README

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 0.5.2

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). 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: 0 %

    -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 2009-2012 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