skani - accurate, fast nucleotide identity calculation for MAGs, genomes, and databases

Introduction

skani is a program for calculating average nucleotide identity (ANI) from DNA sequences (contigs/MAGs/genomes) for ANI > ~80%.

skani uses an approximate mapping method without base-level alignment to get ANI. It is magnitudes faster than BLAST based methods and almost as accurate. skani offers:

1. Accurate ANI calculations for MAGs. skani is accurate for incomplete and medium-quality metagenome-assembled genomes (MAGs). Pure sketching methods (e.g. Mash) may underestimate ANI for incomplete MAGs.

2. Aligned fraction results. skani outputs the fraction of genome aligned, whereas pure k-mer based methods do not.

3. Fast computations. Indexing/sketching is ~ 3x faster than Mash, and querying is about 25x faster than FastANI (but slower than Mash).

4. Efficient database search. Querying a genome against a preprocessed database of >65000 prokaryotic genomes takes a few seconds with a single processor and ~6 GB of RAM. Constructing a database from genome sequences takes a few minutes to an hour.

Updates

v0.2.1 released - 2023-10-11

More consistent support for small contigs and sequences.

Major

• --faster-small option included in dist and triangle.

Genomes (and contigs with the --i, --ri, --qi options) with less than 20 marker k-mers are not screened according to the -s option. This was always the case but never documented. This makes skani more sensitive for small sequences, but can hamper performance on very large datasets with lots of small genomes/contigs.

This heuristic can now be disabled with the --faster-small option.

See the CHANGELOG for the skani's full versioning history.

Install

Option 1: Build from source

Requirements:

1. rust programming language and associated tools such as cargo are required and assumed to be in PATH.
2. A c compiler (e.g. GCC)
3. make

Building takes a few minutes (depending on # of cores).

git clone https://github.com/bluenote-1577/skani
cd skani

# If default rust install directory is ~/.cargo
cargo install --path . --root ~/.cargo
skani dist refs/e.coli-EC590.fasta refs/e.coli-K12.fasta

# If ~/.cargo doesn't exist use below commands instead
#cargo build --release
#./target/release/skani dist refs/e.coli-EC590.fasta refs/e.coli-K12.fasta

See the Releases page for obtaining specific versions of skani.

Option 2: Conda (source version: 0.2.1)

conda install -c bioconda skani

Option 3: Pre-built x86-64 linux statically compiled executable

We offer a pre-built statically compiled executable for x86-64 Linux systems. That is, if you're on an x86-64 Linux system, you can just download the binary and run it without installing anything.

For using the latest version of skani:

wget https://github.com/bluenote-1577/skani/releases/download/latest/skani
chmod +x skani
./skani -h

Important: the binary runs slightly slower (3-10%) most of the time, but it can be drastically slower on some tasks.

Quick start

# compare two genomes for ANI. skani is symmetric, so order does not affect ANI
skani dist genome1.fa genome2.fa 
skani dist genome2.fa genome1.fa 

# compare multiple genomes; all options take -t for multi-threading.
skani dist -t 3 -q query1.fa query2.fa -r reference1.fa reference2.fa -o all-to-all_results.txt

# compare individual fasta records (e.g. contigs)
skani dist --qi -q assembly1.fa --ri -r assembly2.fa  

# construct database and do memory-efficient search
skani sketch genomes_to_search/* -o database
skani search query1.fa query2.fa ... -d database

# use sketch from "skani sketch" output as drop-in replacement
skani dist database/query.fa.sketch database/ref.fa.sketch

# construct similarity matrix/edge list for all genomes in folder
skani triangle genome_folder/* > skani_ani_matrix.txt
skani triangle genome_folder/* -E > skani_ani_edge_list.txt

# we provide a script in this repository for clustering/visualizing distance matrices.
# requires python3, seaborn, scipy/numpy, and matplotlib.
python scripts/clustermap_triangle.py skani_ani_matrix.txt 

Tutorials and manuals

skani basic usage information

For more information about using the specific skani subcommands, see the guide linked above.

skani tutorials

1. Tutorial: setting up the GTDB prokaryotic genome database to search against

2. Tutorial: classifying entire assemblies against > 85,000 genomes in under 2 minutes

3. Tutorial: strain-level clustering of MAGs using skani, and why Mash/FastANI have issues

skani cookbook

Some common use cases and parameter settings are outlined in the cookbook.

Pre-sketched databases for searching

Pre-sketched databases can be downloaded and quickly searched against. GTDB-R214 is currently supported.

skani advanced usage information

See the advanced usage guide linked above for more information about topics such as:

• optimizing sensitivity/speed of skani
• optimizing skani for long-reads or contigs
• making skani for memory efficient for huge data sets

Output

If the resulting aligned fraction for the two genomes is < 15%, no output is given.

In practice, this means that only results with > ~82% ANI are reliably output (with default parameters). See the skani advanced usage guide for information on how to compare lower ANI genomes.

The default output for search and dist looks like

Ref_file	Query_file	ANI	Align_fraction_ref	Align_fraction_query	Ref_name	Query_name
refs/e.coli-EC590.fasta	refs/e.coli-K12.fasta	99.39	93.95	93.37	NZ_CP016182.2 Escherichia coli strain EC590 chromosome, complete genome	NC_007779.1 Escherichia coli str. K-12 substr. W3110, complete sequence

• Ref_file: the filename of the reference.
• Query_file: the filename of the query.
• ANI: the ANI.
• Aligned_fraction_query/reference: fraction of query/reference covered by alignments.
• Ref/Query_name: the id of the first record in the reference/query file.

The order of results is dependent on the command and not guaranteed to be deterministic when > 5000 query genomes are present. dist and search try to place the highest ANI results first.

Citation

Jim Shaw and Yun William Yu. Fast and robust metagenomic sequence comparison through sparse chaining with skani. Nature Methods (2023). https://doi.org/10.1038/s41592-023-02018-3

Feature requests, issues

skani is actively being developed by me (Jim Shaw). I'm more than happy to accommodate simple feature requests (different types of outputs, etc). Feel free to open an issue with your feature request on the GitHub repository. If you catch any bugs, please open an issue or e-mail me (e-mail on my website).

Calling skani from rust or python

Rust API

If you're interested in using skani as a rust library, check out the minimal example here: https://github.com/bluenote-1577/skani-lib-example. The documentation is currently minimal (https://docs.rs/skani/0.1.0/skani/) and I guarantee no API stability.

Python bindings

If you're interested in calling skani from python, see the pyskani python interface and bindings to skani written by Martin Larralde. Note: I am not personally involved in the pyskani project and do not offer guarantees on the correctness of the outputs.