## RUNNING AUGUSTUS
1. [BASIC USAGE](#basic-usage)
2. [OPTIONAL ARGUMENTS](#optional-arguments)
3. [DATABASE ACCESS](#database-access)
4. [USING HINTS](#using-hints)
5. [TRAINING OF CLADE-SPECIFIC PARAMETERS](#training-of-clade-specific-parameters)
(USUALLY NOT REQUIRED!!!)
6. [TRAINING CGP SCORE PARAMETERS](#training-cgp-score-parameters)
# BASIC USAGE
In order to call AUGUSTUS in the comparative gene prediction mode, 4 mandatory
arguments need to be passed:
--species=identifier
a species for which model parameters are trained.
For a list of identifiers see README.md.
Decide on one of the species in your set that you can find in the list of
identifiers or that comes closest to one of the identifiers.
For instance, use the identifier 'human' for a mammal data set or the
identifier 'chicken' for a bird data set.
--speciesfilenames=genomes.tbl
a file containing for each species the path to its genome file.
Each line in 'genomes.tbl' consists of two tab-separated fields.
The first field is a genome or species identifier
(does not correspond to the identifier in --species !!!).
The second field is the directory and file name for the genome file, e.g.
hg19 /dir/to/genome/human.fa
mm9 /dir/to/genome/mouse.fa
bosTau4 /dir/to/genome/cow.fa
galGal3 /dir/to/genome/chicken.fa
The genome files must be in fasta format and may contain the sequences of
one or more chromosomes/scaffolds.
The file 'mouse.fa' might look as follows
>chr16
AGCTCGCAGTGTTGATGCTTCAGTCTC
>chr3
ccagaggagacagttagtactaaatgcaccaa
For running Augustus-cgp on a subset of genomes, simply delete all lines of
non-target genomes in --speciesfilenames.
The alignment and phylogenetic tree need no modification if only a subset
of genomes is used.
--alnfile=aln.maf
a file containing a multiple sequence alignment of the genomes in MAF format.
The sequence names (first field in an 's' line) must be the species identifiers
(as they appear in 'genomes.tbl')
and the sequences identifiers (as they appear in the genome files) delimited
by '.', e.g.
a score=628177.000000
s hg19.chr21 2032 36 + 32085 TAGG-----------TCTTGCTTCGCCGCAGGAGCGTGGCGGCGGGG
s mm9.chr16 1745 36 + 25968 TAGG-----------TCTTGCTGCGTCGGAGCAACGTGGCAGCAGAG
s bosTau4.chr1 1935 36 + 30875 TAGG-----------TCTTGCTCCGCCGGAGGAGCGTGGCGGCAGGA
s galGal3.chr1 1000 45 + 22283 CAGTAACTGAGCTATTGCTGCTCTGCTG--AGTGAGCCGGAGCAGGG
a score=3843.000000
s hg19.chr21 2068 9 + 32085 CCATGGCCG
s bosTau4.chr1 1971 9 + 30875 CTGGGGCCG
s mm9.chr16 1781 9 + 25968 CTCTGGCCT
Alignment rows of species that are not listed in --speciesfilenames are ignored.
--treefile=tree.nwk
a phylogenetic tree of the species in Newick format, e.g.
(((hg19:0.16268,mm9:0.352605):0.020666,bosTau4:0.219477):0.438357,galGal3:0.474279);
All branch lengths are required and leaf nodes must be named after the
genome/species identifier (as in 'aln.maf' and 'genomes.tbl').
Also a valid format (often output of phylogenetic tree reconstruction tools
such as MrBayes, PHYLIP, ...) is for instance
begin trees;
translate
1 hg19,
2 mm9,
3 bosTau4,
4 galGal3
;
tree con_50_majrule = [&U] (((1:0.16268,2:0.352605):0.020666,3:0.219477):0.438357,4:0.474279);
end;
In cases where the phylogeny is not known, a star-like tree with uniform branch lengths might be
used instead, e.g.
(hg19:0.01,mm9:0.01,bosTau4:0.01,galGal3:0.01);
If --speciesfilenames only contains a subset of the species in --treefile, a subtree of is extracted.
example usage:
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf --speciesfilenames=genomes.tbl
a small data set for testing can be found in examples/cgp/
# OPTIONAL ARGUMENTS
a) General Options:
-------------------
--/CompPred/onlySampledECs=on/off
if on, only exons from the sampling of gene structures are taken as the set
of possible candidate exons.
Otherwise additional candidate exons are determined by combining all possible
pairs of ASS/DSS, start/DSS, ASS/stop and start/stop that are within the
maximum length of exons (--max_exon_len, default: 12000).
Turn this flag on, to reduce the overall runtime memory requirements at
the cost of a potential decrease in accuracy.
(default: off)
--/CompPred/liftover_all_ECs=on/off
by default only likely exon candidates (the ones from sampling) are lifted
over to the other genomes. If this flag is turned on ALL exon candidates
are lifted over to the other genomes. This increases the runtime and memory
requirement, but is potentially more accurate
(default: off)
--UTR=on/off
predict the untranslated regions in addition to the coding sequence.
Note that the 3'-UTR, 5'UTR or both can be absent in some predicted genes
if candidate UTRs perform poorly in the ab initio model and are not supported
by extrinsic evidence. Enforce the prediction of UTRs with
--/CompPred/genesWithoutUTRs=false
This option requires that a UTR model was trained for the species specified with
--species=...
--nc=on/off
simultaneous prediction of coding genes and non-coding genes (mostly lincRNA)
(default: off)
Non-coding genes are only predicted if they have RNA-Seq support.
This option is experimental, as the scores of exons/introns of non-coding
genes in the gene structure graph still need to be defined.
Usage only intended for developers!
--genemodel=partial/complete
partial : allow prediction of incomplete genes at the sequence boundaries (default)
complete: only predict complete genes
--/CompPred/genesWithoutUTRs=true/false
if true, all predicted genes are flanked by a 5'- and 3'- untranslated region
(with the exception of partial genes at the sequence boundaries).
this option only makes sense together with --UTR=on.
--noprediction=true/false
If true, no prediction is made. Useful for getting the gene ranges and
homologous candidate exons.
--/CompPred/outdir=path
send all output files to this directory (default is the current directory)
--printOEs=true/false
print all homologous candidate exons to the file orthoExons.<species>.gff3
(default: off)
--/CompPred/printOrthoExonAli=true/false
prints codon alignments of all tuples of homologous candidate exons to
the file 'orthoexons_codonAlignment.maf'
(default: false)
--/CompPred/printConservationWig=true/false
prints conservation tracks (in wiggle format) of all syntenic regions to
the file <species>.wig
(default: off)
--exoncands=true/false
print all candidate exons to the file exonCands.<species>.gff3 (default: off)
--softmasking=1
adds regions with lowercase nucleotides as nonexonpart hints of source "RM".
This is the preferrable way to deal with repeat (soft) masked genomes.
If --extrinsicCfgFile is not given, it used the default extrinsic-cgp.cfg
with bonus 1.15, if another extrinsic config file is given, it must contain
the "RM" source.
--temperature=t
heat the posterior distribution for sampling, 0=cold, 7=hottest, take probs
to the power of (8-temperature)/8
A higher temperature tends to include more suboptimal gene structures during sampling.
(default: 3)
--optCfgFile=cgp_parameters.cfg
include parameter file from training. The training uses logistic regression
to classify candidate exons based on cross-species features like selective
pressure (dN/dS), conservation, phylogenetic diversity, etc.
(default: config/cgp/log_reg_parameters_default.cfg)
--allow_hinted_splicesites=atac
comma-separated list of non-canonical splice site pairs that enables the
prediction of rare introns with unusual splice sites in addition to the
GT-AG and GC-AG introns that are allowed by default.
b) Options to adjust the generation of geneRanges (syntenic regions) from the input alignment
---------------------------------------------------------------------------------------------
--maxDNAPieceSize=n
This value specifies the maximal length of a sequence chunk in a geneRange.
For longer sequence chunks, the geneRange is cut into several pieces that
are processed separately.
(default: 200000)
--/CompPred/maxCov
Decreasing maxCov punishes multiple overlapping geneRanges more.
By default, maxCov = 3 penalizes the same region covered by more than 3 alignments
--/CompPred/covPen
Increasing the coverage penalty covPen punishes long overlaps between geneRanges more.
By default, covPen = 0.2 punishes uncovered bases 5 times more than each
base covered too often
c) Options to adjust properties of splice sites, exons, introns and genes
-------------------------------------------------------------------------
--max_exon_len=n
maximum length of a candidate exon
(default: 12000)
Typically, this needs not be changed.
--min_intron_len=n
minimum length of a candidate intron
(default: 39)
--min_coding_len=n
minimum length of a coding region
(default: 102)
--/CompPred/mil_factor=f
mean intron length factor (>=1), the higher the less are long introns penalized
(default: 1).
A value of 100 roughly corresponds to not penalizing long introns.
(does not concern explicit introns from sampling whose lengths are modeled
explicitly by a geometric-tail distribution. This only concerns implicit
introns that are constructed along auxiliary bars in the gene structure graph).
--/CompPred/dssqthresh=q
threshold for the inclusion of donor splice sites based on the pattern
probability (q in [0,1] )
q=0.05 means that only dss are considered that have a pattern, such that
5% of true splice site patterns have lower probability.
q=0 means that all splice site patterns are considered.
--/CompPred/assqthresh=q --/CompPred/assmotifqthresh=q
thresholds for the inclusion of acceptor splice sites
(the inclusion of an acceptor splice site depends both on the ASS and the
ASS motif threshold)
d) Options to adjust the scoring function of candidate exons/introns:
---------------------------------------------------------------------
--/CompPred/omega=on/off
estimate selective pressure (non-synonymous to synonymous rate ration dN/dS)
for each codon alignment of homologous candidate exons
(default: on)
--/CompPred/conservation=on/off
compute an average columnwise conservation score for each tuple of homologous
candidate exons
(default: on)
--/CompPred/ec_thold=a
parameter that is added to the scoring function of candidate exons
(default: 0)
Enables the shifting of the scoring function such that sensitivity (SN) and
specificity (SP) are in balance.
If t>0 is the threshold from logistic regression for which SN and SP are
in balance on the training set, then set
a = log((1/t) - 1)
--/CompPred/ic_thold=b
parameter that is added to the scoring function of candidate introns
(default: 0).
Analogous to parameter --/CompPred/ec_thold above.
--/CompPred/scale_codontree=f
scaling factor to scale branch lengths in the codon tree to one codon
substitution per time unit.
After applying this factor to each branch length for the input tree, the
tree should be scaled for the expected number of CODON substitutions.
(default: 1)
e) Options to adjust the phylogenetic model:
--------------------------------------------
--/CompPred/phylo_model=2,3 or 4
number of states in the phylogenetic model (default: 2)
model 2: state 1: EC present but not predicted
state 2: EC present and predicted
rate Matrix Q = [(-lambda, lambda), (mu, -mu)]
depends on parameters --/CompPred/exon_gain (lambda) and --/CompPred/exon_loss (mu)
(see next parameters)
model 3: model 2 + state 3: EC not present but alignment present
additionally depends on --/CompPred/ali_error
(see next parameters)
model 4: model 3 + state 4: no alignment present
models 3 and 4 are experimental as the rate matrices are not well defined.
(usage only recommended for developers that want to play around with the rate matrices)
--/CompPred/exon_loss=r
rate r>0 of exon loss (parameter of the phylogenetic models, see above)
(default: 0.0001)
--/CompPred/exon_gain=r
rate r>0 of exon gain (parameter of the phylogenetic models, see above)
(default: 0.0001)
--/CompPred/ali_error=r
rate r of alignment errors (parameter of the phylogenetic model 3 and 4, see above)
(default: 0.1)
--/CompPred/phylo_factor=f
specifies the influence of the phylogenetic model
(default: 1).
The higher f is chosen, the more weight is given to the phylogenetic model,
i.e. the more consistent the gene structures are across the species.
f) Options to adjust the DD algorithm:
--------------------------------------
--/CompPred/dd_rounds=r
the number of Dual Decomposition rounds (default: 5).
--/CompPred/maxIterations=n
the maximum number of Dual Decomposition iterations per round
(default: 500)
--/CompPred/dd_step_rule=harmonic/square_root/base_2/base_e/polyak/constant/mixed
the step size function (default: mixed)
- constant: c
- harmonic: c / (v+1)
- square_root: c / sqrt(v+1)
- base_2: c / (2^v)
- base_6: c / (e^v)
- polyak: (d_t - p_best) / numInconsistencies
- mixed: 1. round: polyak, all other rounds: square_root
where c is the step size parameter (see next parameter) and
v is the number of iterations prior to the current iteration, in which the
value of the dual problem increases. The polyak step size adjusts the step
size dynamically from quantities computed in previous iterations:
d_t is the current dual value,
p_best is the best primal value, seen so far and
numInconsistencies is the current number of inconsistencies between the
complicating variables.
--/CompPred/dd_factor=a-b
value range of the step size parameter c (default: 1-4). Only required for
step size rules other than "polyak".
When only a single round of DD (--/CompPred/dd_rounds=1) is chosen, specify
a single value for the step size parameter,
e.g. --/CompPred/dd_factor=a. For r>1 rounds of DD, the value range [a-b]
is split into equidistant values, e.g. for r=4, a=1 and b=4, the values
1,2,3 and 4 are used for the first, second, ... and fourth round of DD,
respectively.
# DATABASE ACCESS
The flat-file option above reads in all genomes into RAM. This may require too
much memory, e.g. for a large number of vertebrate-sized genomes. Also, this is
inefficient when many parallel comparative AUGUSTUS runs are started on a compute
cluster. Therefore, another option allows to read only the required sequences
from a database.
## Option 1: SQLite
Sequences and hints can be accessed using an SQLite database
(in our experience the SQLite access runs more stable than MySQL).
Other than the MySQL database that stores the full sequences, the SQLite database
only stores file offsets to achieve random access to the genome files.
a) create an SQLite database and populate it
Use the program 'load2sqlitedb' in the AUGUSTUS repository.
Run load2sqlitedb with the parameter "--help" to view the usage instructions
load2sqlitedb --help
example code for loading genome files to the database vertebrates.db:
load2sqlitedb --species=hg19 --dbaccess=vertebrates.db human.fa
load2sqlitedb --species=mm9 --dbaccess=vertebrates.db mouse.fa
load2sqlitedb --species=bosTau4 --dbaccess=vertebrates.db cow.fa
load2sqlitedb --species=galGal3 --dbaccess=vertebrates.db chicken.fa
b) running AUGUSTUS with SQLite db access:
call AUGUSTUS with parameters --dbaccess AND --speciesfilenames
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--dbaccess=vertebrates.db --speciesfilenames=genomes.tbl
## Option 2: MySQL
This is an alternative to the SQLite database from above.
a) creating a MySQL database (example code) and a user:
apt install mysql-server
/* The initial MySQL root account passwords are usally empty */
mysql -u root -p
> CREATE DATABASE vertebrates;
> CREATE USER `cgp`@`%` IDENTIFIED BY 'db_passwd'; /* or any other password */
> GRANT ALL PRIVILEGES ON vertebrates.* TO cgp@'%';
> exit
b) loading sequences into the database:
Use the program 'load2db' in the AUGUSTUS repository.
Run load2db with the parameter "--help" to view the usage instructions
load2db --help
Call 'load2db' for each genome, double check that the correct species identifier is used, e.g.
load2db --species=hg19 --dbaccess=vertebrates,localhost,cgp,db_passwd human.fa
load2db --species=mm9 --dbaccess=vertebrates,localhost,cgp,db_passwd mouse.fa
load2db --species=bosTau4 --dbaccess=vertebrates,localhost,cgp,db_passwd cow.fa
load2db --species=galGal3 --dbaccess=vertebrates,localhost,cgp,db_passwd chicken.fa
c) running AUGUSTUS with database access:
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf --dbaccess=vertebrates,localhost,cgp,db_passwd
# USING HINTS
Extrinsic evidence (or [hints](RUNNING-AUGUSTUS.md#using-hints) can be
integrated using a flat file or database access.
Note that you have to retrieve BOTH genomes and hints either from a flat file or
from the database. Mixed combinations are not possible.
Let's assume we have extrinsic evidence for human and mouse and already prepared the
hints files for human and mouse in GFF format (just as you would do it in the single
species version of AUGUSTUS):
human.hints.gff contains hints from human RNA-seq and repeat masking
chr21 b2h intron 9908433 9909046 0 . . pri=4;src=E
chr21 repmask nonexonpart 10018268 10018612 0 . . src=RM
chr21 w2h ep 48084612 48084621 41.600 . . src=W;pri=4;mult=41;
mouse.hints.gff contains hints from the mouse Refseq annotation
chr10 mm9_refGene CDS 50409921 50410055 0.000000 + 0 source=M
chr10 mm9_refGene intron 50410056 50419745 0.000000 + . source=M
## retrieving hints from a flat file
First concatenate the hints files into a single file. Prepend the species identifier
to the sequence identifier (first column) in the hints files:
cat human.hints.gff | perl -pe 's/(^chr\d+)/hg19\.$1/' >> hints.gff
cat mouse.hints.gff | perl -pe 's/(^chr\d+)/mm9\.$1/' >> hints.gff
hints.gff now looks as follows
hg19.chr21 b2h intron 9908433 9909046 0 . . pri=4;src=E
hg19.chr21 repmask nonexonpart 10018268 10018612 0 . . src=RM
hg19.chr21 w2h ep 48084612 48084621 41.600 . . src=W;pri=4;mult=41;
mm9.chr10 mm9_refGene CDS 50409921 50410055 0.000000 + 0 source=M
mm9.chr10 mm9_refGene intron 50410056 50419745 0.000000 + . source=M
prepare the extrinsic config file. Use config/extrinsic/extrinsic-cgp.cfg as template
call AUGUSTUS (just as in the single species version) with the hints file and
the extrinsic config file
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--speciesfilenames=genomes.tbl --hintsfile=hints.gff --extrinsicCfgFile=cgp.extrinsic.cfg
## retrieving hints from a SQLite database
Loading hints into the SQLite database works exactly the same as loading genomes
into the database. After the genome files are loaded, call 'load2sqlitedb' to
load hints for a particular species. Use the same species identifier as for the genomes:
load2sqlitedb --species=hg19 --dbaccess=vertebrates.db human.hints.gff
load2sqlitedb --species=mm9 --dbaccess=vertebrates.db mouse.hints.gff
prepare the extrinsic config file. Use config/extrinsic/extrinsic-cgp.cfg as template
call AUGUSTUS with --dbhints enabled:
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--dbaccess=vertebrates.db --speciesfilenames=genomes.tbl --dbhints=true \
--extrinsicCfgFile=cgp.extrinsic.cfg
## retrieving hints from a MySQL database
Loading hints into the MySQL database works exactly the same as loading genomes
into the database. After the genome files are loaded, call 'load2db' to load
hints for a particular species. Use the same species identifier as for the genomes:
load2db --species=hg19 --dbaccess=vertebrates,localhost,cgp,db_passwd human.hints.gff
load2db --species=mm9 --dbaccess=vertebrates,localhost,cgp,db_passwd mouse.hints.gff
prepare the extrinsic config file. Use config/extrinsic/extrinsic-cgp.cfg as template
call AUGUSTUS with --dbhints enabled:
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--dbaccess=vertebrates,localhost,cgp,db_passwd --dbhints=true \
--extrinsicCfgFile=cgp.extrinsic.cfg
# TRAINING OF CLADE-SPECIFIC PARAMETERS
(USUALLY NOT REQUIRED!!!)
Clade-specific parameters include the rates for exon gain and loss
--/CompPred/exon_loss=r
--/CompPred/exon_loss=r
as well as the scaling factor
--/CompPred/phylo_factor=f
If necessary, these parameters can be optimized similar to the meta parameters
in single species gene prediction using the script 'optimize_augustus.pl'.
In short, a range of parameter values is specified for each parameter in a
config file with the extension _metapars.cgp.cfg (e.g. human_metapars.cgp.cfg).
Different values in these ranges are tried out in several rounds and values
giving highest accuracy are chosen.
In the evaluation step, the external program Eval¹ and a reference gene set are required.
a) Installation of Eval
The software package eval by Keibler and Brent is required for retrieving accuracy
values of predictions.
It can be downloaded from
wget http://mblab.wustl.edu/media/software/eval-2.2.8.tar.gz
tar zxvf eval-2.2.8.tar.gz
add following lines to your .bashrc file to include the perl executable evaluate_gtf.pl
to your $PATH environment variable (optional), and the perl modules EVAL.pm and GTF.pm
to your $PERL5LIB environment variable (mandatory)
export PATH=$PATH:/path/to/eval-2.2.8
export PERL5LIB=$PERL5LIB:/path/to/eval-2.2.8
to check that the installation was successful, run following command
evaluate_gtf.pl -v /path/to/eval-2.2.8/chr22.refseq.gtf /path/to/eval-2.2.8/chr22.twinscan.gtf \
/path/to/eval-2.2.8/chr22.genscan.gtf
b) Running optimize_augustus.pl for cgp parameter training
Run optimize_augustus.pl and read the instructions in USAGE 2 for further information
optimize_augustus.pl
example code:
optimize_augustus.pl --species=human --treefile=tree.nwk --alnfile=aln.maf \
--dbaccess=db.vertebrates --speciesfilenames=genomes.tbl --eval_against=hg19 \
--stopCodonExcludedFromCDS=1 eval.gtf
The file eval.gtf contains a reference gene set for the human genome that is used
for evaluation.
¹Keibler, E. and M.R. Brent. 2003. "Eval: A software package for analysis of genome annotations."
BMC Bioinformatics 4:50.
# TRAINING CGP SCORE PARAMETERS
To train the parameters used to score exon and intron candidates you have two options:
1. if your training set (input alignment) is small, run AUGUSTUS as shown in the following example
```
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--speciesfilenames=genomes.tbl --referenceFile=referenceFeature.gff \
--refSpecies=hg19 --param_outfile=params.cfg
```
This command will not predict genes. Specifying a reference file with --referenceFile
will make AUGUSTUS train feature parameters used to score exon and intron candidates
using reference coding exons (CDS) and introns provided by the reference file.
referenceFeature.gff needs to be in gtf, gff or gff3 format.
All lines with type "intron" or "CDS" are used for training.
Other lines will be ignored. Note, that the program is case sensitive.
Stop codons need to be included in terminal coding exons.
Specify the reference species with --refSpecies. The reference species must be
one of the clade species and is denoted by the identifier used in the tree or
alignment file. Intron and CDS features in referenceFeature.gff must be
from the reference species.
The trained parameters are written to the file params.cfg.
After training, run AUGUSTUS in cgp mode with params.cfg as optional config file
```
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--speciesfilenames=genomes.tbl --optCfgFile=params.cfg
```
If --param_outfile is not specified parameters will be written to
$AUGUSTUS_CONFIG_PATH/cgp/log_reg_parameters_trained.cfg.
Of course the genomes can also be stored in MySQL or SQLite databases.
Adjust the commands accordingly.
2. if option 1. takes to long you can parallelize the training by splitting the
alignment file and run AUGUSTUS in parallel to collect all relevant attributes
of all exon and intron candidates in the training set as follows
```
mkdir run1 run2 ...
for dir in run*; do
augustus --species=human --treefile=tree.nwk --alnfile=$dir/aln.maf \
--speciesfilenames=genomes.tbl --exoncands=1 --/CompPred/outdir=$dir \
--outfile=$dir/aug.out --errfile=$dir/aug.err &
done
```
concatenate the following files after all jobs are done
```
cat run*/exonCands.refSpecies.gff3 run*/refSpecies.sampled_GFs.gff \
run*/orthoExons.refSpecies.gff3 > all_exon_intron_candidates.gff
```
run AUGUSTUS again, now in training mode using all_exon_intron_candidates.gff (this is fast)
```
augustus --species=human --treefile=tree.nwk --referenceFile=referenceFeature.gff \
--refSpecies=hg19 --trainFeatureFile=all_exon_intron_candidates.gff --param_outfile=params.cfg
```
The trained parameters are written to the file params.cfg.
After training, run AUGUSTUS in cgp mode with params.cfg as optional config file
```
augustus --species=human --treefile=tree.nwk --alnfile=aln.maf \
--speciesfilenames=genomes.tbl --optCfgFile=params.cfg
```
If --param_outfile is not specified parameters will be written to
$AUGUSTUS_CONFIG_PATH/cgp/log_reg_parameters_trained.cfg.
Of course the genomes can also be stored in MySQL or SQLite databases.
Adjust the commands accordingly.