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Merge pull request #7919 from TomHarrop/add-tiberius
Add tiberius
2 parents 7d30fcc + 44cd6ed commit 2418513

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‎tools/tiberius/.shed.yml‎

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---
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categories: [Genome annotation]
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description: Tiberius is a tool for deep learning-based ab initio gene structure prediction
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homepage_url: https://github.com/Gaius-Augustus/Tiberius
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long_description: Tiberius is a tool for deep learning-based ab initio gene structure prediction
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name: tiberius
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owner: iuc
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remote_repository_url: https://github.com/galaxyproject/tools-iuc/tree/main/tools/tiberius
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type: unrestricted

‎tools/tiberius/macros.xml‎

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<macros>
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<token name="@VERSION@">2.0.3</token>
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<xml name="requirements">
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<requirements>
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<container type="docker">larsgabriel23/tiberius:@VERSION@</container>
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</requirements>
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</xml>
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<xml name="xrefs">
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<xrefs>
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<xref type="bio.tools">tiberius</xref>
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</xrefs>
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</xml>
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<xml name="citation">
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<citations>
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<citation type="doi">10.1093/bioinformatics/btae685</citation>
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</citations>
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</xml>
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</macros>

‎tools/tiberius/test-data/KV860338_truncated.fasta.gz‎

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‎tools/tiberius/test-data/tiberius_models.loc‎

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../tool-data/tiberius_models.loc.sample

‎tools/tiberius/tiberius.xml‎

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<tool id="tiberius" name="Tiberius" version="@VERSION@+galaxy0" profile="24.1">
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<description>deep learning-based ab initio gene structure prediction</description>
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<macros>
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<import>macros.xml</import>
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</macros>
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<expand macro="xrefs"/>
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<expand macro="requirements"/>
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<command detect_errors="exit_code"><![CDATA[
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ln -s '$genome' ./genome.fa &&
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TF_NUM_INTRAOP_THREADS="\${GALAXY_SLOTS}"
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tiberius.py
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--genome genome.fa
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--model_cfg '${model_cfg.fields.model_cfg}'
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--out output.gtf
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--batch_size '$batch_size'
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#if $model_cfg.fields.softmasking == 'False':
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--no_softmasking
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#end if
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]]></command>
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<inputs>
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<param argument="--genome" type="data" format="fasta" label="Genome sequence file"/>
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<conditional name="masking">
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<param name="softmasked" type="select" label="Has your genome been soft-masked?" help="The Tiberius developers recommend using unmasked models. Some models that were trained on soft-masked genomes are avaialable, but these are only appropriate for finding genes in genomes that were soft-masked in the same way as the training data.">
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<option value="unmasked" selected="true">No</option>
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<option value="softmasked">Yes</option>
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</param>
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<when value="unmasked">
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<param argument="--model_cfg" type="select" label="Model">
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<options from_data_table="tiberius_models">
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<filter type="static_value" value="False" column="4"/>
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<filter type="static_value" column="1" value="@VERSION@"/>
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<filter type="sort_by" column="7"/>
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</options>
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</param>
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</when>
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<when value="softmasked">
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<param argument="--model_cfg" type="select" label="Model">
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<options from_data_table="tiberius_models">
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<filter type="static_value" column="1" value="@VERSION@"/>
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<filter type="sort_by" column="7"/>
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</options>
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</param>
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</when>
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</conditional>
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<param argument="--batch_size" type="hidden" value="16"/>
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</inputs>
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<outputs>
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<data format="gtf" name="out_gtf" label="${tool.name} annotation with model ${model_cfg.fields.model_cfg}" from_work_dir="output.gtf"/>
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</outputs>
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<tests>
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<!-- test 1 -->
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<test expect_failure="true" maxseconds="15">
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<param name="genome" value="KV860338_truncated.fasta.gz"/>
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<conditional name="masking">
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<param name="model_cfg" value="Vertebrata version 1.1.7"/>
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</conditional>
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<param name="batch_size" value="2"/>
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<assert_command>
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<has_text text="no_softmasking"/>
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</assert_command>
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</test>
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<!-- test 2 -->
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<!-- <test expect_failure="true" maxseconds="30">
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<param name="genome" value="KV860338_truncated.fasta.gz"/>
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<conditional name="masking">
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<param name="softmasked" value="softmasked"/>
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<param name="model_cfg" value="Mammalia version 1.1.5 (Soft-masked)"/>
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</conditional>
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<param name="batch_size" value="2"/>
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<assert_command>
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<not_has_text text="no_softmasking"/>
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</assert_command>
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</test> -->
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</tests>
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<help><![CDATA[
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`Tiberius <https://github.com/Gaius-Augustus/Tiberius>`__ is a
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deep learning-based ab initio gene structure prediction tool.
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It predicts genes from genomic sequence only.
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To run Tiberius, you need to provide a FASTA file containing
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the genomic sequences and choose a pre-trained model.
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More information about the models is available on the
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Tiberius GitHub
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`page <https://github.com/Gaius-Augustus/Tiberius/tree/main/model_cfg>`__
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Tiberius end-to-end integrates convolutional and
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long short-term memory layers with a differentiable HMM layer.
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It can be used to predict gene structures from genomic
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sequences only (ab initio), while matching the accuracy of
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tools that use extrinsic evidence.
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]]></help>
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<expand macro="citation"/>
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</tool>

‎tools/tiberius/tool-data/tiberius_models.loc.sample‎

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#clamsa container_version date model_cfg softmasking target_species tiberius_version value
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False 2.0.3 2025-11-10 vertebrates.yaml False Vertebrata 1.1.7 Vertebrata version 1.1.7
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False 2.0.3 2025-05-12 mammalia_softmasking_v2.yaml True Mammalia 1.1.5 Mammalia version 1.1.5 (Soft-masked)
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False 2.0.3 2025-05-12 mammalia_nosofttmasking_v2.yaml False Mammalia 1.1.5 Mammalia version 1.1.5
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True 2.0.3 2025-05-12 mammalia_clamsa_v2.yaml True Mammalia 1.1.5 Mammalia version 1.1.5 (Soft-masked, ClaMSA)
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False 2.0.3 2026-02-15 insecta.yaml False Insecta 1.1.7 Insecta version 1.1.7
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False 2.0.3 2026-02-06 fungi.yaml False Fungi 1.1.7 Fungi version 1.1.7
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False 2.0.3 2026-02-06 diatoms.yaml False Bacillariophyta 1.1.8 Bacillariophyta version 1.1.8
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False 2.0.3 2026-01-26 chlorophyta.yaml False Chlorophyta 1.1.7 Chlorophyta version 1.1.7
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False 2.0.3 2025-04-15 angiosperms.yaml False Mesangiospermae 1.1.6 Mesangiospermae version 1.1.6

‎tools/tiberius/tool_data_table_conf.xml.sample‎

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<tables>
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<table name="tiberius_models" comment_char="#">
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<columns>clamsa, container_version, date, model_cfg, softmasking, target_species, tiberius_version, value</columns>
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<file path="tool-data/tiberius_models.loc" />
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</table>
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</tables>

‎tools/tiberius/tool_data_table_conf.xml.test‎

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<tables>
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<table name="tiberius_models" comment_char="#">
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<columns>clamsa, container_version, date, model_cfg, softmasking, target_species, tiberius_version, value</columns>
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<file path="${__HERE__}/test-data/tiberius_models.loc" />
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</table>
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</tables>

‎tools/tiberius/update_loc_file.py‎

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#!/usr/bin/env python3
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import argparse
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import sys
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import tempfile
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import xml.etree.ElementTree as ET
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from csv import DictWriter
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from pathlib import Path
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from shutil import which
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from subprocess import PIPE, Popen
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from yaml import safe_load
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_TIBERIUS_REPO = "https://github.com/Gaius-Augustus/Tiberius"
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_YAML_KEYS = ["target_species", "tiberius_version", "date", "softmasking", "clamsa"]
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_LOC_FILE_PATH = Path("tool-data/tiberius_models.loc.sample")
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def get_model_dict(yaml_file):
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with open(yaml_file, "rt") as f:
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model_config = safe_load(f)
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model_dict = {k: model_config.get(k) for k in _YAML_KEYS}
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model_dict["model_cfg"] = yaml_file.name
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return model_dict
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def get_version(macros_xml):
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tree = ET.parse(macros_xml)
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for token in tree.findall("token"):
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if token.attrib["name"] == "@VERSION@":
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return str(token.text)
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raise ValueError("No token element with name @VERSION@ found")
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def pull_repo(tool_version):
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git = which("git")
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tempdir = tempfile.mkdtemp()
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# pull the tag
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pull_command = [
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Path(git),
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"clone",
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"--single-branch",
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"--branch",
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f"v{tool_version}",
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_TIBERIUS_REPO,
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tempdir,
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]
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with Popen(pull_command, stdout=PIPE) as proc:
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print(proc.stdout.read(), sys.stdout)
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return tempdir
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument("macros_xml", help="macros.xml file", type=Path)
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return parser.parse_args()
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def make_display_name(model_dict):
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base_name = (
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model_dict["target_species"] + f' version {model_dict["tiberius_version"]}'
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)
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model_type = [
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"Soft-masked" if model_dict["softmasking"] else None,
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"ClaMSA" if model_dict["clamsa"] else None,
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]
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model_type = [x for x in model_type if x is not None]
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if model_type:
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return base_name + f' ({", ".join(model_type)})'
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return base_name
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def main():
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args = parse_arguments()
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tool_version = get_version(args.macros_xml)
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tool_repo = pull_repo(tool_version)
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yaml_files = Path(tool_repo, "model_cfg").glob("*.yaml")
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model_dicts = [get_model_dict(x) for x in yaml_files]
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for model_dict in model_dicts:
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model_dict["value"] = make_display_name(model_dict)
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model_dict["container_version"] = tool_version
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with open(_LOC_FILE_PATH, "wt") as f:
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loc_file_keys = sorted(set().union(*(d.keys() for d in model_dicts)))
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header = "\t".join(loc_file_keys)
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f.write(f"#{header}\n")
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fc = DictWriter(f, fieldnames=loc_file_keys, delimiter="\t")
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fc.writerows(model_dicts)
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if __name__ == "__main__":
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main()

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