bio-miga/miga

# MiGA types

MiGA has predefined settings and analyses. In order to maximize simplicity and
reproducibility while ensuring a wide array of use cases, MiGA uses presets
called "types". There are different [types of projects](#project-types) and
[types of datasets](#dataset-types):

## Project types

When you create a project, the type of project defines which project-wide
analyses are going to be executed (and how). The different types are:

### Mixed

**Symbol**: `mixed`.

A mixed collection of genomes, metagenomes, and viromes. This is the most basic
type of project, with no support for any project-wide analyses. It is intended
for projects that are only concerned with datasets preprocessing, *e.g.*, read
trimming, assembly, etc.

### Genomes

**Symbol**: `genomes`.

A collection of genomes. This is the most typical type of project, storing a set
of genomes from different taxonomic groups. It can be useful for anything from
indexing a reference database, to create a collection of metagenomic bins, and
anything in between.

### Clade

**Symbol**: `clade`.

A collection of closely-related genomes (ANI >= 90%). This is a project for a
collection of genomes in the same species (or closely-related species) that
require higher resolution but don't require support for a large distance range.

### Metagenomes

**Symbol**: `metagenomes`.

A collection of metagenomes and/or viromes. This is an experimental type,
currently identical to [Mixed](#mixed).

## Dataset types

Once you have a project, the type of the datasets define which analyses are
going to be executed for that particular entry (and how). The different types
are:

### Genome

**Symbol**: `genome`.

The genome from an isolate. This is the most typical case, in which you have
a genome (complete or draft) from a pure culture (excluding SAGs).

### Single-cell genome

**Symbol**: `scgenome`.

A Single-cell Amplified Genome (SAG). This is the particular case in which
you are dealing with an amplified genome from a single cell. These datasets
typically have very uneven coverage (resulting in very incomplete assemblies)
and sometimes have contamination from external DNA.

### Population genome

**Symbol**: `popgenome`.

A population genome (including metagenomic bins). This is the type of dataset
that includes sequences from different strains of the same species, such as
metagenomic bins or metagenomes of highly enriched (but not pure) cultures.

### Metagenome

**Symbol**: `metagenome`.

A metagenome (excluding viromes).

### Virome

**Symbol**: `virome`.

A viral metagenome.

# Query vs reference datasets {#reference}

In addition to the dataset types, some analyses may differ depending on the
status of a dataset as query or reference. Reference datasets are those that
integrate the database of the project; *i.e.*, those that can be queried by
analyses with other datasets like [distances](../part5/workflow.md#distances).
In contrast, query datasets are more isolated: they can use data from other
datasets (or the project), but don't get to form part of the project database.
Defining query datasets is useful when, for example, you have a reference
framework for taxonomy (formed by reference datasets) and want to find the best
classification for a genome without affecting the project itself. By default,
datasets are created as reference datasets.