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ANI calculations for small genomes/reads

skani is not necessarily designed for comparing long-reads or small contigs, but it seems to work relatively well for ANI when the reads/contigs are long enough.

• skani can not classify short-reads. Use a taxonomic classifier such as kraken for this.
• skani can not compare collections of short-reads. Use Mash or sourmash for this.
• skani can not compute AAI for long-reads.

For small contigs or long-reads, here are some suggestions:

1. Make sure to use the --qi option for skani search or skani dist if your contigs/reads are all in one file.
2. skani dist will be faster than skani search, since the bottleneck will be loading genomes into memory.

For parameters:

1. The default marker size -m is set to 1000, so we take one marker per every 1000 k-mers. A good rule of thumb is that you want at least 20 markers on average, so set -m > avg_read_length / 20.
2. Set -c to lower values, e.g. 60, for noisy-long reads (mean identity < 95). The longer + higher identity the reads, the higher -c can be.
3. skani currently loads the entire file into memory instead of processing one read at a time. Consider splitting large sets of reads.

Adjusting memory/speed tradeoffs

Marker index

The --marker-index option is available for skani dist and skani search. This loads all marker k-mers into a hash table for constant time filtering. This is turned off if less than 100 query files are input or when using the --qi option. Otherwise, it is turned on automatically.

Building the table can take up to a minute (for large databases), and the table itself is ~10 GB for 65000 genomes with default parameters. Consider changing the -m option, which is inversely proportional to the memory of this table, if memory is an issue.

Adjusting c

If you want skani to run faster, the main parameter to adjust is the -c parameter. skani's speed and memory efficiency is inversely proportional to c, so increasing c by 2x means 2x faster and less memory. As a default, c = 120 for ANI and c = 15 for AAI.

ANI: for genomes of ANI > 95%, c can be comfortably made higher up to 200 or even 300, but aligned fraction may get less accurate.

AAI: for genomes of AAI > 65%, c can be made up to 30 and still relatively accurate. Results degrade a bit after c gets past 40.

However, decreasing c may not necessarily improve ANI/AAI accuracy for > 85% ANI genomes since many other default algorithm parameters are designed these default values. Furthermore, increasing c means that distant genomes will no longer be comparable; see the section on Comparing lower ANI/AAI genomes.

All-to-all comparisons on massive data sets

skani triangle should be used for all-to-all comparisons on reasonably sized data sets. However, it loads all genome indices into memory, so RAM may be an issue. If RAM is an issue, consider:

1. Pre-sketch using skani sketch -l list_of_genomes.txt -o sketched_genomes and run skani search -d sketched_genomes -l list_of_genomes -o output to do slower but low-memory all-to-all comparisons.
2. Raising the -c parameter can help, see the above section on the -c parameter.
3. Consider raising the parameter -m for faster screens. It defaults to 1000 but 2000 is reasonable for most bacterial genomes, but may lose sensitivity on small genomes such as viruses.

Comparing lower ANI/AAI genomes.

skani focuses on ANI/AAI comparisons for genomes with > 85% ANI and > 60% AAI. To get more accurate results for low ANI/AAI values, one should use a lower value for c.

For example, the supplied genome refs/MN-03.fa is a Klebsiella Pneumoniae genome, and running skani dist refs/MN-03.fa refs/e.coli-K12.fa returns nothing because the two genomes do not have a good enough alignment. However, skani dist refs/MN-03.fa refs/e.coli-K12.fa -c 30 returns an ANI estimate of ~79%.

For distant genomes, the aligned fraction output becomes more accurate as c gets smaller. However, decreasing c may not necessarily make high ANI calculations more accurate. Nevertheless, I would not recommend ANI comparisons for genomes with < 75% ANI, and advise using skani's AAI method instead, which is tuned for sensitive comparisons by default.

Comparing only high ANI/AAI genomes with -s

The option -s controls for an approximate ANI/AAI cutoff. Computations proceed only if the putative ANI (obtained by k-mer max-containment index) is higher than -s. By default, this is 0.8 (80%) for ANI and 0.6 (60%) for AAI.

You can use a higher value of -s if you're only interested in comparing more similar strains.

This cutoff is only approximate. If the true predicted ANI is greater than -s, but the putative is smaller than -s, the calculation does not proceed. Therefore, too high -s and you'll lose sensitivity. The reverse also holds: a putative ANI can be greater than -s but the true predicted can be less than -s, in which case calculation still proceeds.

We don't recommend a lower value of -s unless you know what you're doing, since ANI/AAI calculations under 80%/60% will be bad with default parameters.