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Quality control of assembled genomes

Project description

Introduction

With the increasing rate of the production of genomics data, particularly metagenomic data, there is need for more and faster quality control of resulting assembled genomes. An increasing usage of metagenome assembled genomes (MAGs) means often working with incomplete genomes, which can be acceptable providing the researcher is cognizant of this. Therefore there is a clear use for software able to rapidly provide the stats that describe the quality and completeness of the genomes or genomic bins of interest.

miComplete allows a user to provide a list of genomes or genomic bins to retrieve some basic statistics regarding the given genomes (size, GC-content, N- and L50, N- and L90). Further a set of marker genes in HMM format can be provided to also retrieve completeness and redundance of those markers in each genome. Additionally, a set of weights for the marker genes can be provided to also retrieve the weighted versions of completeness and redundance which can inform the user a bit more of the actual state completeness (see description). Alternatively, the user can calculate new weights for any given set of marker genes provided.

miComplete is still in a relatively early state of development, there are a few missing features and bugs are very much expected. Feedback, bug reports, and feature requests are welcome through Bitbucket’s issue system).

Description

miComplete is a compact software aimed at the rapid determining of the quality of assembled genomes, often metagenome assembled. miComplete also aims at providing a more reliable completeness and redundance metric via a system of weighting the impact of different marker genes presence or absence differently.

Completeness

In miComplete completeness is calculated based on the presence/absence a set of marker genes provided as a set of HMMs. The presence or absence of the marker genes is determined by HMMER3 (see dependencies) if the hit reported is below the cutoff e-value provided. Duplicated marker genes are also gathered, and found duplications are reported as redundance, but only if the e-value of the reported duplicated hit is at least equal to or less than the square root of the the best hit (though this can easily be altered as desired by the user).

Weights

Not all marker genes are equal in determining the completeness of a genome. Some genes associate closely together within a genome (not in the least genes in operons), and thus viewing two genes that typically associate together within an operon as providing the same completeness information as two unrelated genes would be misleading. miComplete is able to calculate a weighted version of completeness and redundance that attempts to factor in how closely the provided marker genes typically associate with other provided marker genes.

Linkage

Weights can be calculated for any given set of marker genes in miComplete. This is can be done by a user by providing a set of reference genomes (note that these need to be single contig chromosomes). The reference genomes can be any set that the user wishes, but as general rule the larger and more diverse number of genomes the better weights. At the end of the run a boxplot of the distribution of weights for all markers is produced.

Dependencies

Python (>=2.7 / >=3.4)

External software

Executables should be available in the user’s $PATH.

HMMER3

HMMER: biosequence analysis using profile hidden Markov models, by Sean Eddy and coworkers. Tested with v. 3.1b2. Available from <http://hmmer.org/>.

prodigal

A gene prediction software by Doug Hyatt. Tested with v. 2.6.3. Download at: <https://github.com/hyattpd/Prodigal>

Python libraries

If built from the package these will be installed automatically, otherwise can easily be installed using pip (Install pip).

Required

  • Biopython (>= 1.70) ($ pip install biopython)
  • Numpy (>= 1.13.1) ($ pip install numpy)
  • Matplotlib (>= 2.0.2) ($ pip install matplotlib)
  • Termcolor (>= 1.1.0) ($ pip install termcolor)

Installation

Python package

miComplete can easily be installed along with all python dependencies:

$ pip install micomplete

Assuming that the python bin is in your $PATH, can then be run as:

$ miComplete

Git

  1. Choose an appropriate location, e.g. your home:

    $ cd $HOME
    
  2. Clone the latest version of the repository:

    $ git clone http://bitbucket.org/evolegiolab/micomplete.git
    
  3. Create symlink to some directory in your $PATH (in this example $HOME/bin):

    $ cd micomplete
    $ ls micomplete
    $ ln -s $(realpath micomplete/micomplete.py $HOME/bin/miComplete)
    
  1. Optionally, add the folder micomplete in your PATH. The scripts should be kept at their original location.

Usage

Positional arguments

A file of sequence(s) along with type (fna, faa, gbk) provided in a tabular format

The file has to contain per line both a path (relative or absolute) to a genomic file as well as the type (separated by a tab):

/seq/genomic_sources/e_coli.fna   fna
/seq/genomic_sources/l_pneumophila.gbk   gbk
(...)

Optional arguments

-h, --help show help message and exit
-c, --completeness
 Do completeness check (also requires a set of HMMs to have been provided)
--hlist Write list of Present, Absent and Duplicated markers for each organism to file
--hmms HMMS Specifies a set of HMMs to be used for completeness check or linkage analysis
--weights WEIGHTS
 Specify a set of weights for the HMMs specified, (optional)
--linkage Specifies that the provided sequences should be used to calculate the weights of the provided HMMs
--evalue EVALUE
 Specify e-value cutoff to be used for completeness check, default=1e-10
--cutoff CUTOFF
 Specify cutoff percentage of markers required to be present in genome for it be included in linkage calculat. Default = 0.9
--threads THREADS
 Specify number of threads to be used in parallel
--log LOG Log name (default=miComplete.log)
-v, --verbose Enable verbose logging
--debug Debug mode

Examples

Sequence tab file, test_set.tab:

test_set_common_fna/klebsiella_pneumoniae.fna   fna
test_set_common_fna/pseudonomonas_aeruginosa.fna        fna
test_set_common_fna/escherichia_coli.fna        fna
test_set_common_fna/salmonella_enterica.fna     fna

Example 1 - Basic stats

This example merely produces basic information about the given sequences:

$ miComplete test_set.tab
Name Length  GC-content      N50     L50     N90     L90
klebsiella_pneumoniae        5682322 57.12   5333942 1       5333942 1
pseudonomonas_aeruginosa     6264404 66.56   6264404 1       6264404 1
escherichia_coli     4641652 50.79   4641652 1       4641652 1
salmonella_enterica  5133713 51.87   4809037 1       4809037 1

miComplete prints result to stdout in tabular format, this can favourably be redirected towards a file with a pipe and examined with spreadsheet reader.

$ miComplete test_set.tab > results.tab

Example 2 - Completeness

This example will produce the same basic statistics, but also completeness and redundance:

$ miComplete test_set.tab -c --hmms share/Bact139.hmm
Name Length  GC-content      Present Markers Completeness    Redundance      N50     L50     N90     L90
escherichia_coli     4641652 50.79   139     1.000   1.000   4641652 1       4641652 1
salmonella_enterica  5133713 51.87   138     0.993   1.000   4809037 1       4809037 1
klebsiella_pneumoniae        5682322 57.12   136     0.978   1.000   5333942 1       5333942 1
pseudonomonas_aeruginosa     6264404 66.56   135     0.971   1.000   6264404   1     6264404 1

That is great, but the run time is starting to increase significantly since we have to translate four genomes to proteomes. We can speed up the process by running all four parallel with --threads:

$ miComplete test_set.tab -c --hmms share/Bact139.hmm --threads 4 > results.tab

Example 3 - Weighted completeness

This example will also produce the weighted completeness:

$ miComplete test_set.tab -c --hmms share/Bact139.hmm --weights share/Bact139.weights --threads 4
Name Length  GC-content      Present Markers Completeness    Redundance      CompletenessW   RedundanceW     N50     L50     N90     L90
escherichia_coli     4641652 50.79   139     1.000   1.000   1.000   1.000   4641652 1       4641652 1
salmonella_enterica  5133713 51.87   138     0.993   1.000   0.991   1.000   4809037 1       4809037 1
klebsiella_pneumoniae        5682322 57.12   136     0.978   1.000   0.982   1.000   5333942 1       5333942 1
pseudonomonas_aeruginosa     6264404 66.56   135     0.971   1.000   0.965   1.000   6264404 1       6264404 1

Example 4 - Creating weights

Finally we will create our own set of weights given a set of marker genes for which we do not already have weights:

$ miComplete test_set.tab -c --hmms share/Bact109.hmm --linkage --threads 4 > Bact109.weights

Also produces a box plot of the distribution of weights for each marker gene.

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