Skip to content

fasta_lake.search#

fasta_lake.search #

Sage DDA settings: cleavage and precursor mass window are independent choices.

Upstream search engine: https://github.com/lazear/sage; Lazear (2023), https://doi.org/10.1021/acs.jproteome.3c00486. FastaLake writes explicit search settings; Sage performs spectrum matching, scoring and its native confidence estimation. Search q-values are not whole adaptive-workflow FDR calibration.

sage_config #

sage_config(fasta, mzml, output, *, digestion='tryptic', search_mode='closed', min_length=5, max_length=50)

Build the complete configuration for one Sage v0.14.7 acquisition.

Parameters:

Name Type Description Default
fasta path - like

Selected target database, one measured acquisition, and its new search directory. Sage generates decoys; input FASTA must contain targets only.

 required
mzml path - like

Selected target database, one measured acquisition, and its new search directory. Sage generates decoys; input FASTA must contain targets only.

 required
output path - like

Selected target database, one measured acquisition, and its new search directory. Sage generates decoys; input FASTA must contain targets only.

 required
digestion (tryptic, semi - tryptic, unspecific)

Require two, one, or no tryptic termini. Protein termini are valid enzymatic boundaries. Permissiveness does not establish protease activity.

 "tryptic"
search_mode (closed, open)

Closed uses ±20 ppm. Open uses [-500, +100] Da, no variable modifications, no isotope-error expansion, and no RT prediction.

 "closed"
min_length int

Search peptide lengths, independent of the de novo evidence filter.

 5
max_length int

Search peptide lengths, independent of the de novo evidence filter.

 5

Returns:

Type Description
dict

A fresh JSON-compatible config. LFQ and target/decoy generation are retained. Every actual search saves these settings in its JSON.
Source code in fasta_lake/search.py 
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
def sage_config(
    fasta, mzml, output, *, digestion="tryptic", search_mode="closed", min_length=5, max_length=50
):
    """Build the complete configuration for one Sage v0.14.7 acquisition.

    Parameters
    ----------
    fasta, mzml, output : path-like
        Selected target database, one measured acquisition, and its new search
        directory. Sage generates decoys; input FASTA must contain targets only.
    digestion : {"tryptic", "semi-tryptic", "unspecific"}
        Require two, one, or no tryptic termini. Protein termini are valid
        enzymatic boundaries. Permissiveness does not establish protease activity.
    search_mode : {"closed", "open"}
        Closed uses +/-20 ppm. Open uses [-500, +100] Da, no variable
        modifications, no isotope-error expansion, and no RT prediction.
    min_length, max_length : int
        Search peptide lengths, independent of the de novo evidence filter.

    Returns
    -------
    dict
        A fresh JSON-compatible config. LFQ and target/decoy generation are
        retained. Every actual search saves these settings in its JSON.
    """
    if digestion not in DIGESTIONS:
        raise ValueError(f"Unknown digestion: {digestion}; choose from {DIGESTIONS}")
    if search_mode not in SEARCH_MODES:
        raise ValueError(f"Unknown search mode: {search_mode}; choose from {SEARCH_MODES}")
    if (
        type(min_length) is not int
        or type(max_length) is not int
        or not 1 <= min_length <= max_length
    ):
        raise ValueError("Search peptide lengths require integers: 1 <= min <= max")
    cfg = _dda_config(fasta, mzml, output)
    enzyme = cfg["database"]["enzyme"]
    enzyme.update(min_len=min_length, max_len=max_length)
    if digestion == "semi-tryptic":
        enzyme["semi_enzymatic"] = True
    elif digestion == "unspecific":
        enzyme.update(cleave_at="", restrict=None, missed_cleavages=0, semi_enzymatic=False)
    if search_mode == "open":
        cfg["precursor_tol"] = {"da": [-500, 100]}
        cfg["isotope_errors"] = [0, 0]
        cfg["predict_rt"] = False
        cfg["database"]["variable_mods"] = {}
        cfg["database"]["max_variable_mods"] = 0
    return cfg