Distances

MiGA Manual
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Introduction
Part I: What is MiGA?
Part II: Getting started
Part IV: Deploying examples
Part V: Additional details
Part VI: Workflows
Distances
MiGA estimates distances (or similarities) between datasets using different techniques. Only genome-to-genome comparisons have been implemented, including the genomes of isolates, metagenome-assembled genomes, or single-cell amplified genomes. No metagenome-to-metagenome or genome-to-metagenome distances are currently available in MiGA.
Hierarchical approach to distances
For any given pair of genomes, MiGA attempts a hierarchical approach to identify the most appopriate metric of similarity:
1. First, the genomes are compared using hAAI. If this method is skipped, if it fails, or if the value is greater than 90%, MiGA continues to step 2. Otherwise, this value is used to estimate the AAI, both values are recorded, and the comparison ends.
2. Next, MiGA compares genomes using AAI. Whenever the AAI is 85% or higher, MiGA continues to step 3. Otherwise the comparison ends.
3. Finally, MiGA estimates ANI.
hAAI
Heuristic Average Amino Acid Identity. The hAAI is the average amino acid identity between the highly conserved proteins of two genomes, as identified by essential genes. It is used to estimate AAI for distant pairs, but it loses resolution between close relatives. This metric is completely bypassed in projects of type clade as well as projects with the metadata field haai_p=no. This field also controls the Software used: blast+ (default), blast, blat, or diamond.
AAI
Average Amino Acid Identity. The AAI is the average amino acid identity between all proteins of two genomes, as identified by cds. When running this analysis, the intermediate reciprocal best matches (RBMs) are also stored in projects of type clade. This feature can be turned off to save storage space or forced to be on in any project type using the metadata field aai_save_rbm=false or aai_save_rbm=true, respectively. The Software used as a search engine can be controlled using the metadata field haai_p: blast+ (default), blast, blat, or diamond. Workflows use blast+ by default, or diamond if the flag --fast is passed (whenever available).
ANI
Average Nucleotide Identity. The ANI is the average nucleotide identity between fragments of two genomes. The Software used as a search engine can be controlled using the metadata field haai_p: blast+ (default), blast, blat, or fastani. Workflows use blast+ by default, or fastani if the flag --fast is passed (whenever available).
SQLite3 schema
The information on the different similarity metrics above is stored in SQLite3 database files. The general schema for hAAI and AAI is:
CREATE TABLE aai(
  seq1 varchar(256), seq2 varchar(256),
  aai float, sd float, n int, omega int
);
CREATE TABLE rbm(
  seq1 varchar(256), seq2 varchar(256),
  id1 varchar(256), id2 varchar(256),
  id float, evalue float, bitscore float
);
The aai table holds the metric values, including: the name of the genomes compared seq1 and seq2, the AAI or hAAI as percentage aai, the standard deviation across proteins when available sd, the total number of RBMs n, and the smaller number of proteins from the two genomes omega. The rbm table holds the RBMs for AAI (whenever available if stored) and is always empty for hAAI.
The general schema for ANI is:
CREATE TABLE ani(
  seq1 varchar(256), seq2 varchar(256),
  ani float, sd float, n int, omega int
);
CREATE TABLE rbm(
  seq1 varchar(256), seq2 varchar(256),
  id1 int, id2 int, id float,
  evalue float, bitscore float
);
CREATE TABLE regions(
  seq varchar(256), id int, source varchar(256),
  `start` int, `end` int
);
The ani table holds the metric values, including: the name of the genomes compared seq1 and seq2, the ANI as percentage ani, the standard deviation across fragments when available sd, the total number of RBM fragments n, and the smaller number of fragments from the two genomes omega. The tables rbm and regions are always empty.
Input data
Clustering
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Hierarchical approach to distances