Provided by: grinder_0.5.3-3_all bug

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

       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/Isolated_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:
           reference_file.default

CLI OPTIONAL ARGUMENTS

       Basic parameters

       -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: total_reads.default

       -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.

       Advanced shotgun and amplicon parameters

       -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:
           read_dist.default

       -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: insert_dist.default

       -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: mate_orientation.default

       -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:
           'exclude_chars.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: delete_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:
           unidirectional.default

       -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: length_bias.default

       -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:
           copy_bias.default

       Aberrations and sequencing errors

       -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:
           mutation_dist.default

       -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: mutation_ratio.default

       -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: homopolymer_dist.default

       -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: chimera_perc.default %

       -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: chimera_dist.default

       -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: chimera_kmer.default bp

       Community structure and diversity

       -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: abundance_model.default

       -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: num_libraries.default

       -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: diversity.default

       -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: shared_perc.default %

       -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: permuted_perc.default %

       Miscellaneous

       -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: desc_track.default

       -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: qual_levels.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: fastq_output.default

       -bn <base_name> | -base_name <base_name>
           Prefix of the output files. Default: base_name.default

       -od <output_dir> | -output_dir <output_dir>
           Directory where the results should be written. This folder will be created if needed.
           Default: output_dir.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