skDER
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skDER: efficient & high-resolution dereplication of microbial genomes to select representatives for comparative genomics and metagenomics.
skDER (& CiDDER)
skDER (& CIDDER): efficient & high-resolution dereplication of microbial genomes to select representatives for comparative genomics and metagenomics.
Note: Please make sure to use version 1.0.7 or greater to avoid a bug in previous versions!
Contents
- Installation
- Overview
- Algorithmic Details
- Application examples & commands
- Alternative approaches and comparisons
- Test case
- Usage
- Citation notice
- Representative genomes for select bacterial taxa
Installation
Bioconda
Note, (for some setups at least) it is critical to specify the conda-forge channel before the bioconda channel to properly configure priority and lead to a successful installation.
Recommended: For a significantly faster installation process, use mamba
in place of conda
in the below commands, by installing mamba
in your base conda environment.
conda create -n skder_env -c conda-forge -c bioconda skder
conda activate skder_env
Conda Manual
# 1. clone Git repo and change directories into it!
git clone https://github.com/raufs/skDER/
cd skDER/
# 2. create conda environment using yaml file and activate it!
conda env create -f skDER_env.yml -n skDER_env
conda activate skDER_env
# 3. complete python installation with the following command:
pip install -e .
Overview
skDER
skDER will perform dereplication of genomes using skani average nucleotide identity (ANI) and aligned fraction (AF) estimates and either a dynamic programming or greedy-based based approach. It assesses such pairwise ANI & AF estimates to determine whether two genomes are similar to each other and then chooses which genome is better suited to serve as a representative based on assembly N50 (favoring the more contiguous assembly) and connectedness (favoring genomes deemed similar to a greater number of alternate genomes).
Compared to dRep by Olm et al. 2017 and galah, skDER does not use a divide-and-conquer approach based on primary clustering with a less-accurate ANI estimator (e.g. MASH or dashing) followed by greedy clustering/dereplication based on more precise ANI estimates (for instance computed using FastANI) in a secondary round. skDER instead leverages advances in accurate yet speedy ANI calculations by skani by Shaw and Yu to simply take a "one-round" approach (albeit skani triangle itself uses a preliminary 80% ANI cutoff based on k-mer sketches). skDER is also primarily designed for selecting distinct genomes for a taxonomic group for comparative genomics rather than for metagenomic application.
skDER, specifically the "dynamic programming" based approach, can still be used for metagenomic applications if users are cautious and filter out MAGs or individual contigs which have high levels of contamination, which can be assessed using CheckM or charcoal. To support this application with the realization that most MAGs likely suffer from incompleteness, we have introduced a parameter/cutoff for the max alignment fraction difference for each pair of genomes. For example, if the AF for genome 1 to genome 2 is 95% (95% of genome 1 is contained in genome 2) and the AF for genome 2 to genome 1 is 80%, then the difference is 15%. Because the default value for the difference cutoff is 10%, in that example the genome with the larger value will automatically be regarded as redundant and become disqualified as a potential representative genome.
skDER features two distinct algorithms for dereplication (details can be found below):
- dynamic approach: approximates selection of a single representative genome per transitive cluster - results in a concise listing of representative genomes - well suited for metagenomic applications [current default].
- greedy approach: performs selection based on greedy set cover type approach - better suited to more comprehensively select representative genomes and sample more of a taxon's pangenome.
CiDDER
Currently only for bacteria - because it uses pyrodigal for gene calling!
In v1.2.0, we also introduced a second program called CiDDER (CD-hit based DEReplication) - which allows for optimizing selection of a minimal number of genomes that achieve some level of saturation of the pan-genome of the full set of genomes (see below for details). Note, CD-HIT determines protein clusters, not proper ortholog groups, and as such an approximation is made of the pan-genome space being sampled by representative genomes.
Details on Dereplication Algorithms
Using Pan-Genome Saturation (CiDDER)
Starting in v1.2.0, CiDDER was introduced to allow representative genome selection based on pan-genome satauration estimates using CD-HIT. After inferring ORFs using pyrodigal, predicted protein sequences are conatenated into a giant FASTA file and clustered using CD-HIT (where parameters are possible to adjust). Each genome is thus treated as a set of distinct protein clusters it features.
Here is an overview of the algorithm:
- Download or process input genomes.
- Predict proteins using pyrodigal.
- Comprehensive clustering of all proteins using CD-HIT (default options: )
- Select genome with the most number of distinct protein clusters as the initial representative.
- Iteratively add more representative genomes one at a time, selecting the next based on maximized addition of novel protein clusters to the current representative set.
- End addition of representative genomes if one of three criteria are met: (i) Next genome adds less than X number of distinct protein clusters (X is by default 0), (ii) over Y% of the total distinct protein clusters across all genomes are found in the so-far selected reprsentative genomes (Y is by default 90%), or (iii) over Z% of the total distinct multi-genome protein clusters across all genomes are found in the so-far selected representative genomes (Z is by default 100%). Thus, by default, only Y is used for representative genome selection.
Using the Dynamic Programming Dereplication Approach (skDER)
Unlike dRep, which implements a greedy approach for selecting representative genomes, the default dereplication method in skDER approximates selection of a single representative for coarser clusters of geneomes using a dynamic programming approach in which a set of genomes deemed as redundant is kept track of, avoiding the need to actually cluster genomes.
Here is an overview of the algorithm:
- Download or process input genomes.
- Compute and create a tsv linking each genome to their N50 assembly quality metric (N50[g]).
- Compute ANI and AF using skani triangle to get a tsv "edge listing" between pairs of genomes (with filters applied based on ANI and AF cutoffs).
- Run through "edge listing" tsv on first pass and compute connectivity (C[g]) for each genome - how many other genomes it is similar to at a certain threshold.
- Run through "N50" tsv and store information.
- Second pass through "edge listing" tsv and assess each pair one at a time keeping track of a singular set of genomes regarded as redudnant:
- if (AF[g_1] - AF[g_2]) >= parameter
--max_af_distance_cutoff
(default of 10%), then automatically regard corresponding genome of max(AF[g_1], AF[g_2]) as redundant.- else calculate the following score for each genome: N50[g]*C[g] = S[g] and regard corresponding genome for min(S[g1], S[g2]) as redundant.
- Second pass through "N50" tsv file and record genome identifier if they were never deemed redudant.
Using the Greedy Dereplication Approach (skDER)
Starting from v1.0.2, skDER also allows users to request greedy clustering instead. This generally leads to a larger, more-comprehensive selection of representative genomes that covers more of the pan-genome.
Here is an overview of this alternate approach:
- Download or process input genomes.
- Compute and create a tsv linking each genome to their N50 assembly quality metric (N50[g]).
- Compute ANI and AF using skani triangle to get a tsv "edge listing" between pairs of genomes (with filters applied based on ANI and AF cutoffs).
- Run through "edge listing" tsv on first pass and compute connectivity (C[g]) for each genome - how many other genomes it is similar to at a certain threshold
- Only consider a genome as connected to a focal genome if they share an ANI greater than the
--percent_identity_cutoff
(default of 99%) and the comparing genome exhibits an AF greater than the--aligned_fraction_cutoff
(default of 90%) to the focal genome (is sufficiently representative of both the core and auxiliary content of the focal genome).- Run through "N50" tsv and compute the score for each genome: N50[g]*C[g] = S[g] and write to new tsv where each line corresponds to a single genome, the second column corresponds to the S[g] computed, and the third column to connected genomes to the focal genome.
- Sort resulting tsv file based on S[g] in descending order and use a greedy approach to select representative genomes if they have not been accounted for as a connected genome from an already selected representative genome with a higher score.
Test Case
We provide a simple test case to dereplicate the six genomes available for Cutibacterium granulosum in GTDB release 214 using skDER, together with expected results.
To run this test case:
# Download test data
wget https://github.com/raufs/skDER/raw/main/test_case.tar.gz
# Download bash script to run skder
wget https://raw.githubusercontent.com/raufs/skDER/main/run_tests.sh
# Run the wrapper script to perform testing
bash ./run_tests.sh
Usage
If experiencing issues related to "Argument list too long", consider issuing
ulimit -S -s unlimited
prior to running skDER.
# the skder executable should be in the path after installation and can be reference as such:
skder -h
The help function should return the following:
usage: skder [-h] [-g GENOMES [GENOMES ...]] [-t TAXA_NAME] [-r GTDB_RELEASE] -o OUTPUT_DIRECTORY [-d DEREPLICATION_MODE] [-i PERCENT_IDENTITY_CUTOFF] [-tc] [-f ALIGNED_FRACTION_CUTOFF]
[-a MAX_AF_DISTANCE_CUTOFF] [-p SKANI_TRIANGLE_PARAMETERS] [-s] [-n] [-l] [-b] [-u] [-c THREADS] [-v]
Program: skder
Author: Rauf Salamzade
Affiliation: Kalan Lab, UW Madison, Department of Medical Microbiology and Immunology
skDER: efficient & high-resolution dereplication of microbial genomes to select
representative genomes.
skDER will perform dereplication of genomes using skani average nucleotide identity
(ANI) and aligned fraction (AF) estimates and either a dynamic programming or
greedy-based based approach. It assesses such pairwise ANI & AF estimates to determine
whether two genomes are similar to each other and then chooses which genome is better
suited to serve as a representative based on assembly N50 (favoring the more contiguous
assembly) and connectedness (favoring genomes deemed similar to a greater number of
alternate genomes).
options:
-h, --help show this help message and exit
-g GENOMES [GENOMES ...], --genomes GENOMES [GENOMES ...]
Genome assembly files in (gzipped) FASTA format
(accepted suffices are: *.fasta,
*.fa, *.fas, or *.fna) [Optional].
-t TAXA_NAME, --taxa-name TAXA_NAME
Genus or species identifier from GTDB for which to
download genomes for and include in
dereplication analysis [Optional].
-r GTDB_RELEASE, --gtdb-release GTDB_RELEASE
Which GTDB release to use if -t argument issued [Default is R220].
-o OUTPUT_DIRECTORY, --output-directory OUTPUT_DIRECTORY
Output directory.
-d DEREPLICATION_MODE, --dereplication-mode DEREPLICATION_MODE
Whether to use a "dynamic" (more concise) or "greedy" (more
comprehensive) approach to selecting representative genomes.
[Default is "dynamic"]
-i PERCENT_IDENTITY_CUTOFF, --percent-identity-cutoff PERCENT_IDENTITY_CUTOFF
ANI cutoff for dereplication [Default is 99.0].
-tc, --test-cutoffs Assess clustering using various pre-selected cutoffs.
-f ALIGNED_FRACTION_CUTOFF, --aligned-fraction-cutoff ALIGNED_FRACTION_CUTOFF
Aligned cutoff threshold for dereplication - only needed by
one genome [Default is 90.0].
-a MAX_AF_DISTANCE_CUTOFF, --max-af-distance-cutoff MAX_AF_DISTANCE_CUTOFF
Maximum difference for aligned fraction between a pair to
automatically disqualify the genome with a higher
AF from being a representative.
-p SKANI_TRIANGLE_PARAMETERS, --skani-triangle-parameters SKANI_TRIANGLE_PARAMETERS
Options for skani triangle. Note ANI and AF cutoffs
are specified separately and the -E parameter is always
requested. [Default is ""].
-s, --sanity-check Confirm each FASTA file provided or downloaded is actually
a FASTA file. Makes it slower, but generally
good practice.
-n, --determine-clusters
Perform secondary clustering to assign non-representative
genomes to their closest representative genomes.
-l, --symlink Symlink representative genomes in results subdirectory
instead of performing a copy of the files.
-b, --index-locally Build indices locally instead of in the directory of input genomes.
-u, --ncbi-nlm-url Try using the NCBI ftp address with '.nlm' for
ncbi-genome-download if there are issues.
-c THREADS, --threads THREADS
Number of threads/processes to use [Default is 1].
-v, --version Report version of skDER.
Usage for CiDDER
# the cidder executable should be in the path after installation and can be reference as such:
cidder -h
The help function should return the following:
usage: cidder [-h] [-g GENOMES [GENOMES ...]] [-t TAXA_NAME] [-r GTDB_RELEASE] -o OUTPUT_DIRECTORY [-p CD_HIT_PARAMS] [-mg] [-e] [-a NEW_PROTEINS_NEEDED] [-ts TOTAL_SATURATION]
[-mgs MULTI_GENOME_SATURATION] [-s] [-n] [-ns] [-l] [-u] [-c THREADS] [-m MEMORY] [-v]
Program: cidder
Author: Rauf Salamzade
Affiliation: Kalan Lab, UW Madison, Department of Medical Microbiology and Immunology
CiDDER: Performs genome dereplication based on CD-HIT clustering of proteins to
select a representative set of genomes which adequately samples the
pangenome space. Because gene prediction is performed using pyrodigal,
geneder only works for bacterial genomes at the moment.
The general algorithm is to first select the genome with the most number of distinct
open-reading-frames (ORFS; predicted genes) and then iteratively add genomes based on
which maximizes the number of new ORFs. This iterative addition of selected genomes
is performed until: (i) the next genome to add does not have a minimum of X new disintct
ORFs to add to the set of ORFs belonging, (ii) some percentage Y of the total distinct
ORFs are found to have been sampled, or (iii) some percentage Z of the total multi-genome
distinct ORFs are found to have been sampled. The "added-on" genomes from the iterative
procedure are listed as representative genomes.
For information on how to alter CD-HIT parameters, please see:
https://github.com/weizhongli/cdhit/blob/master/doc/cdhit-user-guide.wiki#cd-hit
options:
-h, --help show this help message and exit
-g GENOMES [GENOMES ...], --genomes GENOMES [GENOMES ...]
Genome assembly files in (gzipped) FASTA format
(accepted suffices are: *.fasta,
*.fa, *.fas, or *.fna) [Optional].
-t TAXA_NAME, --taxa-name TAXA_NAME
Genus or species identifier from GTDB for which to
download genomes for and include in
dereplication analysis [Optional].
-r GTDB_RELEASE, --gtdb-release GTDB_RELEASE
Which GTDB release to use if -t argument issued [Default is R220].
-o OUTPUT_DIRECTORY, --output-directory OUTPUT_DIRECTORY
Output directory.
-p CD_HIT_PARAMS, --cd-hit-params CD_HIT_PARAMS
CD-HIT parameters to use for clustering proteins - select carefully
(don't set threads or memory - those are done by default in cidder) and surround by quotes
[Default is: "-n 5 -c 0.95 -aL 0.75 -aS 0.90"]
-mg, --metagenome-mode
Run pyrodigal using metagenome mode.
-e, --include-edge-orfs
Include proteins from ORFs that hang off the edge of a contig/scaffold.
-a NEW_PROTEINS_NEEDED, --new-proteins-needed NEW_PROTEINS_NEEDED
The number of new protein clusters needed to add [Default is 0].
-ts TOTAL_SATURATION, --total-saturation TOTAL_SATURATION
The percentage of total proteins clusters needed to stop representative
genome selection [Default is 90.0].
-mgs MULTI_GENOME_SATURATION, --multi-genome-saturation MULTI_GENOME_SATURATION
The percentage of total multi-genome protein clusters needed to stop
representative genome selection [Default is 100.0].
-s, --sanity-check Confirm each FASTA file provided or downloaded is actually
a FASTA file. Makes it slower, but generally
good practice.
-n, --determine-clusters
Perform secondary clustering to assign non-representative
genomes to their closest representative genomes based on shared protein clusters.
-ns, --determine-clusters-skani
Perform secondary clustering to assign non-representative
genomes to their closest representative genomes based on skani-computed ANI.
-l, --symlink Symlink representative genomes in results subdirectory
instead of performing a copy of the files.
-u, --ncbi-nlm-url Try using the NCBI ftp address with '.nlm' for
ncbi-genome-download if there are issues.
-c THREADS, --threads THREADS
Number of threads/processes to use [Default is 1].
-m MEMORY, --memory MEMORY
The memory limit for CD-HIT in Gigabytes [Default is 0 = unlimited].
-v, --version Report version of CiDDER.
Citation notice
skDER relies heavily on advances made by skani for fast ANI estimation while retaining accuracy - thus if you use skDER for your research please cite skani:
Fast and robust metagenomic sequence comparison through sparse chaining with skani
as well as the skDER manuscript:
skDER: microbial genome dereplication approaches for comparative and metagenomic applications
If you use the option to downlod genomes for a taxonomy based on GTDB classifications, please also cite:
If you use CiDDER, please also consider citing pyrodigal (for gene-calling) and CD-HIT (for protein clustering):
CD-HIT: accelerated for clustering the next-generation sequencing data
Acknowledgments
We thank Titus Brown, Tessa Pierce-Ward, and Karthik Anantharaman for helpful discussions on the development of skDER/CiDDER - in particular the idea to directly asses the pan-genome space sampled by representative genomes. We also thank users on GitHub issues for suggesting ideas for new features.
LICENSE
BSD 3-Clause License
Copyright (c) 2023, Rauf Salamzade
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