feature/Sage

tests/data/params/sage_config.json

@@ -0,0 +1,42 @@
+{
+    "database": {
+        "bucket_size": 16384,
+        "fragment_min_mz": 150.0,
+        "fragment_max_mz": 1500.0,
+        "enzyme": {
+            "missed_cleavages": 1,
+            "cleave_at": "KR",
+            "restrict": "P"
+        },
+        "static_mods": {
+            "C": 57.0216
+        },
+        "decoy_tag": "",
+        "generate_decoys": false,
+        "fasta": "A.fasta"
+    },
+    "deisotope": true,
+    "chimera": false,
+    "max_fragment_charge": 1,
+    "report_psms": 1,
+    "precursor_tol": {
+        "ppm": [
+            -50,
+            50
+        ]
+    },
+    "fragment_tol": {
+        "da": [
+            -0.5,
+            0.5
+        ]
+    },
+    "isotope_errors": [
+        -1,
+        3
+    ],
+    "mzml_paths": [
+        "A.mzML"
+    ],
+    "score_type": "SageHyperScore"
+}

tests/data/params/sage_config_alternate.json

@@ -0,0 +1,42 @@
+{
+    "database": {
+        "bucket_size": 16384,
+        "fragment_min_mz": 150.0,
+        "fragment_max_mz": 1500.0,
+        "enzyme": {
+            "missed_cleavages": 1,
+            "cleave_at": "R",
+            "restrict": "P"
+        },
+        "static_mods": {
+            "C": 57.0216
+        },
+        "decoy_tag": "",
+        "generate_decoys": false,
+        "fasta": "A.fasta"
+    },
+    "deisotope": true,
+    "chimera": false,
+    "max_fragment_charge": 1,
+    "report_psms": 1,
+    "precursor_tol": {
+        "ppm": [
+            -50,
+            50
+        ]
+    },
+    "fragment_tol": {
+        "da": [
+            -0.5,
+            0.5
+        ]
+    },
+    "isotope_errors": [
+        -1,
+        3
+    ],
+    "mzml_paths": [
+        "A.mzML"
+    ],
+    "score_type": "SageHyperScore"
+}

tests/integrationtests/wrappers/test_sage.py

@@ -0,0 +1,40 @@
+"""Integration test for Sage."""
+import pandas as pd
+from pathlib import Path
+import urgap
+
+import pytest
+
+
+def test_searchdb_sage(tmp_path: Path) -> None:
+    params = urgap.UFile(uri=f"file:///{urgap._test_folder}/data/params?uftype={urgap.uftypes.proteomics.dbsearch.SAGE_PARAMS}#sage_config.json")
+    params_alt = urgap.UFile(uri=f"file:///{urgap._test_folder}/data/params?uftype={urgap.uftypes.proteomics.dbsearch.SAGE_PARAMS}#sage_config_alternate.json")
+    mzml = urgap.UFile(uri=f"file:///{urgap._test_folder}/data/ms_files?uftype={urgap.uftypes.ms.converter.mzml.ANY}#BSA1.mzML")
+    fasta = urgap.UFile(uri=f"file:///{urgap._test_folder}/data/fastas?uftype={urgap.uftypes.proteomics.FASTA}#BSA1.fasta")
+    urd = urgap.URunDict(
+        {
+            "parameters": {"Sage:0.14.7": {
+                },
+            },
+            "unode_parameters": { 
+                "storage_base_uri": f"file://{tmp_path}",
+            },
+        },
+    )
+    search_node = urgap.init_node("Sage:0.14.7")
+
+    for parameters in (params, params_alt):
+        search_result = search_node.run([parameters, mzml, fasta], urd)
+        assert search_result[0].path.exists() is True
+
+        # check that output is not empty
+        df = pd.read_csv(search_result[0].path, delimiter="\t")
+        if parameters == params:
+            assert df.shape[0] == 103
+
+        elif parameters == params_alt:
+            assert df.shape[0] == 3
+
+        search_node.remove_output_folder(output_file=search_result[0])
+
+

urgap/resources/Sage/0_14_7/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Michael Lazear
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

urgap/resources/Sage/0_14_7/README.md

@@ -0,0 +1,67 @@
+<img src="figures/logo.png" width="300">
+
+# Sage: proteomics searching so fast it seems like magic
+
+[![Rust](https://github.com/lazear/sage/actions/workflows/rust.yml/badge.svg)](https://github.com/lazear/sage/actions/workflows/rust.yml) [![Anaconda-Server Badge](https://anaconda.org/bioconda/sage-proteomics/badges/version.svg)](https://anaconda.org/bioconda/sage-proteomics)
+
+
+For more information please read [the online documentation!](https://sage-docs.vercel.app/docs)
+
+
+# Introduction
+
+Sage is, at it's core, a proteomics database search engine - 
+    a tool that transforms raw mass spectra from proteomics experiments into peptide identifications 
+    via database searching & spectral matching. 
+
+However, Sage includes a variety of advanced features that make it a one-stop shop: retention time prediction, quantification (both isobaric & LFQ), peptide-spectrum match rescoring, and FDR control. You can directly use results from Sage without needing to use other tools for these tasks.
+
+Additionally, Sage was designed with cloud computing in mind - massively parallel processing and the ability to directly stream compressed mass spectrometry data to/from AWS S3 enables unprecedented search speeds with minimal cost. 
+
+ Sage also runs just as well reading local files from your Mac/PC/Linux device!
+
+## Why use Sage instead of other tools?
+
+Sage is **simple to configure**, **powerful** and **flexible**. 
+It also happens to be well-tested, **mind-boggingly fast**, open-source (MIT-licensed) and free.
+
+## Citation
+
+If you use Sage in a scientific publication, please cite the following paper:
+
+[Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale](https://doi.org/10.1021/acs.jproteome.3c00486)
+
+
+## Features
+
+- Incredible performance out of the box
+- [Effortlessly cross-platform](https://sage-docs.vercel.app/docs/started#download-the-latest-binary-release) (Linux/MacOS/Windows), effortlessly parallel (uses all of your CPU cores)
+- [Fragment indexing strategy](https://sage-docs.vercel.app/docs/how_it_works) allows for blazing fast narrow and open searches (> 500 Da precursor tolerance)
+- [Isobaric quantification](https://sage-docs.vercel.app/docs/how_it_works#tmt-based) (MS2/MS3-TMT, or custom reporter ions)
+- [Label-free quantification](https://sage-docs.vercel.app/docs/how_it_works#label-free): consider all charge states & isotopologues *a la* FlashLFQ
+- Capable of searching for [chimeric/co-fragmenting spectra](https://sage-docs.vercel.app/docs/configuration/additional)
+- Wide-window (dynamic precursor tolerance) search mode - [enables WWA/PRM/DIA searches](https://sage-docs.vercel.app/docs/configuration/tolerance#wide-window-mode)
+- Retention time prediction models fit to each LC/MS run
+- [PSM rescoring](https://sage-docs.vercel.app/docs/how_it_works#machine-learning-for-psm-rescoring) using built-in linear discriminant analysis (LDA)
+- PEP calculation using a non-parametric model (KDE)
+- FDR calculation using target-decoy competition and picked-peptide & picked-protein approaches
+- Percolator/Mokapot [compatible output](https://sage-docs.vercel.app/docs/configuration#env)
+- Configuration by [JSON file](https://sage-docs.vercel.app/docs/configuration#file)
+- Built-in support for reading gzipped-mzML files
+- Support for reading/writing directly from [AWS S3](https://sage-docs.vercel.app/docs/configuration/aws)
+
+## Interoperability
+
+Sage is well-integrated into the open-source proteomics ecosystem. The following projects support analyzing results from Sage (typically in addition to other tools), or redistribute Sage binaries for use in their pipelines. 
+
+- [SearchGUI](http://compomics.github.io/projects/searchgui): a graphical user interface for running searches
+- [PeptideShaker](http://compomics.github.io/projects/peptide-shaker): visualize peptide-spectrum matches
+- [MS2Rescore](http://compomics.github.io/projects/ms2rescore): AI-assisted rescoring of results
+- [Picked group FDR](https://github.com/kusterlab/picked_group_fdr): scalable protein group FDR for large-scale experiments
+- [sagepy](https://github.com/theGreatHerrLebert/sagepy): Python bindings to the sage-core library
+- [quantms](https://github.com/bigbio/quantms): nextflow pipeline for running searches with Sage
+- [OpenMS](https://github.com/OpenMS/OpenMS): Sage is included as a "TOPP" tool in OpenMS
+- [sager](https://github.com/UCLouvain-CBIO/sager): R package for analyzing results from Sage searches
+- If your project supports Sage and it's not listed, please open a pull request! If you need help integrating or interfacing with Sage in some way, please reach out.
+
+Check out the (now outdated) [blog post introducing the first version of Sage](https://lazear.github.io/sage/) for more information and full benchmarks!

urgap/resources/Sage/__init__.py

@@ -0,0 +1 @@
+"""Init Sage resources."""

urgap/uftypes/proteomics.py

@@ -20,6 +20,9 @@
 proteomics.dbsearch.XTANDEM_XML = ".xtandem.xml"
 proteomics.dbsearch.DIANN_QUANT = ".diann.quant"
 proteomics.dbsearch.DIANN_REPORT = ".diann_report.tsv"
+proteomics.dbsearch.SAGE_TSV = ".sage_report.tsv"
+proteomics.dbsearch.SAGE_PARAMS = ".sage.json"
+proteomics.dbsearch.MSGFPLUS_TXT = ".msgfplus_params.txt"
 
 proteomics.diannlibrary = types.SimpleNamespace()
 proteomics.diannlibrary.ANY = "proteomics.diannlibrary.ANY"
@@ -65,3 +68,4 @@
 proteomics.qc = types.SimpleNamespace()
 proteomics.qc.ANY = "proteomics.qc.ANY"
 proteomics.qc.OFFSET_CSV = ".offset.csv"
+

urgap/unodes/sage/__init__.py

@@ -0,0 +1 @@
+"""Init Sage Unode."""

urgap/unodes/sage/sage.py

@@ -0,0 +1,106 @@
+"""Urgap sage wrapper."""
+
+import json
+
+from pathlib import Path
+
+import urgap
+
+
+class Sage(urgap.unode.UNodeBase):
+    """Sage wrapper for the Sage search engine.
+
+    Sage is a proteomics database search engine - a tool that transforms raw mass spectra from
+    proteomics experiments into peptide identifications via database searching & spectral matching.
+    """
+
+    META_INFO = {
+        "name": "Sage",
+        "wrapper_version": {"major": 1, "minor": 0, "patch": 0},
+        "versions": [
+            {"version": "0.14.7", "exe_path": "Sage/0_14_7/sage"},
+        ],
+        "parameters_not_triggering_rerun": [],
+        "input_uftypes": {
+            urgap.uftypes.ms.converter.mzml.ANY: {"min": 1, "max": -1},
+            urgap.uftypes.proteomics.dbsearch.SAGE_PARAMS: {"min": 1, "max": 1},
+            urgap.uftypes.proteomics.FASTA: {"min": 1, "max": 1},
+        },
+        "output_uftypes": {
+            urgap.uftypes.proteomics.dbsearch.SAGE_TSV: {"min": 1, "max": 1},
+
+        },
+        "engine": None,
+        "engine_type": ("db_search", "proteomics"),
+        "citation": """Lazear, M. R. (2023). Sage: An open-source tool for fast proteomics searching and quantification at scale. Journal of Proteome Research, 22(11), 3652-3659. https://doi.org/10.1021/acs.jproteome.3c00486""",
+    }
+
+    def __init__(self, *args: object, **kwargs: object) -> None:
+        """Initialize Sage class."""
+        super().__init__(*args, **kwargs)
+
+    def preflight(self, utrace: urgap.UTrace) -> urgap.UTrace:
+        """Preflight routine for Sage wrapper.
+
+        During preflight,
+            - parameters are formatted
+            - mods are mapped and formatted
+
+        Args:
+            utrace: Combination of urun_dict, ufile_list and unode.meta.
+
+        Returns:
+            UTrace object, combination of urun_dict, ufile_list and unode.meta.
+
+        Raises:
+            ValueError: If both a parameter file and command-line parameters
+                in the urun_dict are provided. Please provide only one.
+        """
+        param_files = utrace.input_files.get_path_objects_by_uftype(
+            urgap.uftypes.proteomics.dbsearch.SAGE_PARAMS,
+        )
+
+        mzml_file = utrace.input_files.get_path_objects_by_uftype(
+            urgap.uftypes.ms.converter.mzml.ANY,
+        )[0]
+        fasta_file = utrace.input_files.get_path_objects_by_uftype(
+            urgap.uftypes.proteomics.FASTA,
+        )[0]
+
+        config_dict = json.loads(param_files[0].read_text())
+        config_dict["output_directory"] = str(mzml_file.parent)
+
+        config_path = param_files[0].parent / "sage_config.json"
+        config_path.write_text(json.dumps(config_dict))
+        utrace.urun_dict.command_list = [
+            str(self.exe_path),
+            str(config_path),
+            "--fasta",
+            str(fasta_file),
+            str(mzml_file),
+
+
+        ]
+        return utrace
+
+
+
+    def postflight(self, utrace: urgap.UTrace) -> urgap.UTrace:
+        """Postflight routine for Sage wrapper.
+
+        During postflight the Sage native .tsv output file is converted into the
+        pre-defined urgap output file, which is of csv format.
+
+        Args:
+            utrace: Combination of urun_dict, ufile_list and unode.meta.
+
+        Returns:
+            UTrace object, combination of urun_dict, ufile_list and unode.meta.
+        """
+        full_path = utrace.input_files.get_path_objects_by_uftype(
+            urgap.uftypes.ms.converter.mzml.ANY,
+        )[0]
+        sage_tsv = full_path.parent / "results.sage.tsv"
+        self.tmp_files.append(sage_tsv)
+        Path(sage_tsv).rename(utrace.output_files[0].path)
+        return utrace