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项目作者： wpwupingwp

项目描述 ：
all-in-one solution for discovering novel DNA barcode
高级语言： Python
项目地址: git://github.com/wpwupingwp/BarcodeFinder.git
创建时间： 2017-11-22T02:25:51Z
项目社区：https://github.com/wpwupingwp/BarcodeFinder
开源协议：GNU Affero General Public License v3.0
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PyPI version

Quick start

  • Install Python 3 (3.9 or newer).

  • Open terminal, run

    1.  # Install, using pip (recommended)
    2.  pip3 install OGU --user
    4.  # Initialize with Internet
    5.  # Windows
    6.  python -m OGU init
    7.  # Linux and macOS
    8.  python3 -m OGU init
    10.  # Run
    11.  # Windows
    12.  python -m OGU
    13.  # Linux and macOS
    14.  python3 -m OGU

Table of Contents

Features

:heavy_check_mark: Automatically collect, organize and clean sequence data
from NCBI GenBank or local: collect data with abundant options; extract CDS,
intergenic spacer, or any other annotations from original sequence; remove
redundant sequences according to species information; remove invalid or
abnormal sequences/fragments; generate clean dataset with uniform sequence id.

:heavy_check_mark: Evaluate variance of sequences by calculating nucleotide
diversity, observed resolution, Shannon index, tree resolution, phylogenetic
diversity (original and edited version), gap ratio, and others. Support
sliding-window scanning.

:heavy_check_mark: Design universal primer for the alignment. Support
ambiguous bases in primers.

:heavy_check_mark: Visualize the evolution pattern of different gene or non-coding
sequences in the organelle of one taxa instead of a single species.

Prerequisite

Hardware

Organelle Genome Utilities (OGU) requires very few computational resources.
A normal PC/laptop is enough. For downloading large amount of data, make sure
the Internet connection is stable and fast enough.

Software

For the portable version, nothing need to be installed manually.

For installing from pip, Python is
required. Notice that the python version should be higher than 3.8.

:white_check_mark: All third-party dependencies will be automatically
installed with Internet, including biopython, matplotlib, coloredlogs,
numpy, primer3-py, (python packages), and
MAFFT,
IQTREE,
BLAST.

Installation

We assume that users have already installed
Python3 (3.9 or above).

Install with pip

  1. Install Python. 3.9 or newer is
    required.

  2. Open command line, run

  1. pip3 install OGU --user

Initialization

During the first running, OGU will check and initialize the
running environment. Missing dependencies will be automatically installed.

This step requires Internet connection.

By default, the program will automatically finish initialization, if any error
occurs, users can choose one of the following methods:

Automatic

Run the following command.

  1. # Windows
  2. python -m OGU init
  3. # Linux and macOS
  4. python3 -m OGU init

Use prepared package

According to your system, download related compressed file
from packages.

For Windows users:

  1. paste %HOMEDRIVE%%HOMEPATH%/ to explorer’s address bar and open.
  2. create .OGU folder. Don’t miss the dot.
  3. open .OGU folder, paste downloaded compressed file and unzip. Make sure after
    decompress there are three folders in .OGU.

For Linux and macOS users, please download and unpack files into
~/.OGU.

Manually install

For Linux users with root privileges, just use the package manager:

  1. # Ubuntu and Debian
  2. sudo apt install mafft ncbi-blast+ iqtree
  3. # Fedora (1)
  4. sudo dnf install mafft ncbi-blast+ iqtree
  5. # Fedora (2)
  6. sudo yum install mafft ncbi-blast+ iqtree
  7. # ArchLinux
  8. sudo pacman -S mafft ncbi-blast+ iqtree
  9. # FreeBSD
  10. sudo pkg install mafft ncbi-blast+ iqtree

For macOS users with root privileges, install brew if it has not been
installed previously:

  1. /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

If any errors occur, install Xcode-select and retry.

Then:

  1. brew install blast mafft brewsci/science/iqtree

If using Windows or lacking root privileges, users should follow these
instructions:

  1. BLAST+

  2. MAFFT

    • Windows

      Choose “All-in-one version”, download and unzip. Then follow the steps
      in the BLAST+ installation manual to set the PATH.

    • Linux

      Choose “Portable package”, download and unzip. Then follow the
      instructions of BLAST+ to set the PATH for MAFFT.

    • macOS

      Choose “All-in-one version”, download and unzip. Then follow the steps
      in the BLAST+ installation manual to set the PATH.

  3. IQ-TREE

    • Download

      Download the installer according to OS. Unzip and add the path of
      subfolder bin to PATH.

Usage

Graphical user interface

Open the command line (Windows) or terminal (Linux and macOS),
run

  1. OGU

or

  1. # linux and macos
  2. python3 -m OGU
  3. # windows
  4. python -m OGU

command line

Once a user opens the command line (Windows) or terminal (Linux and macOS),
just type the command:

  1. # Windows
  2. python -m OGU [input] -[options] -out [out_folder]
  3. # Linux and macOS
  4. python3 -m OGU [input] -[options] -out [out_folder]

Quick examples

  1. Download all rbcL sequences of species in Poaceae family and do
    pre-process.
  1. # Windows
  2. python -m OGU.gb2fasta -gene rbcL -taxon Poaceae -out rbcL_Poaceae
  3. # Linux and macOS
  4. python3 -m OGU.gb2fasta -gene rbcL -taxon Poaceae -out rbcL_Poaceae
  1. Download all ITS sequences of Rosa genus. Do pre-process and keep redundant
    sequences:
  1. # Windows
  2. python -m OGU.gb2fasta -query internal transcribed spacer -taxon Rosa -out Rosa_its -uniq no
  3. # Linux and macOS
  4. python3 -m OGU.gb2fasta -query internal transcribed spacer -taxon Rosa -out Rosa_its -uniq no
  1. Download all Lamiaceae chloroplast genomic sequences in the RefSeq database.
    Then do pre-process and evaluation of variance (skip primer designing):
  1. # Windows
  2. python -m OGU -og cp -refseq yes -taxon Lamiaceae -out Lamiaceae_cp
  3. # Linux and macOS
  4. python3 -m OGU -og cp -refseq yes -taxon Lamiaceae -out Lamiaceae_cp
  1. Download sequences of Zea mays, set length between 100 bp and 3000 bp,
    and then perform evaluation and primer designing. Note that the space in
    the species name is replaced with underscore “_“.
  1. # Windows
  2. python -m OGU -taxon Zea_mays -min_len 100 -max_len 3000 -out Zea_mays -primer
  3. # Linux and macOS
  4. python3 -m OGU -taxon Zea_mays -min_len 100 -max_len 3000 -out Zea_mays -primer
  1. Download all Oryza mitochondria genomes in RefSeq database, keep the
    longest sequence for each species and run a full analysis:
  1. # Windows
  2. python -m OGU -taxon Oryza -og mt -min_len 50000 -max_len 200000 -uniq longest -out Oryza_cp -refseq yes -primer
  3. # Linux and macOS
  4. python3 -m OGU -taxon Oryza -og mt -min_len 50000 -max_len 200000 -uniq longest -out Oryza_cp -refseq yes -primer

Sequence ID

Organelle Genome Utilities uses a uniform sequence id format for input fasta files and all output
sequences.

  1. Locus|Kingdom|Phylum|Class|Order|Family|Genus|Species|Accession|SpecimenID_Isolate|Type
  2. # example
  3. rbcL|Viridiplantae|Streptophyta|Magnoliopsida|Poales|Poaceae|Oryza|longistaminata|MF998442|TAN:GB60B-2014|gene

The order of the fields is fixed. The fields are separated by vertical bars
(“|”). The space character (“ “) was disallowed and was replaced by an
underscore (“_“). Due to missing data, some fields may be empty.

Locus: SeqName refers to the name of a sequence. Usually it is the gene
name. For intergenic spacer, an underscore (“_“) is used to connect two
gene’s names, e.g., “geneA_geneB”.

If a valid sequence name cannot be found in the annotations of the GenBank
file, Organelle Genome Utilities will use “Unknown” instead.

For chloroplast genes, if “-rename” option is set, the program will try to use
regular expressions to fix potential errors in gene names.

Kingdom: The kingdom (Fungi, Viridiplantae, Metazoa) of a species. For
convenience, a superkingdom (Bacteria, Archaea, Eukaryota, Viruses, Viroids)
may be used if the kingdom information for a sequence is missing.

Phylum: The phylum of the species.

Class: The class of the species.

Because some species’ classes are empty (for instance, basal angiosperm),
Organelle Genome Utilities will guess the class of the species.

Given the taxonomy information in GenBank file:

  1. Eukaryota; Viridiplantae; Streptophyta; Embryophyta; Tracheophyta;
  2.     Spermatophyta; Magnoliophyta; basal Magnoliophyta; Amborellales;
  3.     Amborellaceae; Amborella.

Organelle Genome Utilities will use “basal Magnoliophyta” as the class because this
expression locates before the order name (“Amborellales”).

Order: The order name of the species.

Family: The family name of the species.

Genus: The genus name of the species, i.e., the first part of the scientific
name.

Species: The specific epithet of the species, i.e., the second part of the
scientific name of the species. It may contain the subspecies’ name.

Accession: The GenBank Accession number for the sequence. It does not
contain the record’s version.

SpecimenID and Isolate: Specimen ID and Isolate ID of the sequence. May be empty.

Type: Type of the sequence. It is usually “gene” or “spacer”.

Command line

:exclamation: In Linux and macOS, Python2 is python2 and Python3 is
python3. However, in Windows, Python3 is called python, too. Please
notice the difference.

  • Show help information of each module

    1. # Windows
    2. python -m OGU -h
    3. python -m OGU.gb2fasta -h
    4. python -m OGU.evaluate -h
    5. python -m OGU.primer -h
    6. # Linux and macOS
    7. python3 -m OGU.gb2fasta -h
    8. python3 -m OGU.evaluate -h
    9. python3 -m OGU.primer -h

  • Full process

    1. # Windows
    2. python -m OGU -gene [gene name] -taxon [taxon name] -og [organelle type] -out [output name]
    3. # Linux and macOS
    4. python3 -m OGU -gene [gene name] -taxon [taxon name] -og [organelle type] -out [output name]

  • Collect, convert, and clean GenBank data with gb2fasta module

    1. # Windows
    2. python -m OGU.GB2fasta -gene [gene name] -taxon [taxon name] -og [organelle type] -out [output name]
    3. # Linux and macOS
    4. python3 -m OGU.gb2fasta -gene [gene name] -taxon [taxon name] -og [organelle type] -out [output name]

  • Evaluate variance of given fasta files

    1. # Windows
    2. python -m OGU.evaluate -fasta [fasta files]
    3. # Linux and macOS
    4. python3 -m OGU.evaluate -fasta [input file]

  • Design universal primers of given alignments.

    1. # Windows
    2. python -m OGU.primer -aln [alignment files]
    3. # Linux and macOS
    4. python3 -m OGU.primer -aln [alignment files]

Visualize

This function is designed to visualize the evolution pattern of organelle genomes within a taxon (
order, family, or genus) rather than focusing on a single species.

Note

  1. It is recommended to select a reference genome with high-quality annotation. Bad annotation
    may lead to gene missing in the figure.
  2. For plastid genomes, a portion of the rps12 gene may be omitted from the figure due to the
    large size of its “intron”.
  3. Plastid genome structure information is required for visualization. Users can obtain size data
    from papers or use tools such as OGDRAW, NOVOWrap, or Chloroplot to determine the
    size.
  4. For mitochondrial genomes, the D-loop region may be omitted due to potential naming conflicts.
    Users are advised to review the Evaluation.csv file and reference genome file before running the
    visualization.
  5. The output figure is in PDF format. Users can edit the figure to improve its appearance,
    particularly for overlapping label text. Inkscape, an open-source, cross-platform and free
    software is recommended.

Input

  1. input_csv: The sequence variance evaluation result from the OGU.Evaluate module.
  2. ref_gb: A reference genome file containing only one organelle genome. Generated from
    OGU.GB2fasta with the “-out_debug” option. Since plastid and animal mitochondrial genome
    structures are generally stable, users only need to select one as a representative.
  3. taxon: If ref_gb is empty, OGU will automatically generate a reference genome file for
    visualization purposes. It is recommended to use this option instead of ref_gb, but users must
    ensure a stable internet connection.
  4. og_type: Organelle type. Use cp for plastid and mt for mitochondria.
  5. lsc, ssc, and ir: Plastid structure sizes. If the input is a mitochondria genome, leave
    these fields empty.
  6. output: Output folder. The result will be a “Figure.pdf” file within the folder.

Command-Line Interface

To use the command-line interface, run OGU.visualize.

Graphical User Interface

  1. Click Visualize on the main window of the GUI.
  2. Click Load example and select the organelle type to load example data. Or to input your own
    data.
  3. Click Run to execute the program.

Jupyter Notebooks

Jupyter notebooks are available for analyzing the visualization results in detail using circular
figures:

To use these notebooks:

  1. Install JupyterLab by running pip3 install jupyterlab.
  2. Double-click the notebook to open it in Jupyter Notebook, Visual Studio Code, or another preferred IDE.
  3. Edit the filename variable to point to the Evaluation.csv file obtained from OGU.Evaluate.
  4. Edit the gb_file variable to point to the extended gb file obtained from OGU.GB2fasta. Remember to generate it with the “-out_debug” option in OGU.gb2fasta.
  5. If visualizing plastid data, provide the lengths of LSC, SSC, IRa, and IRb. Or use the default values, which are based on Tobacum.
  6. Customize color themes as desired.
  7. Run all cells to generate the PDF figure output.

Input

Organelle Genome Utilities accepts:

  1. GenBank queries. Users can use “-query” or combine with any other filters;
  2. GenBank-format files.
  3. Unaligned fasta files. Each file is considered as one locus when evaluating
    the variance;
  4. Alignments (fasta format).

Output

All results will be put in the output folder. If the user does not set the
output path via “-out”, Organelle Genome Utilities will create a folder labelled “Result”.

In the output folder, several sub-folders will be created.

  • GenBank

    Raw GenBank files.

  • Divide

    Fasta files converted from the GenBank file. Each file represents a
    fragment of the original sequence according to the annotation.

    For instance, a record in a “rbcL.gb” file may also contain atpB gene’s
    sequences. The “rbcL.fasta” file does not contain any upstream/downstream
    sequences and “atpB_rbcL.fasta” does not have even one base of the atpB or
    rbcL gene, just the spacer (assuming the annotation is precise).

    User can skip this dividing step with the option “-no_divide”.

  • Fasta

    Raw fasta files users provided.

  • Unique

    Fasta files after removing redundant sequences.

  • Expanded_fasta

    To design primers, Organelle Genome Utilities extend a sequence to its
    upstream/downstream. Only used in the primer module.

  • Alignment

    Aligned fasta files.

    .aln: The aligned fasta files.

    .-consensus.fastq: The fastq format of the consensus sequence of the
    alignment. Note that it contains alignment gap (“-“). It is NOT
    RECOMMENDED to be used directly because the consensus-generating algorithm is
    optimised for primer design.

  • Evaluate

    Including output files from the evaluation module.

    .pdf: The PDF format of the figure containing the sliding-window scan
    result of the alignment.

    .csv: The CSV format file of the sliding-window scan result. "Index"
    means the location of the base in the alignment.

  • Primer

    Including output files from the primer module.

    .primer.fastq: The fastq format file of a primer’s sequence. It contains
    two sequences, and the direction is 5’ to 3’. The first is the forward
    primer, and the second is the reverse primer. The quality of each base is
    equal to its proportion of the column in the alignment. Note that the
    sequence may contain ambiguous bases if it was not disabled.

    .primers.csv: The list of primer pairs in CSV (comma-separated values
    text) format.

    .candidate.fasta: The candidate primers. This file may contain
    thousands of records. Do not recommend paying attention to it.

    .candidate.fastq: Again, the candidate primers. This time, each file has
    the quality information that equals to the proportion of the bases in the
    column of the alignment.

  • Temp

    Including temporary files. Could be safely deleted .

In the output folder, there are some other important output files:

  • Primers.csv

    The list of primer pairs in CSV (comma-separated values text) format.

    Its title:

    1.   Locus,Samples,Score,AvgProductLength,StdEV,MinProductLength,MaxProductLength,Coverage,Observed_Res,Tree_Res,PD_terminal,Entropy,LeftSeq,LeftTm,LeftAvgBitscore,LeftAvgMismatch,RightSeq,RightTm,RightAvgBitscore,RightAvgMismatch,DeltaTm,AlnStart,AlnEnd,AvgSeqStart,AvgSeqEnd

    Locus: The name of the locus/fragment.

    Samples: The number of sequences used to find this pair of primers.

    Score: The score of this pair of primers. Usually the higher, the better.

    AvgProductLength: The average length of the DNA fragment amplified by
    this pair of primers.

    StdEV: The standard deviation of the AvgProductLength. A higher number
    means the primer may amplify different lengths of DNA fragments.

    MinProductLength: The minimum length of an amplified fragment.

    MaxProductLength: The maximum length of an amplified fragment. Note that
    all of these fields are calculated using given sequences.

    Coverage: The coverage of this pair of primers over the sequences it
    used. Calculated with the BLAST result. High coverage means that the pair
    is much more “universal”.

    Observed_Res: The observed resolution of the sub-alignment sliced by
    the primer pair, which is equal to the number of unique sequences divided
    by the number of total sequences. The value is between 0 and 1.

    Tree_Res: The tree resolution of the sub-alignment, which is equal to
    the number of internal nodes on a phylogenetic tree (constructed from the
    alignment) divided by number of terminal nodes. The value is between 0 and
    1.

    PD_terminal: The average of the terminal branch’s length. It’s an edited
    version of the Phylogenetic Diversity for DNA barcoding evaluation.

    Entropy: The Shannon equitability index of the sub-alignment. The value
    is between 0 and 1.

    LeftSeq: Sequence of the forward primer. The direction is 5’ to 3’.

    LeftTm: The melting temperature of the forward primer. The unit is
    degree Celsius (°C).

    LeftAvgBitscore: The average raw bitscore of the forward primer, which
    is calculated by BLAST.

    LeftAvgMismatch: The average number of mismatched bases of the forward
    primer, as counted by BLAST.

    RightSeq: Sequence of reverse primer. The direction is 5’ to 3’.

    RightTm: The melting temperature of the reverse primer. The unit is
    degrees Celsius (°C).

    RightAvgBitscore: The average raw bitscore of the reverse primer, which
    is calculated by BLAST.

    RightAvgMismatch: The average number of mismatched bases of the reverse
    primer, as counted by BLAST.

    DeltaTm: The difference in the melting temperatures of the forward and
    reverse primers. A pair of primers with a high DeltaTm may result in
    failure during the PCR experiment.

    AlnStart: The location of the beginning of the forward primer (5’,
    leftmost of primer pairs) in the entire alignment.

    AlnEnd: The location of the end of the reverse primer (5’, rightmost of
    primer pairs) in the entire alignment.

    AvgSeqStart: The average beginning of the forward primer in the original
    sequences. ONLY USED FOR DEBUG.

    AvgSeqEnd: The average end of the forward primer in the original
    sequences. ONLY USED FOR DEBUG.

    The primer pairs are sorted by Score. Since the score may not fully
    satisfy the user’s specific considerations, it is suggested that primer
    pairs be chosen manually if the first primer pair fails during the PCR
    experiment.

  • Log.txt

    The log file. Contains all the information printed on the screen.

  • Evaluation.csv

    The summary of all loci/fragments, which only contains the variance
    information for each fragment. One of the new field, GapRatio, means the
    ratio of the gap (“-“) in the alignment. PD means the original version
    of the phylogenetic diversity and PD_stem means an alternative version
    of it which only calculate the length of the stem branch in the
    phylogenetic tree.

Options

Here are some general options for the program and submodule:

-h: Prints help messages of the program or one of the module.

-gb [filename]: User-provided GenBank file or files. Could be one or more
files that separated by space.

For instance,

  1. # one file
  2. -gb sequence.gb
  3. # multiple files
  4. -gb matK.gb rbcL.gb Oryza.gb Homo_sapiens.gb

-fasta [filename]: User-provided unaligned fasta files. Could be one or
multiple.

-aln [filename]: Alignment files that the user provides. Could be one or
multiple.

It only supports the fasta format. Ambiguous bases and gaps (“-“) are supported.

-out [folder name]: The output folder’s name. All results will be put into
the output folder. If the user does not set an output path via “-out”,
Organelle Genome Utilities will create a folder named “Result”.

OGU does not overwrite the existing folder with the same name.

It is HIGHLY RECOMMENDED to use only letters, numbers and underscores (“_“) in
the folder name to avoid mysterious errors caused by other Unicode characters.

Options below are for specific modules.

gb2fasta

Query

Options used for querying NCBI GenBank.

-taxon [taxonomy name]: The taxonomy name. It could be any taxonomic rank
from kingdom (same as “-group”) to species, as long as the user inputs correct
name (the scientific name of species or taxonomic group in latin, NOT
ENGLISH). It will restrict the query to the targeted taxonomy unit. Make sure
to use quotation marks if taxonomy has more than one word or use underscore
to replace space, for instance "Zea mays" or Zea_mays.

-gene [gene name]: The gene’s name which the user wants to query in GenBank.
If the user wants to use logical expressions like “OR”, “AND”, “NOT”, s/he
should use “-query” instead. If there is space in the gene’s name, make sure
to use quotation marks.

Note that “ITS” is not a gene name—it is “internal transcribed spacer”.

Sometimes “-gene” options may bring in unwanted sequences. For example, if a
user queries “rbcL[gene]” in GenBank, spacer sequences may contain rbcL or
rbcL‘s upstream/downstream gene, such as “atpBrbcL spacer” or _atpB.

-og [ignore|both|no|mt|mitochondrion|cp|chloroplast|pl|plastid]: Query
organelle sequences or not. The default value is ignore.

  1. - `ignore`: do not consider organelle type, same as GenBank website's
  2.   default setting.
  4. - `both`: only query organelle sequences, including both plastid and
  5.   mitochondrion.
  7. - `no`: exclude organelle sequences from the query.
  9. - `cp` or `chloroplast` or `pl` or `plastid`: only query plastid sequences
  11. - `mt` or `mitochondrion`: only query mitochondrion sequences.

-refseq [both|yes|no]: query in RefSeq database or not. The default value is
both.

  1. - `both`: query all sequences in or not in RefSeq database, same as NCBI
  2.   website's default setting.
  4. - `yes`: only query sequences in RefSeq database.
  6. - `no`: exclude sequences in RefSeq database.

RefSeq is considered to have
higher sequence and annotation quality than GenBank. This option could be used
for getting nuclear/organelle genomes from NCBI. In this situation (-refseq yes), the length limit will be removed automatically.

-count [number]: Restrict numbers of sequences to be downloaded. The default
value 0 means no restriction.

-min_len [length]: The minimum length of the records downloaded from
GenBank. The default value is 100 (bp). The number must be an integer.

-max_len [length]: The maximum length of the records downloaded from
GenBank. The default value is 10000 (bp). The number must be an integer.

-date_start [yyyy/mm/dd]: The beginning of the release data range of the
sequences, the format is yyyy/mm/dd.

-date_end [yyyy/mm/dd]: The end of the release data range of the sequences,
the format is yyyy/mm/dd.

-molecular [all|DNA|RNA]: The molecular type,
which could be DNA or RNA. The
default is all—no restriction.

-email [email address]: NCBI GenBank database requires users to provide
an email address in case of abnormal situations that NCBI need to contact
the user. For convenience, OGU will use
“guest@example.com” if the user does not provide an email address. However,
it is better to provide a real email address for potential contact.

-query [expression]: The query string provided by the user. It behaves in
the same manner as the query the user typed into the Search Box in NCBI
GenBank’s webpage.

Make sure to follow NCBI’s grammar for queries. It can contain several words.
Remember to add quotation marks if an item contains more than one words, for
instance, "Homo sapiens"[organism], or use underscore to replace space,
Homo_sapiens[organism].

-exclude [expression]: Use this option to use negative option. For instance,
“-exclude Zea [organism]” (do not include quotation marks) will add “ NOT
(Zea[organism])” to the query.

This option can be useful for excluding a specific taxon.

  1. -taxon Zea -exclude "Zea mays"[organism]

This will query all records in genus Zea while records of Zea mays will be
excluded.

For much more complex exclude options, please consider to use “Advance search”
in GenBank website.

-group [all|animals|plants|fungi|protists|bacteria|archaea|viruses]: To
restrict the query in given group. The default value is all—no
restriction.

It is reported that the “group” filter may return abnormal records, for
instance, return plants’ records when the group is “animal” and the
“organelle” is “chloroplast”. Furthermore, it may match a great number of
records in GenBank. Hence, we strongly recommend using “-taxon” instead.

Divide

Options used for converting GenBank files to fasta files.

-out_debug: If you are going to use visualize pipeline to draw detailed circle
figure, use this option to generate extended version genbank file.

-no_divide: If set, it will analyse the whole sequence instead of the
divided fragments. By default, OGU divides one GenBank record into
several fragments according to its annotation.

-rename: If set, the program will try to rename genes. For instance, “rbcl”
will be renamed to “rbcL”, and “tRNA UAC” will be renamed to “trnVuac”, which
consists of “trn”, the amino acid’s letter and transcribed codon. This may be
helpful if the annotation has nonstandard uppercase/lowercase or naming format
that it can merge the same sequences to one file for the same locus having
variant names.

If using Windows operating system, consider using this option to avoid
contradictory filenames.

-unique [longest|first|no]: The method used to remove redundant sequences.
OGU will remove redundant sequences to ensure only one sequence per
species by default. A user can change its behaviour by setting different
methods.

  1. - `first`: According to the records' order in the original GenBank file,
  2.   only the first sequence of the same species' same locus will be kept.
  3.   Others will be ignored directly. This is the default option due to
  4.   performance considerations.
  6. - `longest`: Keep the longest sequence for one species. The program will
  7.   compare the sequence's length from the same species' same locus.
  9. - `no`: Skip this step. All sequences will be kept.

-allow_mosaic_spacer: If one gene nested with another gene, normally they
do not have spacers. The default value is False.

However, some users want the fragments between two gene’s beginnings and ends.
This option is for this specific purpose (e.g., matK-trnK_UUU). For normal
usage, do not recommend.

-expand [number]: The expansion length in upstream/downstream. If set,
OGU will expand the sequence to its upstream/downstream after the
dividing step to find primer candidates. The default value is 0.

Note that this option is different with “-max_len”. This option limits the
length of one annotation’s sequence. The “-max_len” limits the whole
sequence’s length of one GenBank record.

-allow_repeat: If genes repeated in downstream, this option will allow the
repeat region to be extracted, otherwise any repeated region will be omitted.
The default value is False.

-allow_invert_repeat: If two genes invert-repeated in downstream, this
option allow the second spacer’s name to be different with the first one. Combine
with -allow_repeat, two spacers will be kept. If only one is needed, just set
-allow_invert_repeat and do not set -allow_repeat omitted.
The default value is False.

For instance, geneA-geneB located in one invert-repeat region (IR) of
chloroplast genome. In another IR region, there are geneB-geneA. This option
will extract sequences of two different direction as two unique spacers.

-max_name_len [number]: The maximum length of a feature name. Some
annotation’s feature name in GenBank file is too long, and usually, they are
not the target sequence the user wants. By setting this option, OGU
will truncate the annotation’s feature name if it is too long. By default, the
value is 50.

-max_gene_len [value]: The maximum length of a sequence for one annotation.
Some annotations’ sequences are too long (for instance, one gene has two
exons, and its intron is longer than 10 Kb). This option will skip those long
sequences. By default, the value is 20000 (bp).

Evaluate

-ig or -ignore_gap: ignore gaps in the alignment. Missing data is typically represented as the
letter “N”. Our software retains “N” in its original form for records containing it. During sequence
variance analysis, “N” is treated as an equal mixture of the four nucleotides (“ATCG”) when
calculating Pi, observed resolution, and the Shannon Index. Indels are represented as hyphens (“-”)
in the alignment. For sequence variance analysis, hyphens are treated as a virtual fifth base when
calculating Pi, observed resolution, and the Shannon Index, receiving the same treatment as the four
DNA bases.

-iab or -ignore_ambigous: ignore ambiguous bases in the alignment. Ambiguous bases are treated
as equal mixtures of the possible bases. For example, the letter “Y”, which represents either C or
T, is treated as the sum of one-half “C” and one-half “T.”

-quick: skip sliding-window scan.

-size [number]: the window size of the sliding window scan. The default
value is 500.

-step [number]: the step size of the sliding window scan. The default value
is 50.

-skip_primer: skip primer designing. The default value is False.

Primer design

-coverage [value]: The minimum coverage of the base and primer. The default
value is 0.5 (50%). It is used to remove primer candidates if its coverage
among all sequences is smaller than the threshold. The coverage of primers is
calculated by BLAST.

-res [value]: The minimum observed resolution of the fragments or primer
pairs. The default value is 0.3 (30%). The value should be in 0.0 to 1.0.

OGU uses the observed resolution instead of others because of the
speed. Also, it is considered to be the lower bound of the real resolution
that a fragment with a low observed resolution may not have a satisfactory
tree resolution/phylogenetic diversity, either.

-pmin [length]: The minimal length of the primer. The default value is 20.

-pmax [length]: The maximal length of the primer. The default value is 25.

-topn [number]: How many pairs of primers is kept for each input alignment.
The default value is 1, i.e., only keep the best primer pair according to
its score. To keep more pairs, set “-t” to more than 1.

-amin [length]: The minimum amplified length (include primer). The default
value is 300 (bp). Note this limits the PCR product’s length instead of the
sub-alignment’s length.

-amax [length]: The maximum amplified length (include primer). The default
value is 800 (bp).

The “-amin” and “-amax” are used to screen primer candidates. It uses BLAST
results to set the location of primers on each template sequence and
calculates the average lengths of the products. Because of the variance of
species, the same locus may have different lengths in different species, plus
with the stretching of the alignment that gaps were added during the aligning,
please consider adding some margins for these two options.

For instance, if a user wants the amplified length to be smaller than 800 and
greater than 500, s/he could consider setting “-amin” to 550 and “-amax” to

750.

-ambiguous [number]: The maximum number of ambiguous bases allowed in one
primer. The default value is 4.

-mismatch [number]: The maximum number of mismatched bases in a primer. This
option is used to remove primer candidates if the BLAST results show that
there is too much mismatch. The default value is 4.

Performance

For a taxon that is not very large and includes few fragments, OGU
can finish the task in minutes. For a large taxon (such as the Asteraceae
family or the whole class of the Poales) containing multiple fragments (such
as the chloroplast genomes), the time to complete may be one hour or more on a
PC or laptop.

OGU requires few memories (usually less than 0.5 GB, although, for a
large taxon BLAST may require more) and few CPUs (one core is enough). It can
run very well on a normal PC. Multiple CPU cores may be helpful for the
alignment and tree construction steps.

For Windows users, MAFFT may be very slow due to antivirus
software.
Please consider
following this instruction to
install
Ubuntu on Windows to obtain better results.

Citation

  1. @article{https://doi.org/10.1111/1755-0998.14044,
  2. author = {Wu, Ping and Xue, Ningning and Yang, Jie and Zhang, Qiang and Sun, Yuzhe and Zhang, Wen},
  3. title = {OGU: A Toolbox for Better Utilising Organelle Genomic Data},
  4. journal = {Molecular Ecology Resources},
  5. volume = {25},
  6. number = {3},
  7. pages = {e14044},
  8. keywords = {intergenic spacers, organelle genome, plastid evolution, polymorphism evaluation},
  9. doi = {https://doi.org/10.1111/1755-0998.14044},
  10. url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.14044},
  11. eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.14044},
  12. note = {e14044 MER-24-0083.R3},
  13. abstract = {ABSTRACT Organelle genomes serve as crucial datasets for investigating the genetics and evolution of plants and animals, genome diversity, and species identification. To enhance the collection, analysis, and visualisation of such data, we have developed a novel open-source software tool named Organelle Genome Utilities (OGU). The software encompasses three modules designed to streamline the handling of organelle genome data. The data collection module is dedicated to retrieving, validating and organising sequence information. The evaluation module assesses sequence variance using a range of methods, including novel metrics termed stem and terminal phylogenetic diversity. The primer module designs universal primers for downstream applications. Finally, a visualisation pipeline has been developed to present comprehensive insights into organelle genomes across different lineages rather than focusing solely on individual species. The performance, compatibility and stability of OGU have been rigorously evaluated through benchmarking with four datasets, including one million mixed GenBank records, plastid genomic data from the Lamiaceae family, mitochondrial data from rodents, and 308 plastid genomes sourced from various angiosperm families. Based on software capabilities, we identified 30 plastid intergenic spacers. These spacers exhibit a moderate evolutionary rate and offer practical utility comparable to coding regions, highlighting the potential applications of intergenic spacers in organelle genomes. We anticipate that OGU will substantially enhance the efficient utilisation of organelle genomic data and broaden the prospects for related research endeavours.},
  14. year = {2025}
  15. }

License

The software itself is licensed under
AGPL-3.0 (not include
third-party
software).

Q&A

Please submit your questions in the
Issue page :smiley:

  • Q: The first-time run is slow.

    A: OGU will automatically install third-party software (MAFFT/BLAST/IQTREE)
    from AWS at first-time running. Microsoft Windows users, especially in some
    regions may have slow connection. Please be patient, or you can manually
    install them. See Initialization.

  • Q: During the installation process, I am prompted that some Python packages
    cannot be installed.

    A: It is recommended that you try using a virtual environment to isolate OGU
    from the operating system.

    1. # create a virtual environment named "myvenv"
    2. python3 -m venv myvenv
    3. # activate the environment
    4. # linux and macos
    5. source myvenv/bin/activate
    6. # windows
    7. myvenv/Scripts/activate.ps1
    8. # install
    9. pip3 install OGU