## metaerg.py, version 2.5.9
Metaerg.py annotates genomes or sets of mags/bins from microbial ecosystems (bacteria, archaea, viruses). Input data
consists of nucleotide fasta files, one per genome or mag, each with one or more contigs. Output files with annotations
are in common formats such as .gff, .gbk, .fasta and .html with predicted genes, their functions and taxonomic
classifications.
Metaerg 2.5 can annotate a group of related genomes and apply a suite of comparative genomics analyses. To do this, put
contig fasta nucleotide files, one for each genome in a folder and run metaerg with the option "--mode comparative_genomics".
Annotations become much stronger, richer and easier to interpret when you follow this approach.
This analysis proceeds as follows:
* Homologous proteins and RNA genes are clustered using [mmseqs](https://github.com/soedinglab/MMseqs2) version 15-6f452 with --min-seq-id 0.5.
* Orthologues and paralogues are called based on the distance to the center-protein.
* Each cluster of homologous proteins is aligned with [famsa](https://github.com/refresh-bio/FAMSA) version 2.2.2.
* Median codon usage bias for each cluster of homologous proteins is calculated as a measure of expected expression level.
* Median omega = ka/ks is calculated for each cluster to estimate whether the gene is under purifying or diversifying selection.
* Based on the co-location of orthologous genes in different genomes, gene clusters are predicted.
* These results are written in various formats (see below) and visualized for each gene in the interactive gene table of each genome.
* A table with all the properties of each cluster of homologous genes is written (including representation for each genome).
By building its blast database off gtdbtk and transferring functional annotations from the NCBI, metaerg.py
uses a controlled vocabulary for taxonomy and a relatively clean vocabulary for functions. This makes annotations much
more concise than the original version of metaerg and many other annotation tools. In addition, metaerg uses NCBI's
conserved domain database and RPSBlast to assign genes to subsystems for effective data exploration. Subsystems are a
work in progress, and can be expanded and customized as needed.
The Metaerg 2.5 pipeline ...
* predicts CRISPR regions using [CRISPRDetect](https://github.com/davidchyou/CRISPRDetect_2.4), version 2.4.
* predicts tRNAs using [Aragorn](https://www.ansikte.se/ARAGORN/Downloads/), version 1.2.41.
* predicts RNA genes and other non-coding features using [Infernal](http://eddylab.org/infernal/) - cmscan and RFAM, version 1.1.4.
* predicts retrotransposons with [LTR Harvest](http://genometools.org/tools/gt_ltrharvest.html) - LTRHarvest, genometools version 1.6.2.
* predicts tandem repeats with [Tandem Repeats Finder](https://tandem.bu.edu/trf/trf.html), version 4.0.9.
* predicts other repeat regions with [Repeatscout](http://bix.ucsd.edu/repeatscout/), version 1.0.5, and [Repeatmasker](http://www.repeatmasker.org/RepeatMasker/), version 4.1.5.
* predicts coding genes with [Prodigal](https://github.com/hyattpd/Prodigal), version 2.6.3.
* annotates taxonomy and functions of RNA and protein genes using [Diamond](https://github.com/bbuchfink/diamond), version 2.0.15, [NCBI blastn](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/), version 2.14.0 and a database of >50,000 prokaryotes, based on [gtdb](https://gtdb.ecogenomic.org/) version 214, 11,569 viral and 139 eukaryotic genomes.
* annotates gene functions using [RPSBlast](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/), version 2.14.0 and NCBI's Conserved Domain Database (CDD).
* annotates genes involved in production of secondary met abolites using [Antismash](https://dl.secondarymetabolites.org/releases), version 7.0.
* annotates membrane and translocated proteins using [PureseqTM](https://github.com/PureseqTM/pureseqTM_package) and [deepsig](https://github.com/BolognaBiocomp/deepsig).
* assigns genes to a [built-in set of 1,001 functions](https://github.com/kinestetika/MetaErg/blob/master/src/metaerg/run_and_read/data/functional_gene_data) using [HMMER](http://hmmer.org), version 3.3.2 and commmunity contributed HMM profiles (see below).
* estimates doubling times of a genome's host based on [codon usage bias](https://www.pnas.org/doi/epdf/10.1073/pnas.2016810118)
* presents annotations in [datatables/jQuery](https://www.datatables.net/)-based intuititve, searchable, colorful HTML that can be explored in a web browser and copy/pasted into excel.
* saves annotations as a fasta-amino-acid file, a genbank file, and as a sqlite database for effective exploration, statistics and visualization with python or R.
* saves an overview of all annotated genomes' properties and functions as an excel file.
* enables the user to add custom HMMs and expand the set of functional genes as needed.
When using metaerg, please cite [Xiaoli Dong and Marc Strous (2019) Frontiers in Genetics](https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full)
## Important changes in version 2.5
* Comparative genomics mode was added. This depends on MMSeqs and Famsa. These programs are automatically installed by metaerg.
## Important changes in version 2.4
* Minced, TMHMM and SignalP are no longer used as helper programs.
* CRISPRDetect, padloc, PureseqTM and deepsig are used instead. Installation is straightforward. Either use the --install_deps option, run the commands in "installation.py" for a manual install or use Docker.
* These changes reduce overall runtime.
* Various small bugs and inconveniences were fixed .
* If you have data previously annotated with metaerg, use --update_annotations to update.
## Usage:
```
>metaerg --contig_file contig-file.fna --database_dir /path/to/metaerg-databases/
```
To annotate a set of genomes in a given dir (each file should contain the contigs of a single genome):
```
>metaerg --contig_file dir-with-contig-files --database_dir /path/to/metaerg-databases/ \
--file_extension .fa
```
Metaerg needs ~40 min to annotate a 4 Mb genome on a desktop computer. The comparative genomics analysis requires about
20 min extra (in total) for a ls set of ~20 genomes.
You can use the following arguments when running metaerg:
```text
--contig_file A nucleotide fasta files with contigs to be annotated OR a dir containing
nucleotide fasta files.
--output_dir The path where the results will be saved. Default: the dir that contains the
contig file(s)
--database_dir The path to the metaerg database.
--file_extension If a dir was provided to --contig_file, the file extension of the
nucleotide fasta files. Default: .fna
--rename_contigs Assemblers may create very long names for contigs, which is suboptimal for
presentation of results. This argument will make metaerg rename the
contigs more concisely.
--rename_genomes Binners may create very long names for bins/MAGs, which is suboptimal for
presentation of results. This argument will make metaerg rename the
bins/MAGs more concisely. When using this option, contigs will also be
renamed.
--delimiter Metaerg will create "locus tags" (unique IDs) for genes with according to
the following scheme: "[MAG file name].[CONTIG name].[Gene number]".
By default, '.' is used as the delimiter separating the three parts of the
ID, as shown in the example. If you want to use a different character to
separate the parts, use this argument. If your custom character is
detected in filenames or contig names, and you are not renaming contigs
or MAGs, metaerg will terminate with an error message.
--prefix When renaming genomes, by default genomes will be named "gXXXX", where 'g'
is the prefix and XXXX is a number. If you would like a different prefix,
use this argument.
--min_contig_length Only annotate contigs that are longer than the specified length.
Default: 0.
--cpus Number of threads used for annotation. Default: threads available
on the system / 2.
--mode Use "--mode contig" to annotate contigs individually instead of assuming they
are part of a genome, MAG or bin. When using this option, metaerg will not
run repeatscout and will run prodigal in metagenome mode. Use the option
--translation_table below to override using metagenome mode.
Use "--mode comparative_genomics" to annotate a clade of related genomes/MAGs.
Use "--mode genome" to annotate genomes/MAGs, one per file.
Default: "genome".
--force Overwrite previous results. By default, results of previous steps will be
kept. You need to specify which steps will be forced (see --skip for a list
of steps Use --force all to overwrite all previous results.
--update_annotations Do not rerun any helper programs (keep previous results) but redo all the
data processing. Use this option for example after you updated metaerg.
--skip Use this argument to skip one or more annotation steps. Use the following
names for the steps, with names to be skipped separated by a comma (,):
antismash Annotate genes involved in production of secondary
metabolites and other aspects of the "interactome".
aragorn Call transfer RNAs.
cdd Annotate gene functions according to NCBI's conserved
domain database.
cmscan Call RNA genes (such as rRNA genes).
deepsig Detect protein signal peptides for translocation across
the membrane.
diamond_and_blastn Annotate gene functions and classify genes
taxonomically based on homology to genes of other
organisms.
hmm Annotate gene functions according to metaergs built-in
scheme.
ltr_harvest Call retrotransposons.
crispr_detect Call CRSIPR repeats.
padloc Annotate genes involved in cellular defense
prodigal Call open reading frames (genes encoding proteins). If
you skip this step, no proteins will be annotated.
pureseqtm Annotate transmembrane helixes (membrane proteins and
anchors).
repeat_masker Call any repeat sequences.
trf Call tandem repeats.
--translation_table Translation table(s) to be used when calling open reading frames with
prodigal. Default: 11,25. If the mean ORF length is less than 200 amino-
acids, metaerg will rerun prodigal with the next translation table in the
list.
--download_database Use this argument to download and install the prebuilt metaerg database.
CAUTION: The metaerg databases are big, requiring approximately 165 Gb of
disk space.
--create_database Use this argument to build the metaerg database from scratch. The metaerg
database consists of several components. By default, this argument will
build all. If you wish to build specific database components, use one or
more of the following letters:
P - build prokaryotes
V - build viruses
E - build eukaryotes
B - format all (PVE) blast databases
R - build RFAM
C - build CDD
S - build/update community contributed HMM databases
A - build antismash database
D - build padloc database
--checkm_dir If you have previously used checkm or checkm2 to determine the quality of
the MAGs/bins, you can specify the dir with the checkm or checkm2 results
here, and metaerg will integrate the estimated completeness and
contamination into its output.
--gtdbtk_dir Use this argument with --create_database to point metaerg to the gtdbtk
database. It needs this to build its prokaryote blast database.
--install_deps Use this argument to install all helper programs on your system. You need
to follow this argument with an installation dir, where you want to have
the programs installed.
--padloc_database Use optionally with --install_deps if you want this database to be in a
non-default place/filesystem. Afterward, use --create_database D to
actually download and install the padloc database.
--antismash_database Use optionally with --install_deps if you want this database to be in a
non-default place/filesystem. Afterward, use --create_database A to
actually download and install the antismash database.
```
## Using the Docker Image
Metaerg depends on many helper programs and may require some time and troubleshooting to install. To avoid these issues,
use the [docker image](https://hub.docker.com/r/kinestetika/metaerg). Alternatively, use singularity or apptainer to run
the docker image on a HPC, as explained by [jkzorz](https://github.com/jkzorz/Metagenomes_Illumina/blob/main/annotation.md) and [lianchun yi]:
```commandline
>singularity pull docker://kinestetika/metaerg
>singularity build --sandbox /path/where/top/create/metaerg_latest.sif docker://kinestetika/metaerg:latest
>singularity exec --bind /path/to/metaerg_database:/databases --bind /path/to/fasta/files:/data --writable /path/to/the/sandbox/dir
metaerg --database_dir /databases --contig_file /data --file_extension .fna
```
>[!NOTE]
>The --bind instruction binds the dir "/path/to/metaerg_database" (which is on your real filesystem) to the dir "/databases" (which is inside your container). Same for the contig dir.
Or, using apptainer:
```commandline
>apptainer build metaerg.sif docker://kinestetika/metaerg:latest
```
## Installation
To install metaerg, its 19 helper programs (diamond, prodigal, etc.) and databases run the following commands:
```commandline
>python -m virtualenv metaerg-env
>source metaerg-env/bin/activate
>pip install --upgrade metaerg
>metaerg --install_deps /path/to/bin_dir
>source /path/to/bin_dir/profile
>metaerg --download_database --database_dir /path/to/metaerg-databases/
```
If you install metaerg's helper programs this way, before running metaerg you need to run the following, to prepend the helper programs to your path:
```commandline
>source /path/to/bin_dir/profile
```
## Database
The database was created from the following sources:
* [gtdbtk](https://ecogenomics.github.io/GTDBTk/index.html) is used for its taxonomy
* NCBI annotations of >40K representative archael and bacterial genomes present in gtdb are sourced directly from the ncbi ftp server.
* NCBI (refseq) annotations of viral genes are obtained from [viral refseq](https://support.nlm.nih.gov/knowledgebase/article/KA-03474/en-us).
* For Eukaryotes, for each taxon within Amoebozoa, Ancyromonadida, Apusozoa, Breviatea, CRuMs, Cryptophyceae, Discoba, Glaucocystophyceae, Haptista, Hemimastigophora, Malawimonadida, Metamonada, Rhodelphea, Rhodophyta, Sar, Aphelida, Choanoflagellata, Filasterea, Fungi, Ichthyosporea, Rotosphaeridagenomes, one genome is added to the database using [ncbi-datasets](https://www.ncbi.nlm.nih.gov/datasets/).
* [RFAM](https://rfam.xfam.org/) and [CDD](https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) databases are also used.
* Specialized function databases - [Cant-Hyd](https://github.com/dgittins/CANT-HYD-HydrocarbonBiodegradation/blob/main/HMMs/concatenated%20HMMs/CANT-HYD.hmm) and [MetaScan](https://zenodo.org/record/6365663).
Community contributed HMM profiles are sourced from:
* [HydDB](https://services.birc.au.dk/hyddb/) (Hydrogenase Families): Pleas cite [this](https://www.nature.com/articles/srep34212) paper.
* [CANT-HYD](https://github.com/dgittins/CANT-HYD-HydrocarbonBiodegradation) (Genes involved in hydrocarbon degradation): Pleas cite [this](https://www.frontiersin.org/articles/10.3389/fmicb.2021.764058/full) paper.
* [MetaScan](https://github.com/gcremers/metascan) (Various catabolisms): Pleas cite [this](https://www.frontiersin.org/articles/10.3389/fbinf.2022.861505/full?field=&journalName=Frontiers_in_Bioinformatics&id=861505) paper.
* [CRISPR-CAS genes](https://www.nature.com/articles/nature21059): Pleas cite [this](https://www.nature.com/articles/nature21059) paper.
* [Heme Copper Oxidase Families](https://github.com/ranjani-m/HCO): Pleas cite [this](https://www.biorxiv.org/content/10.1101/2021.10.15.464467v2.abstract) preprint.
* [Cytochrome bd oxygen reductases](https://github.com/ranjani-m/cytbd-superfamily): Pleas cite [this](https://www.nature.com/articles/s41396-021-01019-4) paper.
If you for some reason need to build the database yourself (this is usually not needed as the metaerg database can be
downloaded as shown above):
```commandline
>metaerg --create_database --database_dir /path/to/metaerg/db --gtdbtk_dir /path/to/gtdbtkdir
```
## Accessing the .feather and .sqlite files
[Apache Feather format](https://arrow.apache.org/docs/python/feather.html) is a binary file format for tables. Sqlite is a database format. You can for example load these data as a pandas dataframe. In **R**, use the [arrow](https://arrow.apache.org/docs/r/) package.
Each table/database row contains a single gene or feature, defined by the following columns:
```
id the feature's unique identifier
genome the identifier of the genome the feature belongs to
contig the identifier of the contig the feature belongs to
start the start position of the feature (inclusive)
end the start position of the feature (exclusive)
strand the strand (0 or 1 for + or - respectively)
type the type of feature (for example CDS, rRNA, tRNA, ncRNA, retrotransposon)
inference the program used to infer the feature (for example prodigal for CDS)
parent the feature's parent (for example a CRISPR repeat has a repeat_region as a parent)
subsystems the subsystems (functional genes) the feauture is part of
(for example "[ATP synthase|ATP synthase, subunit F0 B]")
descr a succint description of the annotated function
taxon the taxon of the top blast hit
notes any other info (rarely used)
seq the sequence of the feature (AA for CDS, otherwise NT)
antismash the function assigned by antismash, if any
signal_peptide the type of signal peptide found, if any.
tmh the number of transmembrane helixes found
tmh_topology the locations of predicted transmembrane helixes in the protein sequence
blast the top ten blast hits
cdd the top ten cdd hits
hmm the top ten hits to the functional gene hmm database
```
You can for example use python and pandas to inspect annotations:
``` python
from pathlib import Path
import pandas as pd
data_dir = Path('/path/to/my/data')
feather_file = data_dir / 'my-genome.annotations.feather'
contig_file = data_dir / 'my-genome.fna'
contig_dict = load_contigs('my-genome', contig_file, delimiter='xxxx')
feature_data = pd.read_feather(feather_file)
feature_data.set_index('id', inplace=True)
for f in feature_data.itertuples():
for k, v in f._asdict().items():
print(f'{k:20}:{v}')
break # comment out to iterate through all the genes...
```
Using the .sqlite database is even easier:
``` python
from pathlib import Path
from metaerg.datatypes import sqlite
data_dir = Path('/path/to/my/data')
sqlite_file = data_dir / 'my-genome.annotations.sqlite'
db_connection = sqlite.connect_to_db(sqlite_file)
for feature in sqlite.read_all_features(db_connection):
print(feature)
break # comment out to iterate through all the genes...
```
## How Metaerg's functional gene calling works
Metaerg contains a [data file](https://github.com/kinestetika/MetaErg/blob/master/src/metaerg/run_and_read/data/functional_gene_data) that defines metabolic modules, each consisting of multiple genes. These
genes are identified using NCBI's Conserved Domain Database (cdd) and a custom set of Hidden Markov Models contributed
by the community/science literature. Here's what an example module looks like in the data file:
```text
>Respiration, Hydrogenases Ni-Fe
COG3259|TIGR03295|NF033181|COG4042 NiFe hydrogenase catch-all
WP_015407385 NiFe hydrogenase group Ia
WP_012675774 NiFe hydrogenase group Ib
WP_011688559 NiFe hydrogenase group Ic
WP_023457248 NiFe hydrogenase group Id
WP_008030254 NiFe hydrogenase group Ie
WP_015898101 NiFe hydrogenase group If
WP_011822511 NiFe hydrogenase group Ig
WP_014267363 NiFe hydrogenase group Ih
WP_022740096 NiFe hydrogenase group Ii
WP_010878877 NiFe hydrogenase group Ij
WP_012036281 NiFe hydrogenase group Ik
WP_011603008 NiFe hydrogenase group IIa
WP_011714112 NiFe hydrogenase group IIb
WP_011383527.1 NiFe hydrogenase group IIc
WP_010880487 NiFe hydrogenase group IId
WP_012020882.1 NiFe hydrogenase group IIe
WP_022846160 NiFe hydrogenase group IIIa
WP_011719855 NiFe hydrogenase group IIIb
WP_013637988 NiFe hydrogenase group IIIc
WP_011686721 NiFe hydrogenase group IIId
WP_022739022 NiFe hydrogenase group IVa
WP_011388069 NiFe hydrogenase group IVb
WP_026783288 NiFe hydrogenase group IVc
WP_014122750 NiFe hydrogenase group IVd
WP_022739883 NiFe hydrogenase group IVe
WP_018133527 NiFe hydrogenase group IVf
WP_013129492 NiFe hydrogenase group IVg
WP_011018833 NiFe hydrogenase group IVh
WP_004029221 NiFe hydrogenase group IVi
```
A metabolic module starts with a greater-than (>) sign followed by the name of the module. Each line after lists one
gene. The gene is defined by a one or more cdd or custom-hmm identifiers separated by or (|). After the identifiers
follows a space and finally the name of the gene (which can contain spaces). The example shows the Ni-Fe hydrogenase
module. This module contains "catch-all" hydrogenase domains from the cdd database (as the first gene on the first line)
as well as custom hmm's targeting specific subtypes of Ni-Fe hydrogenase. These hmm's were created by the [Greening Lab](https://www.nature.com/articles/srep34212).
For cdd domains, metaerg will identify a protein as the gene when one of the cdd domains is among the top 5 cdd hits.
For custom hmm's, metaerg will identify a protein as the gene if the hmm profile is the top hit and al least 70% of the
profile hmm aligns to the gene. If an hmm profile defines a trusted cutoff score, metaerg will use that score as the
cutoff.
The advantage of using cdd compared to custom hmm databases is that cdd contains profiles for most known genes. So if,
for example, a protein resembles a hydrogenase, such as subunit nuoD of the respiratory complex I, cdd has
no difficulty identifying nuoD as nuoD. However, the custom hmm database does not contain a nuoD profile, so nuoD genes,
present in many bacterial genomes, will be erroneously identified as a NiFe hydrogenases if you only use custom profiles.
This problem cannot be overcome by using a fixed e-value or score cutoff, as each hmm profile produces a unique
range of evalues and scores. In theory, this problem could be overcome by determining a trusted cutoff score for each
profile. However, that is a lot of work and not commonly done when scientists create hmm databases.
That being said, cdd is not good enough by itself, because it misses out on many important microbiome functions and \
genes. Some genes are completely absent, and some are poorly defined. Expert input is needed for correct identification
of these genes. That is why we need custom hmms.
Metaerg's solution to this problem is to combine cdd with custom hmms. In the example, if a gene is not captured by the
catch-all cdd hydrogenase modules, it is likely not a NiFe hydrogenase. If it is, the custom hmm's will show the
subtype of the NiFe hydrogenase.
## How to add your own custom functional gene database and HMMs
1. Locate your metaerg database dir. Inside the metaerg database dir, is a dir "hmm".
2. Within "hmm", create a new dir named "user_config", if it does not already exist.
3. Inside "user_config", you can create one or more files with the extension ".config.txt".
4. These files should be formatted like in the example above.
5. Each module should start with >.
6. Each gene starts with one or more cdd or custom hmm names, followed by a space and the gene name/description.
7. To query the cdd database for relevant cdd names, you use grep with the file "/path/to/metaerg/database/cdd/cddid.tbl."
8. Within "hmm", create a new dir named "user_hmm", if it does not already exist.
9. Add your custom hmm files to this dir.
10. Update the metaerg database using the following command:
```commandline
>metaerg --create_database S --database_dir /path/to/metaerg/db
```
11. Only those hmm profiles listed in your config file will actually be built into to the metaerg database
Raw data
{
"_id": null,
"home_page": "https://github.com/kinestetika/MetaErg",
"name": "metaerg",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "repeat-regions genes functions taxonomy",
"author": "Marc Strous",
"author_email": "mstrous@ucalgary.ca",
"download_url": "https://files.pythonhosted.org/packages/da/c0/96f1cce06193ca6094592133cb7f96e7955424f9843d7484c336e6d01682/metaerg-2.5.9.tar.gz",
"platform": null,
"description": "## metaerg.py, version 2.5.9\n\nMetaerg.py annotates genomes or sets of mags/bins from microbial ecosystems (bacteria, archaea, viruses). Input data \nconsists of nucleotide fasta files, one per genome or mag, each with one or more contigs. Output files with annotations \nare in common formats such as .gff, .gbk, .fasta and .html with predicted genes, their functions and taxonomic \nclassifications.\n\nMetaerg 2.5 can annotate a group of related genomes and apply a suite of comparative genomics analyses. To do this, put\ncontig fasta nucleotide files, one for each genome in a folder and run metaerg with the option \"--mode comparative_genomics\".\nAnnotations become much stronger, richer and easier to interpret when you follow this approach.\nThis analysis proceeds as follows:\n* Homologous proteins and RNA genes are clustered using [mmseqs](https://github.com/soedinglab/MMseqs2) version 15-6f452 with --min-seq-id 0.5.\n* Orthologues and paralogues are called based on the distance to the center-protein.\n* Each cluster of homologous proteins is aligned with [famsa](https://github.com/refresh-bio/FAMSA) version 2.2.2.\n* Median codon usage bias for each cluster of homologous proteins is calculated as a measure of expected expression level.\n* Median omega = ka/ks is calculated for each cluster to estimate whether the gene is under purifying or diversifying selection.\n* Based on the co-location of orthologous genes in different genomes, gene clusters are predicted.\n* These results are written in various formats (see below) and visualized for each gene in the interactive gene table of each genome.\n* A table with all the properties of each cluster of homologous genes is written (including representation for each genome).\n\nBy building its blast database off gtdbtk and transferring functional annotations from the NCBI, metaerg.py\nuses a controlled vocabulary for taxonomy and a relatively clean vocabulary for functions. This makes annotations much\nmore concise than the original version of metaerg and many other annotation tools. In addition, metaerg uses NCBI's\nconserved domain database and RPSBlast to assign genes to subsystems for effective data exploration. Subsystems are a \nwork in progress, and can be expanded and customized as needed.\n\nThe Metaerg 2.5 pipeline ...\n* predicts CRISPR regions using [CRISPRDetect](https://github.com/davidchyou/CRISPRDetect_2.4), version 2.4.\n* predicts tRNAs using [Aragorn](https://www.ansikte.se/ARAGORN/Downloads/), version 1.2.41.\n* predicts RNA genes and other non-coding features using [Infernal](http://eddylab.org/infernal/) - cmscan and RFAM, version 1.1.4.\n* predicts retrotransposons with [LTR Harvest](http://genometools.org/tools/gt_ltrharvest.html) - LTRHarvest, genometools version 1.6.2.\n* predicts tandem repeats with [Tandem Repeats Finder](https://tandem.bu.edu/trf/trf.html), version 4.0.9.\n* predicts other repeat regions with [Repeatscout](http://bix.ucsd.edu/repeatscout/), version 1.0.5, and [Repeatmasker](http://www.repeatmasker.org/RepeatMasker/), version 4.1.5.\n* predicts coding genes with [Prodigal](https://github.com/hyattpd/Prodigal), version 2.6.3.\n* annotates taxonomy and functions of RNA and protein genes using [Diamond](https://github.com/bbuchfink/diamond), version 2.0.15, [NCBI blastn](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/), version 2.14.0 and a database of >50,000 prokaryotes, based on [gtdb](https://gtdb.ecogenomic.org/) version 214, 11,569 viral and 139 eukaryotic genomes.\n* annotates gene functions using [RPSBlast](https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/), version 2.14.0 and NCBI's Conserved Domain Database (CDD).\n* annotates genes involved in production of secondary met abolites using [Antismash](https://dl.secondarymetabolites.org/releases), version 7.0.\n* annotates membrane and translocated proteins using [PureseqTM](https://github.com/PureseqTM/pureseqTM_package) and [deepsig](https://github.com/BolognaBiocomp/deepsig).\n* assigns genes to a [built-in set of 1,001 functions](https://github.com/kinestetika/MetaErg/blob/master/src/metaerg/run_and_read/data/functional_gene_data) using [HMMER](http://hmmer.org), version 3.3.2 and commmunity contributed HMM profiles (see below).\n* estimates doubling times of a genome's host based on [codon usage bias](https://www.pnas.org/doi/epdf/10.1073/pnas.2016810118)\n* presents annotations in [datatables/jQuery](https://www.datatables.net/)-based intuititve, searchable, colorful HTML that can be explored in a web browser and copy/pasted into excel.\n* saves annotations as a fasta-amino-acid file, a genbank file, and as a sqlite database for effective exploration, statistics and visualization with python or R.\n* saves an overview of all annotated genomes' properties and functions as an excel file. \n* enables the user to add custom HMMs and expand the set of functional genes as needed.\n\nWhen using metaerg, please cite [Xiaoli Dong and Marc Strous (2019) Frontiers in Genetics](https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full)\n\n## Important changes in version 2.5\n* Comparative genomics mode was added. This depends on MMSeqs and Famsa. These programs are automatically installed by metaerg.\n\n## Important changes in version 2.4\n* Minced, TMHMM and SignalP are no longer used as helper programs.\n* CRISPRDetect, padloc, PureseqTM and deepsig are used instead. Installation is straightforward. Either use the --install_deps option, run the commands in \"installation.py\" for a manual install or use Docker.\n* These changes reduce overall runtime.\n* Various small bugs and inconveniences were fixed .\n* If you have data previously annotated with metaerg, use --update_annotations to update.\n\n## Usage:\n```\n>metaerg --contig_file contig-file.fna --database_dir /path/to/metaerg-databases/\n```\nTo annotate a set of genomes in a given dir (each file should contain the contigs of a single genome):\n```\n>metaerg --contig_file dir-with-contig-files --database_dir /path/to/metaerg-databases/ \\\n--file_extension .fa\n```\nMetaerg needs ~40 min to annotate a 4 Mb genome on a desktop computer. The comparative genomics analysis requires about\n20 min extra (in total) for a ls set of ~20 genomes.\n\nYou can use the following arguments when running metaerg:\n```text\n--contig_file A nucleotide fasta files with contigs to be annotated OR a dir containing\n nucleotide fasta files.\n--output_dir The path where the results will be saved. Default: the dir that contains the\n contig file(s) \n--database_dir The path to the metaerg database.\n--file_extension If a dir was provided to --contig_file, the file extension of the \n nucleotide fasta files. Default: .fna\n--rename_contigs Assemblers may create very long names for contigs, which is suboptimal for\n presentation of results. This argument will make metaerg rename the \n contigs more concisely.\n--rename_genomes Binners may create very long names for bins/MAGs, which is suboptimal for\n presentation of results. This argument will make metaerg rename the \n bins/MAGs more concisely. When using this option, contigs will also be\n renamed.\n--delimiter Metaerg will create \"locus tags\" (unique IDs) for genes with according to\n the following scheme: \"[MAG file name].[CONTIG name].[Gene number]\". \n By default, '.' is used as the delimiter separating the three parts of the\n ID, as shown in the example. If you want to use a different character to\n separate the parts, use this argument. If your custom character is \n detected in filenames or contig names, and you are not renaming contigs \n or MAGs, metaerg will terminate with an error message.\n--prefix When renaming genomes, by default genomes will be named \"gXXXX\", where 'g'\n is the prefix and XXXX is a number. If you would like a different prefix, \n use this argument.\n--min_contig_length Only annotate contigs that are longer than the specified length. \n Default: 0.\n--cpus Number of threads used for annotation. Default: threads available \n on the system / 2.\n--mode Use \"--mode contig\" to annotate contigs individually instead of assuming they\n are part of a genome, MAG or bin. When using this option, metaerg will not\n run repeatscout and will run prodigal in metagenome mode. Use the option\n --translation_table below to override using metagenome mode.\n Use \"--mode comparative_genomics\" to annotate a clade of related genomes/MAGs.\n Use \"--mode genome\" to annotate genomes/MAGs, one per file.\n Default: \"genome\".\n--force Overwrite previous results. By default, results of previous steps will be\n kept. You need to specify which steps will be forced (see --skip for a list\n of steps Use --force all to overwrite all previous results.\n--update_annotations Do not rerun any helper programs (keep previous results) but redo all the\n data processing. Use this option for example after you updated metaerg.\n--skip Use this argument to skip one or more annotation steps. Use the following\n names for the steps, with names to be skipped separated by a comma (,):\n \n antismash Annotate genes involved in production of secondary \n metabolites and other aspects of the \"interactome\".\n aragorn Call transfer RNAs.\n cdd Annotate gene functions according to NCBI's conserved\n domain database.\n cmscan Call RNA genes (such as rRNA genes).\n deepsig Detect protein signal peptides for translocation across\n the membrane.\n diamond_and_blastn Annotate gene functions and classify genes\n taxonomically based on homology to genes of other\n organisms. \n hmm Annotate gene functions according to metaergs built-in \n scheme.\n ltr_harvest Call retrotransposons.\n crispr_detect Call CRSIPR repeats.\n padloc Annotate genes involved in cellular defense\n prodigal Call open reading frames (genes encoding proteins). If\n you skip this step, no proteins will be annotated.\n pureseqtm Annotate transmembrane helixes (membrane proteins and\n anchors).\n repeat_masker Call any repeat sequences.\n trf Call tandem repeats.\n \n\n--translation_table Translation table(s) to be used when calling open reading frames with \n prodigal. Default: 11,25. If the mean ORF length is less than 200 amino-\n acids, metaerg will rerun prodigal with the next translation table in the\n list.\n--download_database Use this argument to download and install the prebuilt metaerg database.\n CAUTION: The metaerg databases are big, requiring approximately 165 Gb of \n disk space.\n--create_database Use this argument to build the metaerg database from scratch. The metaerg\n database consists of several components. By default, this argument will\n build all. If you wish to build specific database components, use one or\n more of the following letters:\n \n P - build prokaryotes\n V - build viruses\n E - build eukaryotes\n B - format all (PVE) blast databases\n R - build RFAM\n C - build CDD\n S - build/update community contributed HMM databases\n A - build antismash database\n D - build padloc database\n\n--checkm_dir If you have previously used checkm or checkm2 to determine the quality of\n the MAGs/bins, you can specify the dir with the checkm or checkm2 results \n here, and metaerg will integrate the estimated completeness and\n contamination into its output.\n--gtdbtk_dir Use this argument with --create_database to point metaerg to the gtdbtk\n database. It needs this to build its prokaryote blast database.\n--install_deps Use this argument to install all helper programs on your system. You need\n to follow this argument with an installation dir, where you want to have\n the programs installed.\n--padloc_database Use optionally with --install_deps if you want this database to be in a\n non-default place/filesystem. Afterward, use --create_database D to\n actually download and install the padloc database.\n--antismash_database Use optionally with --install_deps if you want this database to be in a\n non-default place/filesystem. Afterward, use --create_database A to\n actually download and install the antismash database.\n\n```\n\n## Using the Docker Image\nMetaerg depends on many helper programs and may require some time and troubleshooting to install. To avoid these issues,\nuse the [docker image](https://hub.docker.com/r/kinestetika/metaerg). Alternatively, use singularity or apptainer to run \nthe docker image on a HPC, as explained by [jkzorz](https://github.com/jkzorz/Metagenomes_Illumina/blob/main/annotation.md) and [lianchun yi]:\n\n```commandline\n>singularity pull docker://kinestetika/metaerg\n>singularity build --sandbox /path/where/top/create/metaerg_latest.sif docker://kinestetika/metaerg:latest\n>singularity exec --bind /path/to/metaerg_database:/databases --bind /path/to/fasta/files:/data --writable /path/to/the/sandbox/dir\nmetaerg --database_dir /databases --contig_file /data --file_extension .fna\n```\n\n>[!NOTE]\n>The --bind instruction binds the dir \"/path/to/metaerg_database\" (which is on your real filesystem) to the dir \"/databases\" (which is inside your container). Same for the contig dir.\n\nOr, using apptainer:\n```commandline\n>apptainer build metaerg.sif docker://kinestetika/metaerg:latest\n```\n\n## Installation\n\nTo install metaerg, its 19 helper programs (diamond, prodigal, etc.) and databases run the following commands:\n```commandline\n>python -m virtualenv metaerg-env\n>source metaerg-env/bin/activate\n>pip install --upgrade metaerg\n>metaerg --install_deps /path/to/bin_dir\n>source /path/to/bin_dir/profile\n>metaerg --download_database --database_dir /path/to/metaerg-databases/\n```\n\nIf you install metaerg's helper programs this way, before running metaerg you need to run the following, to prepend the helper programs to your path:\n\n```commandline\n>source /path/to/bin_dir/profile\n```\n\n## Database\nThe database was created from the following sources:\n* [gtdbtk](https://ecogenomics.github.io/GTDBTk/index.html) is used for its taxonomy\n* NCBI annotations of >40K representative archael and bacterial genomes present in gtdb are sourced directly from the ncbi ftp server. \n* NCBI (refseq) annotations of viral genes are obtained from [viral refseq](https://support.nlm.nih.gov/knowledgebase/article/KA-03474/en-us).\n* For Eukaryotes, for each taxon within Amoebozoa, Ancyromonadida, Apusozoa, Breviatea, CRuMs, Cryptophyceae, Discoba, Glaucocystophyceae, Haptista, Hemimastigophora, Malawimonadida, Metamonada, Rhodelphea, Rhodophyta, Sar, Aphelida, Choanoflagellata, Filasterea, Fungi, Ichthyosporea, Rotosphaeridagenomes, one genome is added to the database using [ncbi-datasets](https://www.ncbi.nlm.nih.gov/datasets/). \n* [RFAM](https://rfam.xfam.org/) and [CDD](https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) databases are also used.\n* Specialized function databases - [Cant-Hyd](https://github.com/dgittins/CANT-HYD-HydrocarbonBiodegradation/blob/main/HMMs/concatenated%20HMMs/CANT-HYD.hmm) and [MetaScan](https://zenodo.org/record/6365663).\n\nCommunity contributed HMM profiles are sourced from:\n* [HydDB](https://services.birc.au.dk/hyddb/) (Hydrogenase Families): Pleas cite [this](https://www.nature.com/articles/srep34212) paper.\n* [CANT-HYD](https://github.com/dgittins/CANT-HYD-HydrocarbonBiodegradation) (Genes involved in hydrocarbon degradation): Pleas cite [this](https://www.frontiersin.org/articles/10.3389/fmicb.2021.764058/full) paper.\n* [MetaScan](https://github.com/gcremers/metascan) (Various catabolisms): Pleas cite [this](https://www.frontiersin.org/articles/10.3389/fbinf.2022.861505/full?field=&journalName=Frontiers_in_Bioinformatics&id=861505) paper.\n* [CRISPR-CAS genes](https://www.nature.com/articles/nature21059): Pleas cite [this](https://www.nature.com/articles/nature21059) paper.\n* [Heme Copper Oxidase Families](https://github.com/ranjani-m/HCO): Pleas cite [this](https://www.biorxiv.org/content/10.1101/2021.10.15.464467v2.abstract) preprint.\n* [Cytochrome bd oxygen reductases](https://github.com/ranjani-m/cytbd-superfamily): Pleas cite [this](https://www.nature.com/articles/s41396-021-01019-4) paper.\n\nIf you for some reason need to build the database yourself (this is usually not needed as the metaerg database can be \ndownloaded as shown above):\n\n```commandline\n>metaerg --create_database --database_dir /path/to/metaerg/db --gtdbtk_dir /path/to/gtdbtkdir\n```\n\n## Accessing the .feather and .sqlite files\n[Apache Feather format](https://arrow.apache.org/docs/python/feather.html) is a binary file format for tables. Sqlite is a database format. You can for example load these data as a pandas dataframe. In **R**, use the [arrow](https://arrow.apache.org/docs/r/) package. \nEach table/database row contains a single gene or feature, defined by the following columns:\n\n```\nid the feature's unique identifier\ngenome the identifier of the genome the feature belongs to\ncontig the identifier of the contig the feature belongs to\nstart the start position of the feature (inclusive)\nend the start position of the feature (exclusive)\nstrand the strand (0 or 1 for + or - respectively)\ntype the type of feature (for example CDS, rRNA, tRNA, ncRNA, retrotransposon)\ninference the program used to infer the feature (for example prodigal for CDS)\nparent the feature's parent (for example a CRISPR repeat has a repeat_region as a parent)\nsubsystems the subsystems (functional genes) the feauture is part of \n (for example \"[ATP synthase|ATP synthase, subunit F0 B]\") \ndescr a succint description of the annotated function\ntaxon the taxon of the top blast hit\nnotes any other info (rarely used)\nseq the sequence of the feature (AA for CDS, otherwise NT)\nantismash the function assigned by antismash, if any\nsignal_peptide the type of signal peptide found, if any.\ntmh the number of transmembrane helixes found\ntmh_topology the locations of predicted transmembrane helixes in the protein sequence\nblast the top ten blast hits\ncdd the top ten cdd hits\nhmm the top ten hits to the functional gene hmm database \n```\n\nYou can for example use python and pandas to inspect annotations:\n\n``` python\nfrom pathlib import Path\nimport pandas as pd\n\ndata_dir = Path('/path/to/my/data')\nfeather_file = data_dir / 'my-genome.annotations.feather'\ncontig_file = data_dir / 'my-genome.fna'\n\ncontig_dict = load_contigs('my-genome', contig_file, delimiter='xxxx')\nfeature_data = pd.read_feather(feather_file)\nfeature_data.set_index('id', inplace=True)\n\nfor f in feature_data.itertuples():\n for k, v in f._asdict().items():\n print(f'{k:20}:{v}')\n break # comment out to iterate through all the genes...\n```\n\nUsing the .sqlite database is even easier:\n\n``` python\nfrom pathlib import Path\nfrom metaerg.datatypes import sqlite\n\ndata_dir = Path('/path/to/my/data')\nsqlite_file = data_dir / 'my-genome.annotations.sqlite'\n\ndb_connection = sqlite.connect_to_db(sqlite_file)\nfor feature in sqlite.read_all_features(db_connection): \n print(feature)\n break # comment out to iterate through all the genes...\n```\n\n## How Metaerg's functional gene calling works\nMetaerg contains a [data file](https://github.com/kinestetika/MetaErg/blob/master/src/metaerg/run_and_read/data/functional_gene_data) that defines metabolic modules, each consisting of multiple genes. These\ngenes are identified using NCBI's Conserved Domain Database (cdd) and a custom set of Hidden Markov Models contributed \nby the community/science literature. Here's what an example module looks like in the data file:\n\n```text\n>Respiration, Hydrogenases Ni-Fe\nCOG3259|TIGR03295|NF033181|COG4042 NiFe hydrogenase catch-all\nWP_015407385 NiFe hydrogenase group Ia\nWP_012675774 NiFe hydrogenase group Ib\nWP_011688559 NiFe hydrogenase group Ic\nWP_023457248 NiFe hydrogenase group Id\nWP_008030254 NiFe hydrogenase group Ie\nWP_015898101 NiFe hydrogenase group If\nWP_011822511 NiFe hydrogenase group Ig\nWP_014267363 NiFe hydrogenase group Ih\nWP_022740096 NiFe hydrogenase group Ii\nWP_010878877 NiFe hydrogenase group Ij\nWP_012036281 NiFe hydrogenase group Ik\nWP_011603008 NiFe hydrogenase group IIa\nWP_011714112 NiFe hydrogenase group IIb\nWP_011383527.1 NiFe hydrogenase group IIc\nWP_010880487 NiFe hydrogenase group IId\nWP_012020882.1 NiFe hydrogenase group IIe\nWP_022846160 NiFe hydrogenase group IIIa\nWP_011719855 NiFe hydrogenase group IIIb\nWP_013637988 NiFe hydrogenase group IIIc\nWP_011686721 NiFe hydrogenase group IIId\nWP_022739022 NiFe hydrogenase group IVa\nWP_011388069 NiFe hydrogenase group IVb\nWP_026783288 NiFe hydrogenase group IVc\nWP_014122750 NiFe hydrogenase group IVd\nWP_022739883 NiFe hydrogenase group IVe\nWP_018133527 NiFe hydrogenase group IVf\nWP_013129492 NiFe hydrogenase group IVg\nWP_011018833 NiFe hydrogenase group IVh\nWP_004029221 NiFe hydrogenase group IVi\n```\n\nA metabolic module starts with a greater-than (>) sign followed by the name of the module. Each line after lists one \ngene. The gene is defined by a one or more cdd or custom-hmm identifiers separated by or (|). After the identifiers \nfollows a space and finally the name of the gene (which can contain spaces). The example shows the Ni-Fe hydrogenase\nmodule. This module contains \"catch-all\" hydrogenase domains from the cdd database (as the first gene on the first line)\nas well as custom hmm's targeting specific subtypes of Ni-Fe hydrogenase. These hmm's were created by the [Greening Lab](https://www.nature.com/articles/srep34212).\n\nFor cdd domains, metaerg will identify a protein as the gene when one of the cdd domains is among the top 5 cdd hits.\nFor custom hmm's, metaerg will identify a protein as the gene if the hmm profile is the top hit and al least 70% of the \nprofile hmm aligns to the gene. If an hmm profile defines a trusted cutoff score, metaerg will use that score as the\ncutoff.\n\nThe advantage of using cdd compared to custom hmm databases is that cdd contains profiles for most known genes. So if,\nfor example, a protein resembles a hydrogenase, such as subunit nuoD of the respiratory complex I, cdd has\nno difficulty identifying nuoD as nuoD. However, the custom hmm database does not contain a nuoD profile, so nuoD genes,\npresent in many bacterial genomes, will be erroneously identified as a NiFe hydrogenases if you only use custom profiles.\nThis problem cannot be overcome by using a fixed e-value or score cutoff, as each hmm profile produces a unique \nrange of evalues and scores. In theory, this problem could be overcome by determining a trusted cutoff score for each\nprofile. However, that is a lot of work and not commonly done when scientists create hmm databases.\n\nThat being said, cdd is not good enough by itself, because it misses out on many important microbiome functions and \\\ngenes. Some genes are completely absent, and some are poorly defined. Expert input is needed for correct identification\nof these genes. That is why we need custom hmms.\n\nMetaerg's solution to this problem is to combine cdd with custom hmms. In the example, if a gene is not captured by the\ncatch-all cdd hydrogenase modules, it is likely not a NiFe hydrogenase. If it is, the custom hmm's will show the \nsubtype of the NiFe hydrogenase.\n\n## How to add your own custom functional gene database and HMMs\n\n1. Locate your metaerg database dir. Inside the metaerg database dir, is a dir \"hmm\". \n2. Within \"hmm\", create a new dir named \"user_config\", if it does not already exist.\n3. Inside \"user_config\", you can create one or more files with the extension \".config.txt\".\n4. These files should be formatted like in the example above.\n5. Each module should start with >.\n6. Each gene starts with one or more cdd or custom hmm names, followed by a space and the gene name/description. \n7. To query the cdd database for relevant cdd names, you use grep with the file \"/path/to/metaerg/database/cdd/cddid.tbl.\"\n8. Within \"hmm\", create a new dir named \"user_hmm\", if it does not already exist.\n9. Add your custom hmm files to this dir.\n10. Update the metaerg database using the following command:\n\n```commandline\n>metaerg --create_database S --database_dir /path/to/metaerg/db \n```\n\n11. Only those hmm profiles listed in your config file will actually be built into to the metaerg database\n",
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