Core Concepts#
MirMachine runs are controlled by four main ideas: node, family,
model, and score.
Node#
A node is a taxonomic label used to select expected miRNA families.
Use
--node <name>to choose the query node.Use
--print-all-nodesto list valid node names.Use
MirMachine.py --node <name>(without--speciesand--genome) to print families linked to that node.
Family#
A family is a single miRNA family name (for example Let-7).
Use
--family <name>for one-family runs.Family mode does not require
--node.Use
--print-all-familiesto list available families for each model.
Model#
MirMachine supports three covariance-model sets:
combined(default): models built across all supported taxaproto: proto-specific model setdeutero: deutero-specific model set
Model choice affects which family CMs are available and the cutoff file used for confidence filtering.
Search Scope Controls#
By default, MirMachine searches ancestor nodes for the selected query.
--single-node-onlylimits searched families to exactly the selected node.--add-all-nodesexpands the search with descendant nodes.
These flags only apply to --node runs.
Scoring and Confidence#
Each hit receives a CM bitscore and E-value.
--evaluecontrolscmsearch --incE(default:0.2).Family-specific trusted cutoffs are then applied to classify filtered predictions.
GFF headers include a miRNA score (percent of detected families among searched families).
Input Expectations#
--genomemust point to an uncompressed FASTA file.--speciesis the run label used in output filenames and YAML config names.