdir/

The following, taken from the tutorial, explains the different versions of the same pangenome/pangene matrix: is a numeric matrix with tab-separated (TSV) columns, with taxa/genomes as rows and sequence clusters as columns, in which cells with natural numbers indicate whether a given taxa contains 1+ sequences from a given cluster. It can be read and edited with any text editor or spreadsheet software, and is also produced in transposed form for convenience. For example, users might want to sort the clusters by position on a reference genome and use these matrices to visualize results. is similar to the previous one, but contains the actual sequence names in each cluster instead.

pangenome_matrix_t0.phylip is a reduced binary matrix in a format suitable for PHYLIP discrete character analysis software.

pangenome_matrix_t0.fasta is a reduced binary matrix in FASTA format suitable for binary character analysis software such as IQ-TREE, which can compute bootstrap and aLRT support. is a transposed, reduced binary matrix in CSV format suitable for pangenome-wide association analysis with software Scoary.

If the optional R modules described in manual_get_homologues.pdf are installed, such a pangenome matrix can be used to hierarchically cluster strains with this command:
./ -i outdir/

Figure 9: Hierarchical grouping of strains based on pangenome matrix.
Image pangenome_matrix_t0_heatmap-est

4.5 Estimating protein domain enrichment of some sequence clusters

This example uses data from the barley benchmark, the test_barley/ folder, which contains instructions to download sequences from:

After completing the downloads, the folder will contain FASTA files with nucleotide sequences of 14 de-novo assembled transcriptomes and transcripts/cDNA sequences annotated in reference accessions Morex and Haruna Nijo. CDS can be extracted as explained in Section 5 and then Pfam domains can be annotated as follows:

$ ./ -d cds -D -o -m cluster

These annotations will serve to calculate background domain frequencies.

Once this is completed, we can compute "control" clusters with this command:
$ ./ -d cds -M -m cluster

which we will then place in a folder called clusters_cds: -d cds_est_homologues/Alexis_0taxa_algOMCL_e0_ \
  -o clusters_cds -m -n+

In order to call accessory sequences with more confidence we will use only non-cloud clusters ( $ occupancy > 2$), which we do with this command:
$ ./ -d cds -M -t 3 -m cluster

The output should include the next lines:

# number_of_clusters = 34248
# cluster_list = cds_est_homologues/Alexis_3taxa_algOMCL_e0_.cluster_list
# cluster_directory = cds_est_homologues/Alexis_3taxa_algOMCL_e0_

We should be now in position to compile the pan-genome matrix corresponding to these clusters:

./ -d cds_est_homologues/Alexis_3taxa_algOMCL_e0_ \
  -o clusters_cds_t3 -m -n

which should produce:

# number of clusters = 34248

# intersection output directory: clusters_cds_t3
# intersection size = 34248 clusters

# intersection list = clusters_cds_t3/intersection_t0.cluster_list

# pangenome_file = clusters_cds_t3/
# pangenome_phylip file = clusters_cds_t3/pangenome_matrix_t0.phylip

We should now interrogate the pan-genome matrix, for instance looking for clusters found in one genotype (A) but not in others (B):

./ -m clusters_cds_t3/ \
  -A cds/SBCC073.list -B cds/ref.list -g

You should obtain a list of 4348 accessory clusters:

# matrix contains 34248 clusters and 16 taxa

# taxa included in group A = 1

# taxa included in group B = 2

# finding genes present in A which are absent in B ...
# file with genes present in set A and absent in B (4348): 

Finally, we will now estimate whether these clusters are enriched in any Pfam domain, producing also a single FASTA file with the tested sequences:

./ -d cds_est_homologues -c clusters_cds -n -t greater \
  -x clusters_cds_t3/pangenome_matrix_t0__pangenes_list.txt -e -p 0.05 \
  -r SBCC073 -f SBCC073_accessory.fna

The output should be:

# 39400 sequences extracted from 113222 clusters

# total experiment sequence ids = 4818
# total control    sequence ids = 39400

# parse_Pfam_freqs: set1 = 562 Pfams set2 = 3718 Pfams

# created FASTA file: SBCC073_accessory.fna

# sequences=4818 mean length=353.8 , seqs/cluster=1.11

# fisher exact test type: 'greater'
# multi-testing p-value adjustment: fdr
# adjusted p-value threshold: 1

# total annotated domains: experiment=1243 control=19192

#PfamID counts(exp) counts(ctr) freq(exp) freq(ctr) p-value p-value(adj)  description
PF00009 0 20  0.000e+00 1.042e-03 1.000e+00 1.000e+00 Elongation factor Tu GTP binding domain
PF00010 0 32  0.000e+00 1.667e-03 1.000e+00 1.000e+00 Helix-loop-helix DNA-binding domain
PF00665 13  31  1.046e-02 1.615e-03 1.418e-06 1.318e-03 Integrase core domain
PF07727 28  61  2.253e-02 3.178e-03 3.033e-13 1.128e-09 Reverse transcriptase (RNA-dep DNA pol)
PF00931 44  201 3.540e-02 1.047e-02 1.750e-10 3.253e-07 NB-ARC domain
PF13976 14  19  1.126e-02 9.900e-04 2.744e-09 3.401e-06 GAG-pre-integrase domain

4.6 Making and annotating a non-redundant pangenome matrix

The script produces a non-redundant pangenome matrix by comparing all clusters to each other, taking the median sequence in each cluster. By default nucleotide sequences are compared, but if the original input of get_homologues-est comprised both DNA and protein sequences, the user can also choose peptide sequences to compute redundancy, which probably make more sense in terms of protein function. On the contrary, it would seem more appropriate to use DNA sequences to measure diversity.

In this example a DNA-based non-redundant pangenome matrix is computed with BLASTN assuming that sequences might be truncated (option -e) and using 10 processor cores and a coverage cutoff of 50%:
./ -m outdir/ -n 10 -e -C 50

# input matrix contains 5241 clusters and 4 taxa

# filtering clusters ...
# 5241 clusters with taxa >= 1 and sequence length >= 0

# sorting clusters and extracting median sequence ...

# running makeblastdb with outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90.fna

# parsing blast result! (outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90.blast , 0.37MB)
# parsing file finished

# 5172 non-redundant clusters
# created: outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90.fna

# printing nr pangenome matrix ...
# created: outdir/

Note that the previous command can be modified to match external reference sequences, for instance from Swissprot, or pre-computed clusters, such as groups of orthologous sequences, so that the resulting matrix contains cross-references to those external clusters, and their annotations. In either case, both input clusters and reference sequences must be of the same type: either nucleotides or peptides.

The next example shows how a set of clusters produced by get_homologues-est can be matched to some nucleotide reference sequences, in this case annotated rice cDNAs:

./ -m outdir/ -n 10 -e -C 50 -f oryza.fna

This is the produced output:

# input matrix contains 5241 clusters and 4 taxa

# filtering clusters ...
# 5241 clusters with taxa >= 1 and sequence length >= 0

# sorting clusters and extracting median sequence ...
# re-using previous BLAST output outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90.blast

# parsing blast result! (outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90.blast , 0.34MB)
# parsing file finished

# 5172 non-redundant clusters
# created: outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90.fna

# 66339 reference sequences parsed in oryza.fna

# parsing blast result! (outdir/pangenome_matrix_t0_nr_t1_l0_e1_C50_S90_ref.blast , 0.37MB)
# parsing file finished

# matching nr clusters to reference (%alignment coverage cutoff=50) ...

# printing nr pangenome matrix ...
# created: outdir/

# NOTE: matrix can be transposed for your convenience with:

  perl -F'\t' -ane '$r++;for(1 .. @F){$m[$r][$_]=$F[$_-1]}; \
    $mx=@F;END{for(1 .. $mx){for $t(1 .. $r){print"$m[$t][$_]\t"}print"\n"}}' \

The suggested perl command can be invoked to tranpose the matrix, which now contains rows such as these:

non-redundant	Franka.bz2.nucl	Esterel.bz2.nucl	flcdnas_Hnijo.gz.nucl	...	redundant	reference	
1_TR2804-c0_g1_i1.fna	1	1	0	0	NA	LOC_Os09g07300.1 cDNA|BIG, putative, expressed	
2_TR1554-c0_g1_i1.fna	0	2	1	0	NA	LOC_Os03g53280.1 cDNA|WD domain containing protein	
6_TR3918-c0_g1_i1.fna	0	1	1	0	NA	NA	

Pangenome matrices with more than 4 taxa can be plotted with help from script, as explained in manual_get_homologues.pdf.

4.7 Annotating a sequence cluster

After analyzing pan-genome or pan-transcriptome clusters it might be interesting to find out what kind of transcripts or proteins they encode, or we might just want to double-check the BLAST matches that support a produced cluster. The script does just that, and can be used with both nucleotide and peptide clusters. Note that in this first example it prints also the PFam domains (-D) annotated in those sequences:

./ -f outdir/1004_TR425-c0_g2_i1.fna -o 1004_TR425-c0_g2_i1.aln.fna -D

And will produce this output:


# ./ -f outdir/1004_TR425-c0_g2_i1.fna -r  \
#     -o 1004_TR425-c0_g2_i1.aln.fna -P 1 -b 0 -D 1 -c 0 -A  -B 

# total   sequences: 3 taxa: 2

# Pfam domains: PF10602,PF01399,
# Pfam annotation: 26S proteasome subunit RPN7;PCI domain;
# aligned sequences: 3 width:   1595

# alignment sites: SNP=3 parsimony-informative=0 (outdir/1004_TR425-c0_g2_i1.fna)

# taxa included in alignment: 2

# alignment file: 1004_TR425-c0_g2_i1.aln.fna

If option -b is enforced a blunt-end alignment is produced, which might be useful for further analyses. In either case, the produced FASTA alignment file will contain Pfam domains in each header, in addition to the relevant BLAST scores:

>TR425|c0_g2_i1_[Esterel.trinity.fna.bz2] bits E-value N qy ht 1:1595 Pfam:..

Figure 10: Fragment of alignment produced by, rendered with BioEdit.
Image annotcluster

Optionally -c can also be invoked to collapse aligned sequences from the same species or taxon. This might be useful when working with clusters of transcript isoforms, which are often redundant and broken in possibly overlapping fragments. Taking the same example cluster, we could try to collapse isoforms with overlaps $ \geq 30$ residues like this:

./ -f outdir/1004_TR425-c0_g2_i1.fna -o 1004_TR425-c0_g2_i1.aln.fna -D -c 30

This script does not tolerate mismatches between sequences to be collapsed; however, that behaviour can be relaxed by editing the value of variable $MAXMISMCOLLAP=0 at the top of the script. Instead, as BLASTN-placed gaps in identical sequences can often move, by default two such gaps are accepted (see variable MAXGAPSCOLLAP=2).

By default, the script looks for the longest sequences and aligns all other cluster sequences to it with BLASTN (megablast). The user can also pass an external, reference sequence to guide cluster alignment (see option -r). However, in either case, clusters of transcripts often contain a fraction of BLASTN hits that do not match the longest/reference sequence; instead, they align towards the 5' or 3' of other sequences of the clusters and are thus not included in the produced multiple sequence alignment (MSA):

 -----------------            <= longest/reference sequence
                      ....    <= sequences not included in MSA

We called these pseudo-multiple alignments as they are computed from pairwise alignments of the longest/reference (query) to all other cluster sequences. The resulting alignment is produced by MVIEW, which does not record deletions in the query sequence. This means that an alignment like this:

 ------  -----------            <= longest/reference sequence
    ---..----------             <= .. fragment not included in MSA
 ------  -----
   ----  --------

will in fact be saved as:


In case you want to compute full multiple sequence alignments, including all indels, please use option -u). This way the script will produce unaligned complete sequences, flipped if required, so that external software (clustal-omega, muscle, MAFFT, etc) can be used to align them.

4.8 Output files explained

The primary output of get_homologues-est is a set of clusters of sequences in FASTA format. These are stored in a folder named according to the input data and the choice of parameters. For instance, a test run with command

./ -d sample_transcripts_fasta/ -m cluster -M -A -t 0

will produce an output folder named Esterel_alltaxa_algOMCL_e0_. The contents of this folder are summarized in file Esterel_alltaxa_algOMCL_e0_.cluster_list, which looks like this:

cluster 1_TR2804-c0_g1_i1 size=2 taxa=2 file: 1_TR2804-c0_g1_i1.fna aminofile: void
: Esterel.trinity.fna.bz2.nucl
: Franka.trinity.fna.bz2.nucl
cluster 2_TR1554-c0_g1_i1 size=3 taxa=2 file: 2_TR1554-c0_g1_i1.fna aminofile: void
: Esterel.trinity.fna.bz2.nucl
: Esterel.trinity.fna.bz2.nucl
: flcdnas_Hnijo.fna.gz.nucl
cluster 4_TR593-c0_g2_i1 size=2 taxa=1 file: 4_TR593-c0_g2_i1.fna aminofile: void
: Esterel.trinity.fna.bz2.nucl
: Esterel.trinity.fna.bz2.nucl
cluster 6_TR3918-c0_g1_i1 size=2 taxa=2 file: 6_TR3918-c0_g1_i1.fna aminofile: void
: Esterel.trinity.fna.bz2.nucl
: flcdnas_Hnijo.fna.gz.nucl
cluster 7_TR1297-c0_g1_i3 size=4 taxa=2 file: 7_TR1297-c0_g1_i3.fna aminofile: void
: Esterel.trinity.fna.bz2.nucl
: Esterel.trinity.fna.bz2.nucl
: Esterel.trinity.fna.bz2.nucl
: flcdnas_Hnijo.fna.gz.nucl

This excerpt describes the first resulting clusters, the number of sequences in each (size) and their respective genomes/strains (taxa). Note that in this case there are only nucleotide clusters; if twin peptides files are provided as input then protein clusters should also be produced. Each cluster name name is followed by a list of taxa matching the order of sequences contained in it. For instance, if we check the first cluster (Esterel_0taxa_algOMCL_e0_/1_TR2804-c0_g1_i1.fna), it looks like this:

>TR2804|c0_g1_i1 [Esterel.trinity.fna.bz2] | aligned:9590-15261 (15270)
>TR3086|c0_g1_i1 [Franka.trinity.fna.bz2] | aligned:1-5672 (5672)

Besides clusters, there are other output files that can be produced by get_homologues-est; let's review some of them:

5 A step-by-step protocol with barley assembled transcripts

This section describes the steps required to proceed with the analysis of barley transcripts with folder test_barley, which you should get with the software. The following commands are to be pasted in your terminal:

## set get_homologues path if not already in $PATH
export GETHOMS=~/soft/github/get_homologues/

cd test_barley

## 1) prepare sequences
cd seqs

# download all transcriptomes
wget -c -i wgetlist.txt

# extract CDS sequences (this takes several hours)
# choose if dependency Inline::CPP is available in your system
# the script will use 20 CPU cores, please adapt it to your system

# clean and compress
#rm -f _* *noORF* *transcript*
#gzip *diamond*

# put cds sequences aside
mv *cds.f*gz ../cds
cd ..

# check lists of accessions are in place (see HOWTO.txt there)
ls cds/*list

## 2) cluster sequences and start the analyses

# calculate protein domain frequencies (Pfam)
$GETHOMS/ -d cds -D -m cluster -o &> log.cds.pfam

# alternatively, if not running in a SGE cluster, taking for instance 20 CPUs 
$GETHOMS/ -d cds -D -n 20 -o &> log.cds.pfam

# calculate 'control' cds clusters
$GETHOMS/ -d cds -M -t 0 -m cluster &> log.cds

# get non-cloud clusters
$GETHOMS/ -d cds -M -t 3 -m cluster &> log.cds.t3

# clusters for dN/dS calculations
$GETHOMS/ -d cds -e -M -t 4 -m cluster &> log.cds.t4.e

# leaf clusters and pangenome growth simulations with soft-core
$GETHOMS/ -d cds -c -z \
  -I cds/leaf.list -M -t 3 -m cluster &> log.cds.leaf.t3.c

# produce pan-genome matrix and allocate clusters to occupancy classes

# all occupancies
$GETHOMS/ -d cds_est_homologues/Alexis_0taxa_algOMCL_e0_ \
  -o clusters_cds -m -n &> log.compare_clusters.cds

# excluding cloud clusters, the most unreliable in our benchmarks
$GETHOMS/ -d cds_est_homologues/Alexis_3taxa_algOMCL_e0_ \
  -o clusters_cds_t3 -m -n &> log.compare_clusters.cds.t3
$GETHOMS/ -m clusters_cds_t3/ -s \
  &> log.parse_pangenome_matrix.cds.t3

# make pan-genome growth plots
$GETHOMS/ -i cds_est_homologues/ \
	-f core_both &> log.core.plots
$GETHOMS/ -i cds_est_homologues/ \
	-f pan &> log.pan.plots

## 3) annotate accessory genes

# find [-t 3] SBCC073 clusters absent from references
$GETHOMS/ -m clusters_cds_t3/ \
  -A cds/SBCC073.list -B cds/ref.list -g &> log.acc.SBCC073
mv clusters_cds_t3/pangenome_matrix_t0__pangenes_list.txt \

# how many SBCC073 clusters are there? 
perl -lane 'if($F[0] =~ /SBCC073/){ foreach $c (1 .. $#F){ if($F[$c]>0){ $t++ } }; print $t }' \

# find [-t 3] Scarlett clusters absent from references
$GETHOMS/ -m clusters_cds_t3/ \
  -A cds/Scarlett.list -B cds/ref.list -g &> log.acc.Scarlett 
mv clusters_cds_t3/pangenome_matrix_t0__pangenes_list.txt \

# find [-t 3] H.spontaneum clusters absent from references
$GETHOMS/ -m clusters_cds_t3/ \
  -A cds/spontaneum.list -B cds/ref.list -g &> log.acc.spontaneum
mv clusters_cds_t3/pangenome_matrix_t0__pangenes_list.txt \

# Pfam enrichment tests

# core
$GETHOMS/ -d cds_est_homologues -c clusters_cds -n \
	-x clusters_cds_t3/pangenome_matrix_t0__core_list.txt -e -p 1 \
	-r SBCC073 >

$GETHOMS/ -d cds_est_homologues -c clusters_cds -n \
	-x clusters_cds_t3/pangenome_matrix_t0__core_list.txt -e -p 1 \
	-r SBCC073 -t less >

# accessory
$GETHOMS/ -d cds_est_homologues -c clusters_cds -n \
	-x clusters_cds_t3/SBCC073_pangenes_list.txt -e -p 1 -r SBCC073 \
	-f SBCC073_accessory.fna >
$GETHOMS/ -d cds_est_homologues -c clusters_cds -n \
	-x clusters_cds_t3/Scarlett_pangenes_list.txt -e -p 1 -r Scarlett \
	-f Scarlett_accessory.fna >

$GETHOMS/ -d cds_est_homologues -c clusters_cds -n \
	-x clusters_cds_t3/spontaneum_pangenes_list.txt -e -p 1 -r Hs_ \
	-f spontaneum_accessory.fna >

# note that output files contain data such as the mean length of sequences

# get merged stats for figure
perl suppl_scripts/ >
perl -lane 'print if($F[0] >= 5 || $F[1] >= 5 || $F[2] >= 5)' \  >
Rscript suppl_scripts/_plot_heatmap.R

6 Frequently asked questions (FAQs)

Please see also the FAQs in manual_get_homologues.pdf. Most apply also to, such as running on a computer farm or on Windows systems.

7 Credits and references is designed, created and maintained at the Laboratory of Computational Biology at Estación Experimental de Aula Dei/CSIC in Zaragoza (Spain) and at the Center for Genomic Sciences of Universidad Nacional Autónoma de México (CCG/UNAM).

The code was written mostly by Bruno Contreras-Moreira and Pablo Vinuesa, but it also includes code and binaries from OrthoMCL v1.4 (algorithm OMCL, -M), NCBI Blast+, MVIEW, DIAMOND and BioPerl 1.5.2.

Other contributors: Carlos P Cantalapiedra, Alvaro Rodriguez del Rio, Ruben Sancho, Roland Wilhelm, David A Wilkinson.

We ask the reader to cite the main reference describing the get_homologues software,

and also the original papers describing the included algorithms and databases, accordingly:

If you use the accompanying scripts the following references should also be cited:

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