Sage: proteomics searching so fast it seems like magic

Sage is a proteomics search engine - a tool that transforms raw mass spectra from proteomics experiments into peptide identificatons via database searching & spectral matching. But, it's also more than just a search engine - 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.

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)

Let's not forget to mention that it is incredibly fast, sensitive, 100% free, and open source!

Check out the blog post introducing Sage for more information and full benchmarks!

Features

Incredible performance out of the box
Effortlessly cross-platform (Linux/MacOS/Windows), effortlessly parallel (uses all of your CPU cores)
Fragment indexing strategy allows for blazing fast narrow and open searches (> 500 Da precursor tolerance)
MS3-TMT quantification (R-squared of 0.999 with Proteome Discoverer)
Capable of searching for chimeric/co-fragmenting spectra
Retention time prediction models fit to each LC/MS run
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
Configuration by JSON file
Built-in support for reading gzipped-mzML files
Support for reading/writing directly from AWS S3

Experimental features

Label-free quantification: consider all charge states & isotopologues a la FlashLFQ

Assign multiple peptides to complex spectra

When chimeric searching is turned on, 2 peptide identifications will be reported for each MS2 scan, both with rank=1

Sage trains machine learning models for FDR refinement and posterior error probability calculation

Retention times are globally aligned across runs
Boosts PSM identifications using prediction of retention times with a linear regression model
Hand-rolled, 100% pure Rust implementations of Linear Discriminant Analysis and KDE-mixture models for refinement of false discovery rates
Models demonstrate 1:1 results with scikit-learn, but have increased performance
No need for a second post-search pipeline step

Installation

Sage is distributed as source code, and as a standalone executable file.

Installing via conda

Sage can be installed from bioconda:

$ conda install -c bioconda -c conda-forge sage-proteomics
$ sage --help

Compiling the development version

Install the Rust programming language compiler
Download Sage source code via git: git clone https://github.com/lazear/sage.git or by zip file
Compile: cargo build --release
Run: ./target/release/sage config.json

Once you have Rust installed, you can copy and paste the following lines into your terminal to complete the above instructions, and run Sage on the example mzML provided in the repository (a single scan from PXD016766)

git clone https://github.com/lazear/sage.git
cd sage
cargo run --release tests/config.json

Downloading the latest release

Visit the Releases website.
Download the correct pre-compiled binary for your operating system.
Run: sage <path/to/config.json>

Interfacing with AWS S3

Sage is capable of natively reading & writing files to AWS S3:

S3 paths should be specified as s3://bucket/prefix/key.mzML.gz or s3://bucket/prefix for output folder
See AWS docs for configuring your credentials
Using S3 may incur data transfer charges as well as multi-part upload request charges.

Usage

Usage: sage [OPTIONS] <parameters> [mzml_paths]...

🔮 Sage 🧙 - Proteomics searching so fast it feels like magic!

Arguments:
  <parameters>     Path to configuration parameters (JSON file)
  [mzml_paths]...  Paths to mzML files to process. Overrides mzML files listed in the configuration file.

Options:
  -f, --fasta <fasta>
          Path to FASTA database. Overrides the FASTA file specified in the configuration file.
  -o, --output_directory <output_directory>
          Path where search and quant results will be written. Overrides the directory specified in the configuration file.
      --batch-size <batch-size>
          Number of files to search in parallel (default = number of CPUs/2)
      --write-pin
          Write percolator-compatible `.pin` output files
  -h, --help
          Print help information
  -V, --version
          Print version information

Sage is called from the command line using and requires a path to a JSON-encoded parameter file as an argument (see below).

Example usage: sage config.json

Some options in the parameters file can be over-written using the command line interface. These are:

The paths to the raw mzML data
The path to the database (fasta file)
The output directory

For example:

# Specify fasta and output dir:
sage -f proteins.fasta -o output_directory config.json

# Specify mzML files:
sage -f proteins.fasta config.json *.mzML

# Specify mzML file located in an S3 bucket
sage config.json s3://my-bucket/YYYY-MM-DD_expt_A_fraction_1.mzML.gz

Running Sage will produce several output files (located in either the current directory, or output_directory if that option is specified):

Record of search parameters (results.json) will be created that details input/output paths and all search parameters used for the search
MS2 search results will be stored as a tab-separated file (results.sage.tsv) file - this is a tab-separated file, which can be opened in Excel/Pandas/etc
MS2 and MS3 quantitation results will be stored as a tab-separated file (tmt.tsv, lfq.tsv) if quant.tmt or quant.lfq options are used in the parameter file

Configuration file schema

Notes

The majority of parameters are optional - only "database.fasta", "precursor_tol", and "fragment_tol" are required. Sage will try and use reasonable defaults for any parameters not supplied
Tolerances are specified on the experimental m/z values. To perform a -100 to +500 Da open search (mass window applied to precursor), you would use "da": [-500, 100]

Decoys

Using decoy sequences is critical to controlling the false discovery rate in proteomics experiments. Sage can use decoy sequences in the supplied FASTA file, or it can generate internal sequences. Sage reverses tryptic peptides (not proteins), so that the picked-peptide approach to FDR can be used.

If database.generate_decoys is set to true (or unspecified), then decoy sequences in the FASTA database matching database.decoy_tag will be ignored, and Sage will internally generate decoys. It is critical that you ensure you use the proper decoy_tag if you are using a FASTA database containing decoys and have internal decoy generation turned on - otherwise Sage will treat the supplied decoys as hits!

Internally generated decoys will have protein accessions matching "{decoy_tag}{accession}", e.g. if decoy_tag is "rev_" then a protein accession like "rev_sp|P01234|HUMAN" will be listed in the output file.

FASTA digestion

Sage will process a protein into peptides via several routes listed below. Currently, one and only one is supported.

Enzymatic: database.enzyme.cleave_at = "KR" - configuration option set to a sequence of amino acids (e.g. "KR" for trypsin, "FWYL" for chymotrypsin)
Non-enzymatic: database.enzyme.cleave_at = "" - All potential peptides between min_len and max_len will be generated from the sequence
No digestion: database.enzyme.cleave_at = "$" - FASTA entries will be used as-is, subject to min_len and max_len options

Example configuration file

For additional information about configuration options and output file formats, please see DOCS.md

// Note that json does not allow comments, they are here just as explanation
// but need to be removed in a real config.json file
{
  "database": {
    "bucket_size": 32768,           // How many fragments are in each internal mass bucket
    "enzyme": {               // Optional. Default is trypsin, using the parameters below
      "missed_cleavages": 2,  // Optional[int], Number of missed cleavages for tryptic digest
      "min_len": 5,           // Optional[int] {default=5}, Minimum AA length of peptides to search
      "max_len": 50,          // Optional[int] {default=50}, Maximum AA length of peptides to search
      "cleave_at": "KR",      // Optional[str] {default='KR'}. Amino acids to cleave at
      "restrict": "P",        // Optional[char/single AA] {default='P'}. Do not cleave if this AA follows the cleavage site
      "c_terminal": true      // Optional[bool] {default=true}. Cleave at c terminus of matching amino acid
    },
    "fragment_min_mz": 200.0,       // Optional[float] {default=150.0}, Minimum mass of fragments to search
    "fragment_max_mz": 2000.0,      // Optional[float] {default=2000.0}, Maximum mass of fragments to search 
    "peptide_min_mass": 500.0,      // Optional[float] {default=500.0}, Minimum monoisotopic mass of peptides to fragment
    "peptide_max_mass": 5000.0,     // Optional[float] {default=5000.0}, Maximum monoisotopic mass of peptides to fragment
    "ion_kinds": ["b", "y"],        // Optional[List[str]] {default=["b","y"]} Which fragment ions to generate and search?
    "min_ion_index": 2,     // Optional[int] {default=2}, Do not generate b1/b2/y1/y2 ions for preliminary searching. Does not affect full scoring of PSMs
    "static_mods": {        // Optional[Dict[char, float]] {default={}}, static modifications
      "^": 304.207,         // Apply static modification to N-terminus of peptide
      "K": 304.207,         // Apply static modification to lysine
      "C": 57.0215          // Apply static modification to cysteine
    },
    "variable_mods": {    // Optional[Dict[char, float]] {default={}}, variable modifications
      "M": [15.9949],     // Variable mods are applied *before* static mod
      "^Q": [-17.026549],
      "^E": [-18.010565], // Applied to N-terminal glutamic acid
      "$": [49.2, 22.9],  // Applied to peptide C-terminus
      "[": 42.0,          // Applied to protein N-terminus
      "]": 111.0          // Applied to protein C-terminus
    }
    "max_variable_mods": 2, // Optional[int] {default=2} Limit k-combinations of variable modifications
    "decoy_tag": "rev_",    // Optional[str] {default="rev_"}: See notes above
    "generate_decoys": false, // Optional[bool] {default="true"}: Ignore decoys in FASTA database matching `decoy_tag`
    "fasta": "dual.fasta"   // str: mandatory path to FASTA file
  },
  "quant": {                // Optional - specify only if TMT or LFQ
    "tmt": "Tmt16",         // Optional[str] {default=null}, one of "Tmt6", "Tmt10", "Tmt11", "Tmt16", or "Tmt18"
    "tmt_settings": {
      "level": 3,           // Optional[int] {default=3}, MS-level to perform TMT quantification on
      "sn": false           // Optional[bool] {default=false}, use Signal/Noise instead of intensity for TMT quant. Requires noise values in mzML
    },
    "lfq": true,            // Optional[bool] {default=null}, perform label-free quantification
    "lfq_settings": {
      "peak_scoring": "Hybrid", // See DOCS.md for details - recommend that you do not change this setting
      "integration": "Sum",   // Optional["Sum" | "Apex"], use sum of MS1 traces in peak, or MS1 intensity at peak apex
      "spectral_angle": 0.7,  // Optional[float] {default = 0.7}, normalized spectral angle cutoff for calling an MS1 peak
      "ppm_tolerance": 5.0    // Optional[float] {default = 5.0}, tolerance (in p.p.m.) for DICE window around calculated precursor mass
    }
  },
  "precursor_tol": {        // Tolerance can be either "ppm" or "da"
    "da": [
      -500,                 // This value is substracted from the experimental precursor to match theoretical peptides
      100                   // This value is added to the experimental precursor to match theoretical peptides
    ]
  },
  "fragment_tol": {         // Tolerance can be either "ppm" or "da"
    "ppm": [
     -10,                   // This value is subtracted from the experimental fragment to match theoretical fragments 
     10                     // This value is added to the experimental fragment to match theoretical fragments 
    ]
  },
  "isotope_errors": [       // Optional[Tuple[int, int]] {default=[0,0]}: C13 isotopic envelope to consider for precursor
    -1,                     // Consider -1 C13 isotope
    3                       // Consider up to +3 C13 isotope (-1/0/1/2/3) 
  ],
  "deisotope": false,       // Optional[bool] {default=false}: perform deisotoping and charge state deconvolution
  "chimera": false,         // Optional[bool] {default=false}: search for chimeric/co-fragmenting PSMS
  "wide_window": false,     // Optional[bool] {default=false}: _ignore_ `precursor_tol` and search in wide-window/DIA mode
  "predict_rt": false,    // Optional[bool] {default=true}: use retention time prediction model as an feature for LDA
  "min_peaks": 15,          // Optional[int] {default=15}: only process MS2 spectra with at least N peaks
  "max_peaks": 150,         // Optional[int] {default=150}: take the top N most intense MS2 peaks to search,
  "min_matched_peaks": 6,   // Optional[int] {default=4}: minimum # of matched b+y ions to use for reporting PSMs
  "max_fragment_charge": 1, // Optional[int] {default=null}: maximum fragment ion charge states to consider,
  "report_psms": 1,         // Optional[int] {default=1}: number of PSMs to report for each spectra. Higher values might disrupt PSM rescoring.
  "output_directory": "s3://bucket/prefix" // Optional[str] {default=`.`}: Place output files in a given directory or S3 bucket/prefix
  "mzml_paths": [           // List[str]: representing paths to mzML (or gzipped-mzML) files for search
    "local/path.mzML",
    "s3://bucket/PXD0000001/foo.mzML.gz"
  ]       
}

Using the docker image

Sage can be used from a docker image!

$ docker pull ghcr.io/lazear/sage:master
$ docker run -it --rm -v ${PWD}:/data ghcr.io/lazear/sage:master sage -o /data /data/config.json
# The sage executable is located in /app/sage in the image

-v ${PWD}:/data means it will mount your current directory as /data in the docker image. Make sure all the paths in your command and configuration use the location in the image and not your local directory

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