Easy-to-use pipeline for the comprehensive metabolic annotation of microbial genomes.

• Features
• Citation
• Requirements
• Installation
• Usage
• FAQs
• License

MicrobeAnnotator uses an iterative approach to annotate microbial genomes (Bacteria, Archaea and Virus) starting from proteins predicted using your favorite ORF prediction tool, e.g. Prodigal. The iterative approach is composed of three or five main steps (depending on the flavor of MicrobeAnnotator you run).

• Search of initial protein dataset using KOFamscan, extraction of unannotated proteins.
• Search of proteins extracted in the previous step using UniProt Swissprot database. Extract unannotated proteins and repeat search using RefSeq and Trembl (if running the full version).
• In the end, the program will check those entries that had an annotation match but no KO number and will look for other database identifiers (E.C. and InterPro) in the annotation metadata and will try to link those to KO numbers to refine the annotations.
• Summarize the metabolic potential using KEGG modules by extracting KO numbers associated with each match in the databases used. The summary output is a matrix with module completion and two plots showing module completeness per genome (see below).

MicrobeAnnotator: a user-friendly, comprehensive functional annotation pipeline for microbial genomes https://doi.org/10.1186/s12859-020-03940-5

• Programs:
  • Aspera Connect Either (or if you prefer all):
  • Blast >= 2.11.0
  • Diamond >= 2.0.9
  • Sword >= 1.0.4
• Python >=3.6,<3.8
• Python Modules:
  • attrs ==20.3.0
  • biopython ==1.78
  • matplotlib ==3.4.1
  • pandas ==1.2.4
  • psutil ==5.8.0
  • pywget ==3.2
  • requests ==2.25.1
  • seaborn ==0.11.1
• Pip
  • hmmer ==0.1.0

It appears we need a bunch of pre-requisites to run MicrobeAnnotator! The easiest way to install MicrobeAnnotator is using Conda. If you don't have Conda, you can install it as follows:

1. Download Anaconda from https://www.anaconda.com/products/individual.
2. Run bash Anaconda-latest-Linux-x86_64.sh and follow the installation instructions.
3. Once installed you can run conda -V. You should get the version of conda that you installed.

Now, let's add the conda channels required to install the pre-requisites:

conda config --add channels conda-forge
conda config --add channels bioconda
conda config --add channels cruizperez

Then, create an environment for MicrobeAnnotator:

conda create -n microbeannotator python=3.7 pip microbeannotator=2.0.5

Activate it:

conda activate microbeannotator

And then install the last dependecy using pip

pip install hmmer==0.1.0

Both main scripts (microbeannotator and microbeannotator_db_builder) should be in your path ready for use! This should take care of most of the requirements except for Aspera Connect which are a little more involved. Let's install those.

If you prefer to use pip you will need to install the dependencies manually and make sure they are available in PATH. Once you have installed the pre-requisites to run MicrobeAnnotator, you can install MicrobeAnnotator using pip:

pip install microbeannotator==2.0.4

Both main scripts (microbeannotator and microbeannotator_db_builder) should be in your path ready for use!

While not required, Aspera Connect can help speed your data download process. If you don't install this, MicrobeAnnotator will use wget, but if you want to install it, here are the instructions.

To install aspera in a Linux system follow (example with version 3.9.8.176272):

wget https://download.asperasoft.com/download/sw/connect/3.9.8/ibm-aspera-connect-3.9.8.176272-linux-g2.12-64.tar.gz
tar xvfz ibm-aspera-connect-3.9.8.176272-linux-g2.12-64.tar.gz
bash ibm-aspera-connect-3.9.8.176272-linux-g2.12-64.sh

(Usually it will be installed in your home under your user in "/home/[user]/.aspera/connect/bin", it will show you where)

Take note of this installation folder and, for your peace of mind lets add this folder to your PATH:

Add the following line to your ~/.bashrc file: export PATH="$PATH:/home/[user]/.aspera/connect/bin"
Note: Replace [user] by your username

Now reload the ~/.bashrc file with: source ~/.bashrc

Now you have installed Aspera Connect!

If you cannot install Aspera Connect for some reason, MicrobeAnnotator will use another method to download your data, no worries!

First things first. There are two "flavors" of MicrobeAnnotator depending on your storage and computational capabilities (and your time); the regular and light mode. The difference between the two is that the regular implementation will make use of four different databases to annotate your proteins, i.e., the Kofam database and the Swissprot, Trembl and RefSeq databases. On the other hand, the light mode will only use Kofam and Swissprot, which in most cases will annotate the majority of your proteins and will take way less time and space.
You can decide of course which version to run at any time, but for the time being let's have an example with the normal mode (to run the light version just add the --light flag when calling the script).

The first step is to download and format the databases we want to use for MicrobeAnnotator. For this we will execute the microbeannotator_db_builder script within the MicrobeAnnotator folder. You can add see all options and inputs for the script with microbeannotator_db_builder -h.
Run the script as:
microbeannotator_db_builder -d MicrobeAnnotator_DB -m [blast,diamond,sword] -t [# threads]
The options we gave the script were:
-d MicrobeAnnotator_DB that is the folder in which all databases will be downloaded and stored.
-m [blast,diamond,sword] the search method you intend to use. For instance, if you choose "blast" the program will format you database to be searched using NCBI blastp. If you noticed we already installed all three options so you can chooce either (note that sword has some specific processor requirements and might not work in older computers, but you can test it before choosing).
-t [# threads] will be the number of processors used to format the databases.
--step [#] will tell MicrobeAnnotator which step to start from (for example if your run failed for some reason). The steps available are: 1. Download data, 2. Parse annotation data, 3. Building SQLite DB, 4. Create interconversion tables, and 5. Build protein DBs.
--no_aspera will tell MicrobeAnnotator that you don't have Aspera Connect installed so it will use another download method.
--keep will tell MicrobeAnnotator to not remove intermediary files (not recommended because it's not necessary and it will take more of your disk space.

If you run the full version (no --light flag), you will need at least approximately ~230Gb of space. This can increase depending on the search method used (blast and diamond require the raw fasta databases to be further formated). If everything went right, you should find inside the MicrobeAnnotator_DB folder, a directory protein_db and two files, microbeannotator.db and conversion.db that contain all information required by the program to search your proteins against these databases. If anything went wrong, or if you changed your mind about your searching method after it finished, you can restart the process at any step with the --step flag, the help will tell you which are the steps.

This is the main part of MicrobeAnnotator. Here you will search the proteins you provide against the databases we created before and you will receive an annotation table per protein file along with the individual results per database used (in case you want to further explore those), a matrix summarizing the KEGG module completeness and two plots showing said module completeness for an easy, visual comparison between genomes (or files).
This implies that you can pass multiple protein fasta files to MicrobeAnnotator and they will all be processed in a single script! Awesome right?
For this annotation step we will use the microbeAnnotator script (see microbeAnnotator -h for all options).
Run the script as:
microbeannotator -i [fasta_1.fa fasta_2.fa] -d [microbeannotator_db_dir] -o [output folder] -m [blast,diamond,sword] -p [# processes] -t [# threads]
The options we gave the script were:
-i [fasta_1.fa fasta_2.fa], the input fasta files (separated by spaces). If you have many, you can pass them as -i $(ls *.fasta).
-l [file], a file containing the files to process. If you have many files it is easier compared to -i
-d [microbeannotator_db_dir], the folder where you created the databases in the previous step.
-o [output folder], the folder to store all results generated by MicrobeAnnotator.
-m [blast,diamond,sword] the search method you intend to use.
-p [# processes], refers to the number of protein files to be processed simultaneously, e.g -p 3 will process three protein files at the same time.
-t [# threads], refers to the number of processors to use per protein file. For example -t 5 will use five processors per each protein file.
Therefore, of you use -p 2 -t 5, you will need 10 cores (five for each protein file).
--refine, will tell MicrobeAnnotator to completement the initial annotations using E.C. numbers or InterPro ids and convert them to KO numbers.
--continue_run, we know that MicrobeAnnotator can fail. If you have many files it can be really frustrating having to start over (we have been there :)). That is why we added an option to resume the process from the last completed point. This is the flag that does it.

At the end you should see your output folder with:
annotation_results/ folder: Where you will find the annotation tables per file and the list of KO numbers present in the file.
kofamscan_results/ folder: Raw and filtered KofamScan search results.
[database]_results/ folder: Raw and filtered search results for each database used (depends of the flavor used).
A metabolic_summary_barplot file: Summarizes the KEGG module completeness per genome (protein file).
A metabolic_summary_heatmap file: Summarizes the KEGG module completeness of all genomes in an-easier-to-understand heatmap (compared to a raw table, that is).
A metabolic_summary_module_completeness.tab file: Matrix describing the completness of all KEGG modules per genome (protein file).

This should be the end result of MicrobeAnnotator, where you can easily compare the metabolic potential of your microbial genomes and if you like, have more information of the annotation of each protein in each file. Happy annotation!

• How long will MicrobeAnnotator take to build the databases?
  Well, it depends on the version of database you are building. For instance, the full version needs to download the Swissprot, Trembl, and RefSeq databases (and their associated metadata) and parse them into the SQLite database MicrobeAnnotator uses. Depending on your internet connection speed this process can take ~24h (using a single thread), including downloading and processing the data. Fortunately, you just need to do this once! Now, if you are using sword as your search method, you are done, but if you are using Blast or Diamond, there is additional time required for the protein fasta files to be converted into the appropriate formats. This formatting process will take approximately ~5 hours for Blast and ~2.5 hours for Diamond. If you are running the "light" version of the program with sword as your search method, this entire process will only take ~5 minutes (depending of your internet connection speed).

• How much space will the database take in my disk?
  This once again depends on the verison of the program you indend to run and the search method you want to use. For reference the space required by the full database will approximatelly be:

  • Protein fasta files: ~144Gb
  • Blast-formatted files: ~177Gb
  • Diamond-formatted files: ~148Gb
  • SQLite3 database: ~93Gb
    In summary, if you build the full database you need at least ~237Gb of free space in your disk. The light version only will take ~0.65Gb (with sword as search method).

• Can I use the light (--light) version of the program to annotate if I built the database using the full version?
  Of course! You can search and annotate your genomes (protein files) using only kofamscan and the swissprot database, i.e. the light version even if you have the full database available. However, the contrary cannot be done. If you try to perform the full search with the light version of the database, MicrobeAnnotator will not be able to find the required databases and will exit with an error. If you have anough time (and space) you can build the full database and then use the version that better suits your needs at each particular time.

• I have identifiers from other annotation tools, can I use them in MicrobeAnnotator to summarize the metabolic potential?
  Of course! We realize we are not the only annotation tool out there, so we have an additional script identifier_conversion.py, that will allow you to convert ids between databases, either to summarize within microbeannotator or to compare several annotation methods. With this script you can convert between: E.C. <-> KO, InterPro <-> KO, and InterPro <-> E.C. If you have a list of KO identifiers in the end, you can summarize them using another script ko_mapper.py.

• I have a list of KOs obtained somewhere. Can I summarize them and skip all the annotation steps? Sure! We have made an additional script that does exactly this. You will find it as ko_mapper.py.

• I don't see my question here. Where can I ask a question? You can submit a ticket in the issues tab and I will do my best to solve it and answer your question. You can also ask for features you would like to see in the future!

See LICENSE