MATPredict

Find and type the mating-type (MAT) loci of fungi in genome assemblies.

MATPredict searches a genome assembly with a curated reference database of MAT loci. It reports where each locus is, which genes it carries, and which idiomorph or mating type it is (for example Plus/Minus, MAT1-1/MAT1-2, HD/PR alleles). Every reference record traces to a publication or a validated deposit, and every call reports the evidence behind it.

Current release: v0.8.1 (detection code, database and the data, calls and counts shown on the site are those of the v0.8.1 run). Changes: CHANGELOG.md.

Project goals

Curated reference loci. Develop a curated set of model MAT loci for different taxonomic groups, one record per strain and idiomorph, with provenance for every gene.
Detection. Use translated searches to find the key MAT genes and their flanking genes in new genome assemblies, annotated or not.
Typing. Classify each locus by the mating-type naming scheme of its group (for example Plus/Minus, MAT1-1/MAT1-2, HD/PR alleles), and report when the evidence is not enough to decide. Idiomorph labels (Lineage__System_Cell) are computed on output from the raw names by a versioned registry; see the naming note.
Standard reports. Extract the locus region and its genes, and report them in a standard format with figures and tables. Report uncalled genomes with a reason, and two idiomorphs in one genome as a flag for review.
Synteny. Visualize the synteny of MAT regions across sets of species.
Scale. Run on thousands of genomes (for example the full public NCBI fungal genome collection, Fungi_BFD; its 2026 run is documented at https://github.com/1KFG/Fungi_BFD_run2026/) on a SLURM cluster.

Workflow

                 genome assembly (FASTA)  [+ proteins]  [+ NCBI taxid]
                                  │
                                  ▼
 ┌──────────────────────────────────────────────────────────────────────┐
 │ 1. ROUTE      taxid lineage ──► curated families in db/<Phylum>/      │
 │               order.yml. No match ──► phylum fallback, or             │
 │               "not_searched". --phylum forces one phylum.             │
 ├──────────────────────────────────────────────────────────────────────┤
 │ 2. SEARCH     tblastn (genome) / diamond (proteome) with the          │
 │               reference proteins: core MAT genes + flanking genes     │
 ├──────────────────────────────────────────────────────────────────────┤
 │ 3. CLUSTER    hits ──► candidate loci per contig (lineage gap rules)  │
 ├──────────────────────────────────────────────────────────────────────┤
 │ 4. POLISH     exonerate / miniprot gene models on a sliced window     │
 │               (at most 6 loci per family)                             │
 ├──────────────────────────────────────────────────────────────────────┤
 │ 5. VERIFY     modelled-gene bar, fraction floor, MAT-gene gate,       │
 │               flank-carried rule, paralog class, CAAX check           │
 ├──────────────────────────────────────────────────────────────────────┤
 │ 6. TYPE       idiomorph from gene content, or profile-HMM classifier  │
 │               (Mucoromycota sexP/sexM); margin and confidence tier    │
 └──────────────────────────────────────────────────────────────────────┘
                                  │
                                  ▼
     detection_report.yaml   detected_loci.gff3   [detected_loci.fasta]
     (calls, evidence, withheld loci, uncalled reason, two-idiomorph flag)

The reference database feeds steps 1, 2 and 6:

 literature / GenBank deposit
        │  propose (PMID/DOI + source text required)
        ▼
 db/candidates/ ──► automated validation ──► curator sign-off ──► db/<Phylum>/<Order>/
                    (accession resolves,       (accept or          locus.gbk, locus.gff3,
                     translation matches,       reject with         proteins.faa,
                     taxonomy current)          a reason)           metadata.yaml
                                                                         │
                                         classifiers, regression check ◄─┘

Supported lineages

The curated families are defined in each phylum's order.yml.

Phylum	Families (`locus_name`)	Records	Definition
Ascomycota	MAT, PM, mat1, mat2, mat3, MATsc, MATyl, MTL, MATtub	82	db/Ascomycota/order.yml
Basidiomycota	HD, PR, MAT, Aalpha, Abeta, Balpha, Bbeta, aLocus, bLocus, rustHD, redPR, redHD, wallMAT	74	db/Basidiomycota/order.yml
Mucoromycota	MAT (sexP / sexM, with an idiomorph classifier)	21	db/Mucoromycota/order.yml

Other phyla (for example Mortierellomycota, Kickxellomycota, Chytridiomycota) have no curated family. By default their genomes are reported as not_searched. Use --exhaustive to search them with every family.

Curation process

Each reference record is a folder db/<Phylum>/<Order>/<record_id>/ with four files. The record id is <taxid>_<strain>_<locus>_<idiomorph>.

File	Content
`metadata.yaml`	Taxonomy, organism, strain, mating type, locus coordinates, genes, evidence (tier, PMID/DOI, source text), validation result, curation history. Schema: db/_schema/metadata.schema.yaml
`locus.gbk`	The locus as GenBank
`locus.gff3`	Gene features of the locus
`proteins.faa`	Reference proteins, tagged with gene name and role (`core_MAT`, `flanking_conserved`, `flanking_variable`)

Rules:

Evidence. Tier 1: a published and experimentally validated locus. Tier 2: genome-derived coordinates, admitted only with extra checks (domain evidence, alignment to curated orthologs, or a gene tree) and a record of how the locus was derived. A source never comes from inference alone.
Proposal. A candidate record goes to db/candidates/ with a resolvable PMID/DOI or accession and the exact source text.
Automated validation. The accession must resolve, the translation must match the cited protein, and the taxonomy must be current.
Curator sign-off. Only the curator moves a record to accepted. Rejected candidates are kept with a reason.
Regression check. A change to a record, classifier or rule runs the regression panel before sign-off. See docs/regression-check.md.

Related files:

Path	Purpose
db/_schema/	JSON schemas for `metadata.yaml` and `order.yml`; DuckDB schema
db/Mucoromycota/classifiers/	Idiomorph classifier: HMMs, build manifest, paralog negatives
db/suppress.txt	Genomes that must not be searched (for example amplicon-only deposits)
db/taxon_overrides.tsv	Genomes whose deposited name is likely wrong (used in held-out scoring only)
db/assembly_zygosity.yml	Lineages where an assembly can collapse a heterozygous locus
ANNOTATION_ERRORS_FIXED_REPORT.md	Errors found in deposits, annotations and literature, and what was done
analysis/	One report per study, the curator's decisions, open questions and an index
docs/publication-notable-findings/	Findings with citeable evidence (new loci, homothallism candidates)
docs/holdout-benchmark.md	How held-out recall is measured

Repository layout

Path	Content
src/MATPredict/	The Python package and the `matpredict` CLI
src/MATPredict/detect/	Detection pipeline (routing, search, polish, gates, classifier, reports)
src/MATPredict/db/	Database curation, validation, taxonomy, drawing
db/	The curated reference database
scripts/	SLURM runners, regression check, classifier build, record tools
tests/	pytest suite
testset/	Ground-truth coordinates and the regression panel list
analysis/	Study reports and the decision log
docs/	Design notes, handoffs, benchmark and regression documentation

Quickstart

Install

MATPredict uses pixi for its environment. The environment holds Python, BLAST+, DIAMOND, miniprot, exonerate, Augustus, pyhmmer, MAFFT and the drawing tools. Linux (x86-64) only.

git clone https://github.com/stajichlab/MATPredict.git
cd MATPredict
pixi install
pixi run matpredict --help

Other ways to install

Docker. Each release publishes an image to the GitHub Container Registry. The image holds the environment, the package, the curated database and an NCBI taxonomy snapshot (see Offline taxonomy), so a run needs no network.

docker pull ghcr.io/stajichlab/matpredict:latest     # or a release version, :<version>
docker run --rm -v "$PWD":/data \
  ghcr.io/stajichlab/matpredict:latest \
  detect --genome /data/genome.fna --taxid 4837 --out-dir /data/out

On an HPC system without Docker, use Apptainer/Singularity:

apptainer pull matpredict.sif docker://ghcr.io/stajichlab/matpredict:latest
apptainer run matpredict.sif detect --genome genome.fna --taxid 4837 --out-dir out

macOS (Apple Silicon). pixi install works natively on osx-arm64 (every tool in pixi.toml resolves there); the Docker image is also published for linux/arm64.

Conda / mamba. environment.yml lists the same packages as pixi.toml (exported with pixi workspace export conda-environment). Run from the repository root, because it installs MATPredict from the checkout:

mamba env create -f environment.yml
mamba activate matpredict
matpredict --help

The exact versions used for releases are in pixi.lock.

Run one genome

This example uses Phycomyces blakesleeanus NRRL 1555 (NCBI taxid 4837).

# 1. Download the assembly from NCBI
curl -L -o phybl.zip \
  "https://api.ncbi.nlm.nih.gov/datasets/v2/genome/accession/GCF_001638985.1/download?include_annotation_type=GENOME_FASTA"
unzip -q phybl.zip

# 2. Detect MAT loci (run from the repository root, so db/ is found).
#    Set your e-mail for NCBI first (see Configuration).
export MATPREDICT_NCBI_EMAIL=you@example.org
pixi run matpredict detect \
  --genome ncbi_dataset/data/GCF_001638985.1/GCF_001638985.1_Phybl2_genomic.fna \
  --taxid 4837 \
  --out-dir phybl_out

The run takes about 1.5 minutes on one CPU. phybl_out/detection_report.yaml reports one locus:

Mucoromycota:MAT  Minus  high  mat_locus  NW_017265134.1:3978283-3991589
genes: tptA, sexM, rnhA, algA

This genome is the source of a curated record (4837_nrrl1555_MAT_Minus), so the example shows that the tool works. It is not an independent test.

Usage

`matpredict detect`

Every option, policy, preset (--preset v0.8.1 is what the v0.8.1 run used) and environment variable is listed in docs/detect-options.md.

matpredict detect --genome GENOME [--proteins PROTEINS] [--taxid TAXID]
                  --out-dir OUT_DIR [options]

Option	Description
`--genome`	Genome assembly, FASTA (uncompressed)
`--proteins`	Optional proteome FASTA. Enables the DIAMOND fast path
`--taxid`	NCBI taxid. Selects the families for the lineage and the genetic code
`--phylum {Ascomycota,Basidiomycota,Mucoromycota}`	Search one phylum's families and skip taxid routing
`--exhaustive`	Search every family when no curated family covers the genome
`--genetic-code N` / `--genetic-code-map FILE`	Translation table, for one genome or per genome in a batch
`--max-polished-clusters-per-family N`	Polish cap (default 6; 0 = no cap)
`--min-hits N`	Distinct genes a cluster needs before polishing (default 2)
`--min-identity PCT`	Best-hit identity a cluster needs before polishing (default: none)
`--no-require-core-role`	Allow polishing of clusters without a core MAT gene hit
`--exclude-records IDS`	Withhold curated records (comma-separated) for leave-one-out tests
`--evidence-diagnostics FILE`	Write per-hit evidence as JSON lines
`--html` / `--no-html`	Write `report.html` (default: on). `MATPREDICT_HTML=0` turns the default off; the batch scripts set it
`--pdf`	Also write `report.pdf` (WeasyPrint, in the pixi environment and the image; off by default). A report or PDF error is logged and never fails the run
`--sample` / `--organism`	Names shown in the report and recorded in the `run` block
`--emit-cds-fasta`	Add CDS features with translations to the GFF3, and write `detected_loci.fasta` (full sequence of every contig with a call; can be large)

Outputs (in --out-dir):

File	Content
`detection_report.yaml`	Routing mode, genetic code, detected loci (family, contig, coordinates, idiomorph, confidence, `locus_class`, genes found and missing, classifier scores and margin, gene evidence), withheld loci with the reason, zygosity and two-idiomorph flags, families not detected
`detected_loci.gff3`	One gene feature per found or missing gene at each locus
`detected_loci.fasta`	Only with `--emit-cds-fasta`
`report.html`	Readable report (see `matpredict report genome`); not with `--no-html`
`report.pdf`	Only with `--pdf`

Routing modes (routing_mode in the report): lineage (taxid matched a family scope), phylum_fallback, explicit_phylum (--phylum), exhaustive, not_searched.

`matpredict report genome`

matpredict report genome --run OUT_DIR [--out report.html] [--pdf report.pdf] [--sample NAME]

detect writes this report itself by default; use this command to re-render a run (for example one made before the report existed, or with --pdf). Writes a self-contained HTML report of one detect run (default OUT_DIR/report.html): the result in plain words, a gene-order figure and evidence table per called locus, what was searched, withheld candidates and provenance. Open it in any browser; "Save as PDF" prints it. --pdf writes the PDF directly with WeasyPrint (part of the pixi environment, environment.yml and the Docker image), or, where WeasyPrint is missing, a headless Chrome or Chromium on PATH. detect --sample and --organism set the names the report shows.

`matpredict report table` and `matpredict report summary`

matpredict report table --runs RUNS_DIR --out-dir TABLES [--samples sheet.csv --samples-key ASMID]
matpredict report summary --table-dir TABLES --by COLUMN

report table reads a directory of detect --out-dir folders and writes genomes.tsv (one row per genome: assembly stats, runtime, routing, number of loci, idiomorphs, two_idiomorphs, homothallic_candidate, withheld count) and loci.tsv (one row per called locus: contig, span, family, idiomorph, confidence, locus class, core and supported spans, genes). A folder with no report gets a row with status failed, skipped or no_report and the reason. With --samples, the sheet's columns join on the folder name and a sheet row with no folder gets status not_run.

report summary groups genomes.tsv by any column (for example PHYLUM from the sample sheet) and writes summary_by_COLUMN.tsv and .md: genomes, searched, called, recovery (called / searched), high-confidence calls, not searched, failed, not run, genomes with both idiomorphs, homothallic candidates and idiomorph counts. A genome with one idiomorph is not shown to be heterothallic; both_idiomorphs and homothallic_candidate are signals, not verdicts.

`matpredict detect` helper commands

Command	Purpose
`matpredict detect rollout-summary --reports-dir DIR --out FILE`	Combine the reports of a batch into one summary
`matpredict detect suppress-filter --list FILE`	Remove suppressed genomes from an `ASMID<TAB>...` list
`matpredict detect audit-scope`	Check every record's taxid against its family's `taxonomic_scope`
`matpredict detect benchmark`	Leave-one-out sensitivity/specificity benchmark

`matpredict reads-type`

Call the MAT idiomorph of a strain from raw reads, with no assembly. It counts k-mers that occur in only one idiomorph reference. It needs a reference sequence for each idiomorph of the same species or lineage.

Steps

Get one FASTA per idiomorph (two or more). Use the idiomorph sequence with a little shared flank on both ends, from a GenBank record or from a genome that matpredict detect has typed. A reference from the same lineage works better than one from another species: the Fola MAT1-2 locus differs from the GenBank F. oxysporum record at 0.9% of its positions, and the panel built from Fola genomes agreed with more strains (see below). Ready panels: panels/fusarium_oxysporum_fola/ and panels/aspergillus_fumigatus/ (A1163 and Af293 locus segments from the database).

Run one strain:

matpredict reads-type \
  --idiomorph MAT1-1=panels/fusarium_oxysporum_fola/MAT1-1.fasta \
  --idiomorph MAT1-2=panels/fusarium_oxysporum_fola/MAT1-2.fasta \
  --reads strain_R1.fastq.gz strain_R2.fastq.gz --sample strain \
  --max-reads 8000000 --out strain.mat.tsv

Run many strains: give a TSV with columns sample and reads (files separated by commas), and use --samples in place of --reads.
Read the output (one row per strain) as described below.

Options

Option	Meaning
`--idiomorph NAME=FASTA`	One idiomorph reference. Repeat for each idiomorph; at least two. `NAME` is free text and appears in the output.
`--k K`	k-mer length. Default 31. A shorter k tolerates more differences from the reference but gives more shared k-mers.
`--min-unique-run N`	Keep an idiomorph-unique k-mer only if it lies in a run of N consecutive unique positions of its locus. Drops k-mers made unique by SNPs in shared flanks, which match any strain with that flank allele. Default 100; 0 keeps all.
`--reads FILE...`	FASTQ files of one strain. Plain, `.gz` and `.zst` are read directly (`.zst` needs `zstd`).
`--sample NAME`	Name of the strain in the output. Default: the first file name up to the first dot.
`--samples TSV`	Many strains: columns `sample`, `reads`.
`--max-reads N`	Use only the first N reads. Files are read in the order given, so with R1 and R2 the cap uses R1 first. Default: all reads.
`--out FILE`	Output TSV.

Output columns: sample, call, <NAME>_breadth (fraction of the idiomorph's unique k-mers seen), <NAME>_depth (mean count of those k-mers), reads_used, shared_depth (mean count of the k-mers that both idiomorphs share; a single-copy depth control), flags.

Calls

Call	Meaning
`<NAME>`	One idiomorph is present and the others are not.
`both`	Two or more idiomorphs are present: a homothallic strain, a diploid or heterokaryon, or a mixed sample. Read the depths.
`none`	The shared flank k-mers are covered, but no idiomorph is. The panel may lack this strain's allele.
`low_depth`	The shared control depth is below 1, so no call is made. The sample may also be from a different species.

An idiomorph is present when its depth is at least 0.10 of shared_depth and its breadth is at least 0.50 of the breadth that its depth predicts (1 - exp(-depth)). These cut-offs are set in src/MATPredict/reads/typing.py.

Flags: trace_<NAME> means some k-mers of an idiomorph were seen but not enough to call it. idiomorph_depth_ratio_below_0.25 on a both call means the minor idiomorph has less than a quarter of the depth of the major one.

What was measured (Fola, F. oxysporum f. sp. lactucae; details in analysis/2026-10-06_fola-reads-type.md):

147 of 148 strains agree with a samtools breadth call (the one difference has no signal in either method). With the GenBank panel, 145 of 148.
Simulated mixes of two strains (3 pairs): both is called from a 10% minor MAT1-2 share in 3 of 3 pairs, from 5% in 2 of 3, and from a 20% minor MAT1-1 share in 3 of 3. trace_MAT1-2 also appears in pure MAT1-1 strains (background), so it is not evidence of a minor idiomorph.
A. fumigatus (304 assemblies, 331 read sets): reads agree with an assembly BLAST truth in 293 of 296 strains (99.0%); detect on the assemblies agrees in 288 of 297 and reports a single idiomorph for all 8 assemblies that hold both. Seven of those 8 are both from reads (report).
Speed: about 1.5 minutes for 8 million reads on one CPU (Python).
A reference that differs from the strain at about one position in 40 gives none; at one in 60 it is called correctly.

Not part of the subcommand yet (scripts, tested on a few strains only): scripts/build_blastx_tiers.py (protein references by taxonomic distance, for DIAMOND blastx of reads), scripts/recruit_pairs.py (collect read pairs for a local assembly), scripts/simulate_mixes.sh (mixed samples) and scripts/compare_reads_type.py. Figure scripts for the two reports are in scripts/figures/; outputs are in analysis/figures/. A protein search works with references from the same genus and fails with distant ones; see the analysis note.

Batch runs on SLURM

Script	Purpose
scripts/run_clade_panel.slurm	Run every BFD genome of one clade (`RANK=ORDER CLADE=Mucorales`). Per-genome timeout `GENOME_TIMEOUT` (default 3600 s)
scripts/submit_binned_panel.py	Split a panel into genome-size bins with matching time limits
scripts/run_detection_batch.slurm	Generic batch runner
scripts/run_regression_panel.sh, scripts/regression_check.py	Regression check: baseline and candidate on a fixed panel, then a diff

Run a batch from a frozen git worktree on shared storage, not from a tree you are editing. Set SRC and MATPREDICT_DB_ROOT to that worktree, so the code and the database come from the same commit.

`matpredict curate-db`

Command	Purpose
`propose --phylum P --record-file F`	Add a candidate record to `db/candidates/`
`validate --phylum P --record-id ID`	Run automated validation on a candidate
`accept --phylum P --order-or-family O --record-id ID`	Move a validated candidate into `db/<Phylum>/<Order>/` (curator only)
`reject --phylum P --record-id ID --reason TEXT`	Reject a candidate and keep the reason
`build-gff` / `backfill-gff`	Write `locus.gff3` from the record
`draw-locus` / `draw-synteny`	Draw one locus, or synteny across records (pyGenomeViz, clinker)
`build-duckdb [--out FILE]`	Build the DuckDB query cache from the record files
`release`	Stamp a database release on all records

validate calls taxonkit for the taxonomy check. Login nodes may cap a process at about 1 GB (the curator's report; I could not see the cap from a session). taxonkit alone peaks at about 0.85 GB (measured 2026-10-18), so Python, the NCBI client and taxonkit together can exceed 1 GB, and a kill (taxonkit exited -9) was seen once on a login node. The whole validate peaks at about 0.95 GB and takes about 30 s in a 2 GB job (measured on the short partition). Run it on a compute node: sbatch -p short scripts/curate_db_validate.slurm PHYLUM RECORD_ID (highclock and exfab also work) (set REPO to the checkout and PY to a Python with the dependencies and taxonkit on its PATH). A taxonkit failure is reported as an error and leaves taxonomy_current unset; it is never written as false.

Configuration

MATPredict looks up NCBI taxonomy (for --taxid routing and the genetic code) through NCBI E-utilities. NCBI asks every caller to send an e-mail address. MATPredict has no default address. Set yours once, in one of two ways:

Config file (recommended). Create ~/.config/matpredict/config.toml:
```
[ncbi]
email = "you@example.org"
# api_key = "your-ncbi-api-key"   # optional: higher request rate
```
MATPredict reads $MATPREDICT_CONFIG instead when it is set, or $XDG_CONFIG_HOME/matpredict/config.toml when XDG_CONFIG_HOME is set.

Environment variables, for example in ~/.bashrc or a SLURM script:

export MATPREDICT_NCBI_EMAIL=you@example.org
export MATPREDICT_NCBI_API_KEY=your-ncbi-api-key   # optional

An environment variable wins over the config file. With no address set, the tool still runs, sends no e-mail, and logs one warning.

Variable	Default	Purpose
`MATPREDICT_NCBI_EMAIL`	none	E-mail sent to NCBI E-utilities
`MATPREDICT_NCBI_API_KEY`	none	NCBI API key
`MATPREDICT_CONFIG`	`~/.config/matpredict/config.toml`	Config file path
`MATPREDICT_DB_ROOT`	`./db`	Reference database to use
`MATPREDICT_CACHE_DIR`	`./.matpredict_cache`	Cache for NCBI responses. The e-mail and API key are not part of the cache key, so users with different settings share one cache
`MATPREDICT_TAXONOMY`	none (image: `/app/taxonomy/ncbi_taxonomy.tsv.zst`)	Offline NCBI taxonomy table; used before E-utilities
`MATPREDICT_OFFLINE`	off (image: `1`)	`1`: never call NCBI; a taxid not in the table is reported in `routing_error`

Offline taxonomy

detect needs the lineage, phylum and genetic code of a --taxid. A local table answers these without NCBI E-utilities. Build it from a dated NCBI archive and point MATPREDICT_TAXONOMY at it:

curl -O https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdump_archive/taxdmp_2026-10-01.zip
matpredict curate-db build-taxonomy --taxdump taxdmp_2026-10-01.zip \
    --snapshot 2026-10-01 --out ncbi_taxonomy.tsv.zst
export MATPREDICT_TAXONOMY=$PWD/ncbi_taxonomy.tsv.zst
export MATPREDICT_OFFLINE=1        # optional: never call NCBI

All taxa, about 24 MB; loading it takes about 2 s and 80 MB of memory. Each report records the source in taxonomy_source (for example local NCBI taxonomy snapshot 2026-10-01). The Docker image builds this table at image build time and sets both variables.

Tests

pixi run -e test pytest -q

Validation and known limits

Measured results (details in analysis/INDEX.md). The NCBI public genomes and held-out rows are detection (call) rates from the v0.8.1 definitive run, not accuracy: no truth set exists for the NCBI public genomes. Subphylum rates count the classes of each subphylum; the 12 Basidiomycota genomes suppressed by the shared list (no report) are counted as not called; the nine rust genomes of the allow list (#165) are searched. The ground-truth, typing and hold-out rows are older measurements and were not repeated for v0.8.1. How each campaign was judged is in results/2026-10_cleanrun_preparation/.

Test	Result
Zygomycete ground-truth set (23 genomes)	23/23 loci and idiomorphs
Held-out LCG Mucoromycotina genomes (never used in curation)	549/621 called (v0.8.1 run)
Held-out Jena Mucoromycotina genomes	61/64 called (v0.8.1 run)
Idiomorph typing validation	0/540 typed wrong
Pezizomycotina hold-out, order radius	7/7
Ascomycota, 19,415 NCBI public genomes (v0.8.1 run)	17,618 called (90.7%)
Basidiomycota, 3,275 NCBI public genomes (v0.8.1 run)	2,995 called (91.5%): Agaricomycotina 92.6%, Ustilaginomycotina 97.7%, Pucciniomycotina 86.3%
Mucoromycotina, 289 NCBI public genomes (v0.8.1 run)	258 called (89.3%)

Known limits (open items in analysis/open-questions.md):

Only three phyla have curated families.
A genome far from every reference often fails the evidence bar, so an uncalled genome is weak evidence that the genome has no MAT locus. The full Ascomycota run (19,415 genomes, v0.8.1) leaves 1,725 uncalled and 72 not run.
Fragmented assemblies can split a locus or lose its flanking genes.
In Mucoromycota, Absidia flanking genes are often off-scaffold, and the gene order of the Lichtheimiaceae is not yet known.
Basidiomycota pheromone-receptor calls that depend only on the CAAX motif are marked unverified. Each pheromone-receptor call also carries receptor_array_id, receptor_array_size, receptor_array_members and an receptor_array_support flag; receptor arrays occur for mating and non-mating receptors alike, so membership does not make a locus a mating receptor (docs/receptor-arrays.md).
A two-idiomorph result is a flag for review. It can come from homothallism, a mixed culture or a duplication.

Authors

Jason E. Stajich — University of California, Riverside (jason.stajich@ucr.edu, ORCID 0000-0002-7591-0020)

Citation

No paper describes MATPredict yet. Cite the software and the release you used. The citation metadata is in CITATION.cff; GitHub shows it under "Cite this repository".

Stajich JE. MATPredict: MAT locus identification in Fungi. Version 0.9.0. https://github.com/stajichlab/MATPredict

@software{matpredict,
  author  = {Stajich, Jason E.},
  title   = {MATPredict: MAT locus identification in Fungi},
  version = {0.9.0},
  url     = {https://github.com/stajichlab/MATPredict},
  year    = {2026}
}

Please also cite the publications behind the reference records you rely on. Each record's metadata.yaml lists its source (PMID/DOI or accession).

License

MATPredict is released under the MIT License. See LICENSE.

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MATPredict

Contents

Project goals

Workflow

Supported lineages

Curation process

Repository layout

Quickstart

Install

Other ways to install

Run one genome

Usage

`matpredict detect`

`matpredict report genome`

`matpredict report table` and `matpredict report summary`

`matpredict detect` helper commands

`matpredict reads-type`

Batch runs on SLURM

`matpredict curate-db`

Configuration

Offline taxonomy

Tests

Validation and known limits

Authors

Citation

License

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Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
analysis		analysis
datasets/reads		datasets/reads
db		db
docs		docs
logs		logs
misc		misc
panels		panels
results		results
scripts		scripts
src/MATPredict		src/MATPredict
tests		tests
testset		testset
web		web
.dockerignore		.dockerignore
.gitignore		.gitignore
ANNOTATION_ERRORS_FIXED_REPORT.md		ANNOTATION_ERRORS_FIXED_REPORT.md
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CLAUDE.md		CLAUDE.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
environment.yml		environment.yml
pixi.lock		pixi.lock
pixi.toml		pixi.toml
pyproject.toml		pyproject.toml

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MATPredict

Contents

Project goals

Workflow

Supported lineages

Curation process

Repository layout

Quickstart

Install

Other ways to install

Run one genome

Usage

matpredict detect

matpredict report genome

matpredict report table and matpredict report summary

matpredict detect helper commands

matpredict reads-type

Batch runs on SLURM

matpredict curate-db

Configuration

Offline taxonomy

Tests

Validation and known limits

Authors

Citation

License

About

Resources

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Forks

Releases

Packages

Used by

Contributors

Languages

`matpredict detect`

`matpredict report genome`

`matpredict report table` and `matpredict report summary`

`matpredict detect` helper commands

`matpredict reads-type`

`matpredict curate-db`