NAME
grinder - A versatile omics shotgun and amplicon sequencing read
simulator
DESCRIPTION
Grinder is a versatile program to create random shotgun and amplicon
sequence libraries based on DNA, RNA or proteic reference sequences
provided in a FASTA file.
Grinder can produce genomic, metagenomic, transcriptomic,
metatranscriptomic, proteomic, metaproteomic shotgun and amplicon
datasets from current sequencing technologies such as Sanger, 454,
Illumina. These simulated datasets can be used to test the accuracy of
bioinformatic tools under specific hypothesis, e.g. with or without
sequencing errors, or with low or high community diversity. Grinder may
also be used to help decide between alternative sequencing methods for a
sequence-based project, e.g. should the library be paired-end or not,
how many reads should be sequenced.
Grinder features include:
* shotgun or amplicon read libraries
* omics support to generate genomic, transcriptomic, proteomic,
metagenomic, metatranscriptomic or metaproteomic datasets
* arbitrary read length distribution and number of reads
* simulation of PCR and sequencing errors (chimeras, point mutations,
homopolymers)
* support for paired-end (mate pair) datasets
* specific rank-abundance settings or manually given abundance for
each genome, gene or protein
* creation of datasets with a given richness (alpha diversity)
* independent datasets can share a variable number of genomes (beta
diversity)
* modeling of the bias created by varying genome lengths or gene copy
number
* profile mechanism to store preferred options
* available to biologists or power users through multiple interfaces:
GUI, CLI and API
Briefly, given a FASTA file containing reference sequence (genomes,
genes, transcripts or proteins), Grinder performs the following steps:
1. Read the reference sequences, and for amplicon datasets, extracts
full-length reference PCR amplicons using the provided degenerate
PCR primers.
2. Determine the community structure based on the provided alpha
diversity (number of reference sequences in the library), beta
diversity (number of reference sequences in common between several
independent libraries) and specified rank- abundance model.
3. Take shotgun reads from the reference sequences or amplicon reads
from the full- length reference PCR amplicons. The reads may be
paired-end reads when an insert size distribution is specified. The
length of the reads depends on the provided read length distribution
and their abundance depends on the relative abundance in the
community structure. Genome length may also biases the number of
reads to take for shotgun datasets at this step. Similarly, for
amplicon datasets, the number of copies of the target gene in the
reference genomes may bias the number of reads to take.
4. Alter reads by inserting sequencing errors (indels, substitutions
and homopolymer errors) following a position-specific model to
simulate reads created by current sequencing technologies (Sanger,
454, Illumina). Write the reads and their quality scores in FASTA,
QUAL and FASTQ files.
CITATION
If you use Grinder in your research, please cite:
Angly FE, Willner D, Rohwer F, Hugenholtz P, Tyson GW (2012), Grinder: a
versatile amplicon and shotgun sequence simulator, Nucleic Acids Reseach
Available from <http://dx.doi.org/10.1093/nar/gks251>.
VERSION
This document refers to grinder version 0.5.3
AUTHOR
Florent Angly <florent.angly@gmail.com>
INSTALLATION
Dependencies
You need to install these dependencies first:
* Perl (>= 5.6)
<http://www.perl.com/download.csp>
* make
Many systems have make installed by default. If your system does
not, you should install the implementation of make of your choice,
e.g. GNU make: <http://www.gnu.org/s/make/>
The following CPAN Perl modules are dependencies that will be installed
automatically for you:
* Bioperl modules (>=1.6.901).
Note that some unreleased Bioperl modules have been included in
Grinder.
* Getopt::Euclid (>= 0.3.4)
* List::Util
First released with Perl v5.7.3
* Math::Random::MT (>= 1.13)
* version (>= 0.77)
First released with Perl v5.9.0
Procedure
To install Grinder globally on your system, run the following commands
in a terminal or command prompt:
On Linux, Unix, MacOS:
perl Makefile.PL
make
And finally, with administrator privileges:
make install
On Windows, run the same commands but with nmake instead of make.
No administrator privileges?
If you do not have administrator privileges, Grinder needs to be
installed in your home directory.
First, follow the instructions to install local::lib at
<http://search.cpan.org/~apeiron/local-lib-1.008004/lib/local/lib.pm#The
_bootstrapping_technique>. After local::lib is installed, every Perl
module that you install manually or through the CPAN command-line
application will be installed in your home directory.
Then, install Grinder by following the instructions detailed in the
"Procedure" section.
RUNNING GRINDER
After installation, you can run Grinder using a command-line interface
(CLI), an application programming interface (API) or a graphical user
interface (GUI) in Galaxy.
To get the usage of the CLI, type:
grinder --help
More information, including the documentation of the Grinder API, which
allows you to run Grinder from within other Perl programs, is available
by typing:
perldoc Grinder
To run the GUI, refer to the Galaxy documentation at
<http://wiki.g2.bx.psu.edu/FrontPage>.
The 'utils' folder included in the Grinder package contains some
utilities:
average genome size:
This calculates the average genome size (in bp) of a simulated
random library produced by Grinder.
change_paired_read_orientation:
This reverses the orientation of each second mate-pair read (ID
ending in /2) in a FASTA file.
REFERENCE SEQUENCE DATABASE
A variety of FASTA databases can be used as input for Grinder. For
example, the GreenGenes database
(<http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_data_files/Isol
ated_named_strains_16S_aligned.fasta>) contains over 180,000 16S rRNA
clone sequences from various species which would be appropriate to
produce a 16S rRNA amplicon dataset. A set of over 41,000 OTU
representative sequences and their affiliation in seven different
taxonomic sytems can also be used for the same purpose
(<http://greengenes.lbl.gov/Download/OTUs/gg_otus_6oct2010/rep_set/gg_97
_otus_6oct2010.fasta> and
<http://greengenes.lbl.gov/Download/OTUs/gg_otus_6oct2010/taxonomies/>).
The RDP (<http://rdp.cme.msu.edu/download/release10_27_unaligned.fa.gz>)
and Silva
(<http://www.arb-silva.de/no_cache/download/archive/release_108/Exports/
>) databases also provide many 16S rRNA sequences and Silva includes
eukaryotic sequences. While 16S rRNA is a popular gene, datasets
containing any type of gene could be used in the same fashion to
generate simulated amplicon datasets, provided appropriate primers are
used.
The >2,400 curated microbial genome sequences in the NCBI RefSeq
collection (<ftp://ftp.ncbi.nih.gov/refseq/release/microbial/>) would
also be suitable for producing 16S rRNA simulated datasets (using the
adequate primers). However, the lower diversity of this database
compared to the previous two makes it more appropriate for producing
artificial microbial metagenomes. Individual genomes from this database
are also very suitable for the simulation of single or double-barreled
shotgun libraries. Similarly, the RefSeq database contains over 3,100
curated viral sequences (<ftp://ftp.ncbi.nih.gov/refseq/release/viral/>)
which can be used to produce artificial viral metagenomes.
Quite a few eukaryotic organisms have been sequenced and their genome or
genes can be the basis for simulating genomic, transcriptomic (RNA-seq)
or proteomic datasets. For example, you can use the human genome
available at <ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/RefSeqGene/>, the
human transcripts downloadable from
<ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/mRNA_Prot/human.rna.fna.gz> or
the human proteome at
<ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/mRNA_Prot/human.protein.faa.gz>
.
CLI EXAMPLES
Here are a few examples that illustrate the use of Grinder in a
terminal:
1. A shotgun DNA library with a coverage of 0.1X
grinder -reference_file genomes.fna -coverage_fold 0.1
2. Same thing but save the result files in a specific folder and with a
specific name
grinder -reference_file genomes.fna -coverage_fold 0.1 -base_name my_name -output_dir my_dir
3. A DNA shotgun library with 1000 reads
grinder -reference_file genomes.fna -total_reads 1000
4. A DNA shotgun library where species are distributed according to a
power law
grinder -reference_file genomes.fna -abundance_model powerlaw 0.1
5. A DNA shotgun library with 123 genomes taken random from the given
genomes
grinder -reference_file genomes.fna -diversity 123
6. Two DNA shotgun libraries that have 50% of the species in common
grinder -reference_file genomes.fna -num_libraries 2 -shared_perc 50
7. Two DNA shotgun library with no species in common and distributed
according to a exponential rank-abundance model. Note that because
the parameter value for the exponential model is omitted, each
library uses a different randomly chosen value:
grinder -reference_file genomes.fna -num_libraries 2 -abundance_model exponential
8. A DNA shotgun library where species relative abundances are manually
specified
grinder -reference_file genomes.fna -abundance_file my_abundances.txt
9. A DNA shotgun library with Sanger reads
grinder -reference_file genomes.fna -read_dist 800 -mutation_dist linear 1 2 -mutation_ratio 80 20
10. A DNA shotgun library with first-generation 454 reads
grinder -reference_file genomes.fna -read_dist 100 normal 10 -homopolymer_dist balzer
11. A paired-end DNA shotgun library, where the insert size is normally
distributed around 2.5 kbp and has 0.2 kbp standard deviation
grinder -reference_file genomes.fna -insert_dist 2500 normal 200
12. A transcriptomic dataset
grinder -reference_file transcripts.fna
13. A unidirectional transcriptomic dataset
grinder -reference_file transcripts.fna -unidirectional 1
Note the use of -unidirectional 1 to prevent reads to be taken from
the reverse- complement of the reference sequences.
14. A proteomic dataset
grinder -reference_file proteins.faa -unidirectional 1
15. A 16S rRNA amplicon library
grinder -reference_file 16Sgenes.fna -forward_reverse 16Sprimers.fna -length_bias 0 -unidirectional 1
Note the use of -length_bias 0 because reference sequence length
should not affect the relative abundance of amplicons.
16. The same amplicon library with 20% of chimeric reads (90% bimera,
10% trimera)
grinder -reference_file 16Sgenes.fna -forward_reverse 16Sprimers.fna -length_bias 0 -unidirectional 1 -chimera_perc 20 -chimera_dist 90 10
17. Three 16S rRNA amplicon libraries with specified MIDs and no
reference sequences in common
grinder -reference_file 16Sgenes.fna -forward_reverse 16Sprimers.fna -length_bias 0 -unidirectional 1 -num_libraries 3 -multiplex_ids MIDs.fna
18. Reading reference sequences from the standard input, which allows
you to decompress FASTA files on the fly:
zcat microbial_db.fna.gz | grinder -reference_file - -total_reads 100
CLI REQUIRED ARGUMENTS
-rf <reference_file> | -reference_file <reference_file> | -gf
<reference_file> | -genome_file <reference_file>
FASTA file that contains the input reference sequences (full
genomes, 16S rRNA genes, transcripts, proteins...) or '-' to read
them from the standard input. See the README file for examples of
databases you can use and where to get them from. Default: -
CLI OPTIONAL ARGUMENTS
-tr <total_reads> | -total_reads <total_reads>
Number of shotgun or amplicon reads to generate for each library. Do
not specify this if you specify the fold coverage. Default: 100
-cf <coverage_fold> | -coverage_fold <coverage_fold>
Desired fold coverage of the input reference sequences (the output
FASTA length divided by the input FASTA length). Do not specify this
if you specify the number of reads directly.
-rd <read_dist>... | -read_dist <read_dist>...
Desired shotgun or amplicon read length distribution specified as:
average length, distribution ('uniform' or 'normal') and standard
deviation.
Only the first element is required. Examples:
All reads exactly 101 bp long (Illumina GA 2x): 101
Uniform read distribution around 100+-10 bp: 100 uniform 10
Reads normally distributed with an average of 800 and a standard deviation of 100
bp (Sanger reads): 800 normal 100
Reads normally distributed with an average of 450 and a standard deviation of 50
bp (454 GS-FLX Ti): 450 normal 50
Reference sequences smaller than the specified read length are not
used. Default: 100
-id <insert_dist>... | -insert_dist <insert_dist>...
Create paired-end or mate-pair reads spanning the given insert
length. Important: the insert is defined in the biological sense,
i.e. its length includes the length of both reads and of the stretch
of DNA between them: 0 : off, or: insert size distribution in bp, in
the same format as the read length distribution (a typical value is
2,500 bp for mate pairs) Two distinct reads are generated whether or
not the mate pair overlaps. Default: 0
-mo <mate_orientation> | -mate_orientation <mate_orientation>
When generating paired-end or mate-pair reads (see <insert_dist>),
specify the orientation of the reads (F: forward, R: reverse):
FR: ---> <--- e.g. Sanger, Illumina paired-end, IonTorrent mate-pair
FF: ---> ---> e.g. 454
RF: <--- ---> e.g. Illumina mate-pair
RR: <--- <---
Default: FR
-ec <exclude_chars> | -exclude_chars <exclude_chars>
Do not create reads containing any of the specified characters (case
insensitive). For example, use 'NX' to prevent reads with
ambiguities (N or X). Grinder will error if it fails to find a
suitable read (or pair of reads) after 10 attempts. Consider using
<delete_chars>, which may be more appropriate for your case.
Default: ''
-dc <delete_chars> | -delete_chars <delete_chars>
Remove the specified characters from the reference sequences
(case-insensitive), e.g. '-~*' to remove gaps (- or ~) or terminator
(*). Removing these characters is done once, when reading the
reference sequences, prior to taking reads. Hence it is more
efficient than <exclude_chars>. Default:
-fr <forward_reverse> | -forward_reverse <forward_reverse>
Use DNA amplicon sequencing using a forward and reverse PCR primer
sequence provided in a FASTA file. The reference sequences and their
reverse complement will be searched for PCR primer matches. The
primer sequences should use the IUPAC convention for degenerate
residues and the reference sequences that that do not match the
specified primers are excluded. If your reference sequences are full
genomes, it is recommended to use <copy_bias> = 1 and <length_bias>
= 0 to generate amplicon reads. To sequence from the forward strand,
set <unidirectional> to 1 and put the forward primer first and
reverse primer second in the FASTA file. To sequence from the
reverse strand, invert the primers in the FASTA file and use
<unidirectional> = -1. The second primer sequence in the FASTA file
is always optional. Example: AAACTYAAAKGAATTGRCGG and
ACGGGCGGTGTGTRC for the 926F and 1392R primers that target the V6 to
V9 region of the 16S rRNA gene.
-un <unidirectional> | -unidirectional <unidirectional>
Instead of producing reads bidirectionally, from the reference
strand and its reverse complement, proceed unidirectionally, from
one strand only (forward or reverse). Values: 0 (off, i.e.
bidirectional), 1 (forward), -1 (reverse). Use <unidirectional> = 1
for amplicon and strand-specific transcriptomic or proteomic
datasets. Default: 0
-lb <length_bias> | -length_bias <length_bias>
In shotgun libraries, sample reference sequences proportionally to
their length. For example, in simulated microbial datasets, this
means that at the same relative abundance, larger genomes contribute
more reads than smaller genomes (and all genomes have the same fold
coverage). 0 = no, 1 = yes. Default: 1
-cb <copy_bias> | -copy_bias <copy_bias>
In amplicon libraries where full genomes are used as input, sample
species proportionally to the number of copies of the target gene:
at equal relative abundance, genomes that have multiple copies of
the target gene contribute more amplicon reads than genomes that
have a single copy. 0 = no, 1 = yes. Default: 1
-md <mutation_dist>... | -mutation_dist <mutation_dist>...
Introduce sequencing errors in the reads, under the form of
mutations (substitutions, insertions and deletions) at positions
that follow a specified distribution (with replacement): model
(uniform, linear, poly4), model parameters. For example, for a
uniform 0.1% error rate, use: uniform 0.1. To simulate Sanger
errors, use a linear model where the errror rate is 1% at the 5' end
of reads and 2% at the 3' end: linear 1 2. To model Illumina errors
using the 4th degree polynome 3e-3 + 3.3e-8 * i^4 (Korbel et al
2009), use: poly4 3e-3 3.3e-8. Use the <mutation_ratio> option to
alter how many of these mutations are substitutions or indels.
Default: uniform 0 0
-mr <mutation_ratio>... | -mutation_ratio <mutation_ratio>...
Indicate the percentage of substitutions and the number of indels
(insertions and deletions). For example, use '80 20' (4
substitutions for each indel) for Sanger reads. Note that this
parameter has no effect unless you specify the <mutation_dist>
option. Default: 80 20
-hd <homopolymer_dist> | -homopolymer_dist <homopolymer_dist>
Introduce sequencing errors in the reads under the form of
homopolymeric stretches (e.g. AAA, CCCCC) using a specified model
where the homopolymer length follows a normal distribution N(mean,
standard deviation) that is function of the homopolymer length n:
Margulies: N(n, 0.15 * n) , Margulies et al. 2005.
Richter : N(n, 0.15 * sqrt(n)) , Richter et al. 2008.
Balzer : N(n, 0.03494 + n * 0.06856) , Balzer et al. 2010.
Default: 0
-cp <chimera_perc> | -chimera_perc <chimera_perc>
Specify the percent of reads in amplicon libraries that should be
chimeric sequences. The 'reference' field in the description of
chimeric reads will contain the ID of all the reference sequences
forming the chimeric template. A typical value is 10% for amplicons.
This option can be used to generate chimeric shotgun reads as well.
Default: 0 %
-cd <chimera_dist>... | -chimera_dist <chimera_dist>...
Specify the distribution of chimeras: bimeras, trimeras, quadrameras
and multimeras of higher order. The default is the average values
from Quince et al. 2011: '314 38 1', which corresponds to 89% of
bimeras, 11% of trimeras and 0.3% of quadrameras. Note that this
option only takes effect when you request the generation of chimeras
with the <chimera_perc> option. Default: 314 38 1
-ck <chimera_kmer> | -chimera_kmer <chimera_kmer>
Activate a method to form chimeras by picking breakpoints at places
where k-mers are shared between sequences. <chimera_kmer> represents
k, the length of the k-mers (in bp). The longer the kmer, the more
similar the sequences have to be to be eligible to form chimeras.
The more frequent a k-mer is in the pool of reference sequences
(taking into account their relative abundance), the more often this
k-mer will be chosen. For example, CHSIM (Edgar et al. 2011) uses
this method with a k-mer length of 10 bp. If you do not want to use
k-mer information to form chimeras, use 0, which will result in the
reference sequences and breakpoints to be taken randomly on the
"aligned" reference sequences. Note that this option only takes
effect when you request the generation of chimeras with the
<chimera_perc> option. Also, this options is quite memory intensive,
so you should probably limit yourself to a relatively small number
of reference sequences if you want to use it. Default: 10 bp
-af <abundance_file> | -abundance_file <abundance_file>
Specify the relative abundance of the reference sequences manually
in an input file. Each line of the file should contain a sequence
name and its relative abundance (%), e.g. 'seqABC 82.1' or 'seqABC
82.1 10.2' if you are specifying two different libraries.
-am <abundance_model>... | -abundance_model <abundance_model>...
Relative abundance model for the input reference sequences: uniform,
linear, powerlaw, logarithmic or exponential. The uniform and linear
models do not require a parameter, but the other models take a
parameter in the range [0, infinity). If this parameter is not
specified, then it is randomly chosen. Examples:
uniform distribution: uniform
powerlaw distribution with parameter 0.1: powerlaw 0.1
exponential distribution with automatically chosen parameter: exponential
Default: uniform 1
-nl <num_libraries> | -num_libraries <num_libraries>
Number of independent libraries to create. Specify how diverse and
similar they should be with <diversity>, <shared_perc> and
<permuted_perc>. Assign them different MID tags with
<multiplex_mids>. Default: 1
-mi <multiplex_ids> | -multiplex_ids <multiplex_ids>
Specify an optional FASTA file that contains multiplex sequence
identifiers (a.k.a MIDs or barcodes) to add to the sequences (one
sequence per library, in the order given). The MIDs are included in
the length specified with the -read_dist option and can be altered
by sequencing errors. See the MIDesigner or BarCrawl programs to
generate MID sequences.
-di <diversity>... | -diversity <diversity>...
This option specifies alpha diversity, specifically the richness,
i.e. number of reference sequences to take randomly and include in
each library. Use 0 for the maximum richness possible (based on the
number of reference sequences available). Provide one value to make
all libraries have the same diversity, or one richness value per
library otherwise. Default: 0
-sp <shared_perc> | -shared_perc <shared_perc>
This option controls an aspect of beta-diversity. When creating
multiple libraries, specify the percent of reference sequences they
should have in common (relative to the diversity of the least
diverse library). Default: 0 %
-pp <permuted_perc> | -permuted_perc <permuted_perc>
This option controls another aspect of beta-diversity. For multiple
libraries, choose the percent of the most-abundant reference
sequences to permute (randomly shuffle) the rank-abundance of.
Default: 100 %
-rs <random_seed> | -random_seed <random_seed>
Seed number to use for the pseudo-random number generator.
-dt <desc_track> | -desc_track <desc_track>
Track read information (reference sequence, position, errors, ...)
by writing it in the read description. Default: 1
-ql <qual_levels>... | -qual_levels <qual_levels>...
Generate basic quality scores for the simulated reads. Good residues
are given a specified good score (e.g. 30) and residues that are the
result of an insertion or substitution are given a specified bad
score (e.g. 10). Specify first the good score and then the bad score
on the command-line, e.g.: 30 10. Default:
-fq <fastq_output> | -fastq_output <fastq_output>
Whether to write the generated reads in FASTQ format (with
Sanger-encoded quality scores) instead of FASTA and QUAL or not (1:
yes, 0: no). <qual_levels> need to be specified for this option to
be effective. Default: 0
-bn <base_name> | -base_name <base_name>
Prefix of the output files. Default: grinder
-od <output_dir> | -output_dir <output_dir>
Directory where the results should be written. This folder will be
created if needed. Default: .
-pf <profile_file> | -profile_file <profile_file>
A file that contains Grinder arguments. This is useful if you use
many options or often use the same options. Lines with comments (#)
are ignored. Consider the profile file, 'simple_profile.txt':
# A simple Grinder profile
-read_dist 105 normal 12
-total_reads 1000
Running: grinder -reference_file viral_genomes.fa -profile_file
simple_profile.txt
Translates into: grinder -reference_file viral_genomes.fa -read_dist
105 normal 12 -total_reads 1000
Note that the arguments specified in the profile should not be
specified again on the command line.
CLI OUTPUT
For each shotgun or amplicon read library requested, the following files
are generated:
* A rank-abundance file, tab-delimited, that shows the relative
abundance of the different reference sequences
* A file containing the read sequences in FASTA format. The read
headers contain information necessary to track from which reference
sequence each read was taken and what errors it contains. This file
is not generated if <fastq_output> option was provided.
* If the <qual_levels> option was specified, a file containing the
quality scores of the reads (in QUAL format).
* If the <fastq_output> option was provided, a file containing the
read sequences in FASTQ format.
API EXAMPLES
The Grinder API allows to conveniently use Grinder within Perl scripts.
Here is a synopsis:
use Grinder;
# Set up a new factory (see the OPTIONS section for a complete list of parameters)
my $factory = Grinder->new( -reference_file => 'genomes.fna' );
# Process all shotgun libraries requested
while ( my $struct = $factory->next_lib ) {
# The ID and abundance of the 3rd most abundant genome in this community
my $id = $struct->{ids}->[2];
my $ab = $struct->{abs}->[2];
# Create shotgun reads
while ( my $read = $factory->next_read) {
# The read is a Bioperl sequence object with these properties:
my $read_id = $read->id; # read ID given by Grinder
my $read_seq = $read->seq; # nucleotide sequence
my $read_mid = $read->mid; # MID or tag attached to the read
my $read_errors = $read->errors; # errors that the read contains
# Where was the read taken from? The reference sequence refers to the
# database sequence for shotgun libraries, amplicon obtained from the
# database sequence, or could even be a chimeric sequence
my $ref_id = $read->reference->id; # ID of the reference sequence
my $ref_start = $read->start; # start of the read on the reference
my $ref_end = $read->end; # end of the read on the reference
my $ref_strand = $read->strand; # strand of the reference
}
}
# Similarly, for shotgun mate pairs
my $factory = Grinder->new( -reference_file => 'genomes.fna',
-insert_dist => 250 );
while ( $factory->next_lib ) {
while ( my $read = $factory->next_read ) {
# The first read is the first mate of the mate pair
# The second read is the second mate of the mate pair
# The third read is the first mate of the next mate pair
# ...
}
}
# To generate an amplicon library
my $factory = Grinder->new( -reference_file => 'genomes.fna',
-forward_reverse => '16Sgenes.fna',
-length_bias => 0,
-unidirectional => 1 );
while ( $factory->next_lib ) {
while ( my $read = $factory->next_read) {
# ...
}
}
API METHODS
The rest of the documentation details the available Grinder API methods.
new
Title : new
Function: Create a new Grinder factory initialized with the passed
arguments. Available parameters described in the OPTIONS section.
Usage : my $factory = Grinder->new( -reference_file => 'genomes.fna' );
Returns : a new Grinder object
next_lib
Title : next_lib
Function: Go to the next shotgun library to process.
Usage : my $struct = $factory->next_lib;
Returns : Community structure to be used for this library, where
$struct->{ids} is an array reference containing the IDs of the genome
making up the community (sorted by decreasing relative abundance) and
$struct->{abs} is an array reference of the genome abundances (in the
same order as the IDs).
next_read
Title : next_read
Function: Create an amplicon or shotgun read for the current library.
Usage : my $read = $factory->next_read; # for single read my $mate1 =
$factory->next_read; # for mate pairs my $mate2 = $factory->next_read;
Returns : A sequence represented as a Bio::Seq::SimulatedRead object
get_random_seed
Title : get_random_seed
Function: Return the number used to seed the pseudo-random number
generator
Usage : my $seed = $factory->get_random_seed;
Returns : seed number
COPYRIGHT
Copyright 2009-2013 Florent ANGLY <florent.angly@gmail.com>
Grinder is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License (GPL) as published by the
Free Software Foundation, either version 3 of the License, or (at your
option) any later version. Grinder is distributed in the hope that it
will be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details. You should have received a
copy of the GNU General Public License along with Grinder. If not, see
<http://www.gnu.org/licenses/>.
BUGS
All complex software has bugs lurking in it, and this program is no
exception. If you find a bug, please report it on the SourceForge
Tracker for Grinder:
<http://sourceforge.net/tracker/?group_id=244196&atid=1124737>
Bug reports, suggestions and patches are welcome. Grinder's code is
developed on Sourceforge
(<http://sourceforge.net/scm/?type=git&group_id=244196>) and is under
Git revision control. To get started with a patch, do:
git clone git://biogrinder.git.sourceforge.net/gitroot/biogrinder/biogrinder