Metadata-Version: 2.1
Name: pggt
Version: 1.2.0
Summary: Graph-to-allele, CNV and PAV workflow for multi-genome analysis
Author: PGGT developers
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Natural Language :: Chinese (Simplified)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: psutil >=5.9

# PGGT

`PGGT` is a recoverable multi-genome workflow that converts pangenome graph
projections into allele, copy-number variation (CNV), and presence/absence
variation (PAV) components.

It runs five modules:

1. project annotated genes onto GFA walks and build initial components;
2. filter graph-supported components with DIAMOND and gene structure evidence;
3. score every gene and mark low-score genes as noncoding;
4. regroup singleton genes with reciprocal Miniprot and Pfam evidence;
5. select representative genes and write allele/CNV/PAV component tables and
   W-only GFA files.

Genes with a total module-3 score below `1.0` remain in the main component
table. Their output IDs use `ng` instead of the terminal gene marker `g`, for
example `B73_C05g06442` becomes `B73_C05ng06442`. The original-to-output ID
relationship is recorded in `13.gene_id_mapping.tsv` and propagated through
modules 4 and 5.

## What's new in 1.2.0

- Module 3 scores every gene in every component class using CDS, exon, Pfam,
  and expression evidence.
- Pfam best hits are selected by the smallest independent E-value, then the
  largest HMM coverage and domain name as deterministic tie-breakers.
- `--previous-pfam-results` reuses compatible Pfam calls from an earlier
  `07.pfam_scores.tsv` or PGGT result directory. Reuse is validated by the
  Pfam HMM fingerprint and protein sequence SHA256; unmatched sequences are
  scanned normally.
- Low-score gene IDs are marked with `ng` in module 3 and remain marked in
  `04.component_cnv` and `05.allele_CNV_PAV` outputs.

## Installation

PGGT requires Linux and Python 3.9 or later.

```bash
pip install pggt
```

The workflow also calls external command-line programs that are not installed
by pip:

- `bedtools`
- `diamond`
- `samtools`
- HMMER (`hmmscan` and `hmmpress`)
- `miniprot`

For example, these programs can be prepared with Conda/Bioconda:

```bash
conda create -n pggt -c conda-forge -c bioconda \
  python=3.10 bedtools diamond samtools hmmer miniprot
conda activate pggt
pip install pggt
```

Confirm that the command is available:

```bash
pggt --help
```

## Quick start

Run input validation first. `--dry-run` creates no result files.

```bash
pggt \
  --gfa-input pangenome.gfa \
  --genome-dir genomes/ \
  --gff-dir annotations/ \
  --samples species_list.txt \
  --expression-dir expression/ \
  --pfam-hmm Pfam-A.hmm \
  --output-dir pggt_out/ \
  --threads 56 \
  --parallel-jobs 4 \
  --dry-run
```

After validation succeeds, run the same command without `--dry-run`.

To reuse Pfam work when rerunning the same species and annotations, add either
an earlier PGGT result directory or its compact Pfam table:

```bash
pggt [all required arguments] \
  --previous-pfam-results previous_pggt_out/
```

The previous and current runs must use a Pfam database with the same
fingerprint. Reuse is sequence-based, so proteins absent from the previous
table or with changed SHA256 values are scanned automatically.

PGGT 1.1 tables do not retain the statistics for every accepted hit. To avoid
carrying the 1.1 best-hit direction bug into 1.2, unambiguous no-domain and
single-domain legacy rows are reusable; legacy multi-domain rows use an exact
shared-cache record when available and otherwise are rescanned. Tables created
by PGGT 1.2 retain the corrected best hit and are fully reusable.

The output directory must be new. Do not use a GraphTribe result directory as
the PGGT output directory.

## Input requirements

### Sample list

`species_list.txt` must contain exactly one sample name per non-empty line
and at least two unique samples:

```text
B73
EIL02
EIL31
EIL08
EIL37
```

Sample names may contain letters, numbers, underscores, dots, and hyphens. The
first sample is the reference sample used for representative-gene selection.
Every listed sample must occur in the second field of at least one GFA `W`
record.

The same sample names are used to resolve genome, GFF, and expression files.
Each sample must resolve to exactly one file of each type.

### GFA

`--gfa-input` accepts either one GFA file or a directory containing exactly
one `*.gfa` file. Use `--gfa-name FILE` when a directory contains multiple
GFA files.

GFA `W` coordinates are interpreted as 0-based, half-open intervals.

### Genome FASTA files

Provide one uncompressed genome FASTA per sample. Recommended names are:

```text
genomes/B73.fa
genomes/EIL02.fa
```

PGGT first checks `<sample>.fa`, `<sample>.fasta`, `<sample>.fna`, and
`<sample>.fas`. If none exists, it accepts one uniquely matching FASTA whose
name contains the sample name.

FASTA sequence names must match the chromosome or sequence names used by the
corresponding GFF and GFA walks.

### GFF/GFF3 files

Recommended names are:

```text
annotations/B73.renamed.sorted.chr.gff3
annotations/EIL02.renamed.sorted.chr.gff3
```

PGGT also checks `<sample>.gff3` and `<sample>.gff`, followed by one unique
matching GFF file whose name contains the sample name.

The annotations must provide gene, transcript, exon, and CDS relationships.
Gene IDs must be consistent with the expression tables and use the supported
terminal `g` plus digits pattern, such as `B73_C05g06442`; this allows PGGT to
mark a noncoding output ID as `B73_C05ng06442` without ambiguity. Chromosome
names must match the corresponding genome FASTA.

### Expression tables

Expression input is required for every sample. Each file must be a
tab-separated TSV table. The recommended file name is
`<sample>.FPKM.tsv`; `<sample>.expression.tsv` is also accepted.

Example:

```text
Gene_ID	leaf_FPKM	root_FPKM
B73_C01g00001	12.50	3.20
B73_C01g00002	0	0.08
B73_C01g00003	0	0
```

Expression-table rules:

- The header must contain the exact, case-sensitive column name `Gene_ID`.
- At least one expression column must end exactly with `_FPKM`.
- Gene IDs must match gene IDs in the corresponding GFF/GFF3.
- FPKM cells must be numeric. Do not write `NA`, `N/A`, or other text.
- Empty FPKM cells are interpreted as zero.
- When multiple `*_FPKM` columns are present, PGGT uses the maximum value for
  each gene.
- Use one row per gene. If a gene ID occurs more than once, the later row
  replaces the earlier value.
- Genes absent from the expression table receive the `no_expression` score.

Extra non-FPKM columns are allowed. PGGT first checks
`<sample>.FPKM.tsv` and `<sample>.expression.tsv`; otherwise, exactly one
`*<sample>*.tsv` file must exist in the expression directory. Ambiguous
matches stop the run.

### Pfam database

`--pfam-hmm` must point to a non-empty `Pfam-A.hmm` file. PGGT prepares the
required HMMER index files in its working area when necessary.

## Run control

```bash
# Resume a previously interrupted PGGT run
pggt [all original arguments] --resume

# Run only one module
pggt [all required arguments] --only-step 3

# Run a module range
pggt [all required arguments] --from-step 2 --to-step 4
```

Use the same inputs, output directory, thresholds, thread count, and job count
when resuming. Completed modules are validated through their completion
markers before being skipped.

## Threads and shared caches

`--threads` is the total CPU budget. `--parallel-jobs` controls concurrent
per-sample work, Pfam shards, Miniprot mappings, and PAF parsing, and must not
exceed `--threads`.

For five samples on a sufficiently large 56-core node, `--parallel-jobs 5`
allows all five Miniprot target mappings to run concurrently. Values above the
sample count can still increase Pfam shard concurrency, but should be
benchmarked against available memory and storage bandwidth.

Shared caches are stored under `~/.cache/pggt`:

- `~/.cache/pggt/pfam/pfam_cache.sqlite3` caches Pfam results by HMM
  fingerprint and protein sequence SHA256;
- `~/.cache/pggt/miniprot/` stores Miniprot genome indexes.

Set the `PGGT_CACHE_DIR` environment variable to use a different cache root.

## Main outputs

```text
pggt_out/
├── 01.graph_mapping/
├── 02.component_filter/
│   └── 10.filtered_component_table.txt
├── 03.gene_score/
│   ├── 11.scored_gene_component_table.txt
│   ├── 12.nocoding_gene_scores.tsv
│   ├── 13.gene_id_mapping.tsv
│   └── score_tmp_file/
│       ├── 07.pfam_scores.tsv
│       └── 08.singleton_score.tsv
├── 04.component_cnv/
│   └── 04.orthogroup/
│       └── singleton_orthogroup_component_table.txt
├── 05.allele_CNV_PAV/
│   ├── allele_CNV_PAV_component_table.txt
│   └── W_gfa/
│       └── allele_CNV_PAV.W.gfa
└── 00.run_info/
    └── module_timings.tsv
```

Each completed module contains `artifact_manifest.tsv`. Large intermediate
DIAMOND, Pfam, and Miniprot files are not published into successful result
directories. Failed module workspaces remain under `OUTPUT_DIR/.work/` for
diagnosis.

## Standalone module 5

When a compatible module-4 component table already exists, run module 5
directly:

```bash
pggt-module5 \
  --component-table singleton_orthogroup_component_table.txt \
  --samples-file species_list.txt \
  --gff-dir annotations/ \
  --gene-id-map 13.gene_id_mapping.tsv \
  --output-dir module5_out/ \
  --representative-seed 1
```

`--gene-id-map` is required when the component table contains module-3 `ng`
IDs but the source GFF still contains the original `g` IDs.

## Command overview

```bash
pggt --help
pggt-module5 --help
```

## Maintainer notes

Before a PyPI release, run the regression tests, build both the wheel and
source distribution, run `twine check dist/*`, and verify installation in a
clean environment. Update this README whenever command-line options or input
requirements change.

Major workflow changes must be developed in a new versioned package directory,
with matching updates to `pyproject.toml`, runtime `__version__`, this
README, `PGGT流程原理.md`, and `CHANGELOG.md`. Do not modify a previously
published version directory in place.

## License

License information will be added before the first public release.
