{ "cells": [ { "cell_type": "markdown", "id": "b9961d52", "metadata": {}, "source": [ "# DreaMS-Fluorine" ] }, { "cell_type": "markdown", "id": "b21de2cb", "metadata": {}, "source": [ "## 1. Obtain model weights\n", "\n", "Please contact us at roman.bushuiev@uochb.cas.cz to request the DreaMS-Fluorine model weight files (`dreams_fluorine_epoch=1-step=7000.ckpt` and `dreams_fluorine_epoch=30-step=111000.ckpt`). After receiving them, place both files in `/DreaMS/dreams/models/pretrained/`.\n", "\n", "Note that DreaMS-Fluorine was trained using NIST20, so we can share the weights only with NIST library license holders. Please attach your NIST license or order confirmation to the email.\n", "\n", "## 2. Run DreaMS-Fluorine\n", "\n", "Execute the following command, where `--in_dir` specifies the folder containing `.mzML` or `.mgf` files (here, `data/MSV000099559`). The script will generate `dreams_fluorine_predictions.csv` in the same folder, containing predicted fluorine-presence probabilities for each MS/MS spectrum in each file. The current model version supports positive-mode data only.\n", "\n", "```bash\n", "python3 dreams/cli.py dreams_fluorine --in_dir data/MSV000099559\n", "```\n", "\n", "## 3. Examine the predictions" ] }, { "cell_type": "code", "execution_count": 2, "id": "e9380bec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RTchargefile_namepolarityprecursor_mzprecursor_target_mzscan_numberspectrumwindow_lowindow_uoF_preds_111k_stepsF_preds_7k_stepsdformattag
0810.0635401MO23S_030.mzML1304.891639304.8916324153[[81.07015991210938, 83.0491714477539, 84.9596...0.50.50.9235190.210867AOnly 111k checkpoint > 0.9 hit
134.5457911MO23S_030.mzML1241.999878241.999878133[[81.07012939453125, 84.95979309082031, 84.964...0.50.50.9214770.217751AOnly 111k checkpoint > 0.9 hit
2514.0875121MO23S_030.mzML1204.138540204.1385352640[[78.58903503417969, 79.05422973632812, 84.044...0.50.50.9138970.604710AOnly 111k checkpoint > 0.9 hit
3810.7767001MO23S_027.mzML1328.915390328.9154054179[[81.06999206542969, 87.28488159179688, 90.947...0.50.50.9036430.140945AOnly 111k checkpoint > 0.9 hit
4809.9803201MO23S_027.mzML1304.891539304.8915414175[[84.95977783203125, 85.2956314086914, 90.0553...0.50.50.8786630.227668AOnly 111k checkpoint > 0.75 hit
.............................................
11125745.3860002MO23S_027.mzML1615.455358615.4553833859[[79.05448150634766, 80.05440521240234, 81.069...0.50.50.0000000.000000ANaN
11126745.7948402MO23S_027.mzML1593.442417593.4424443861[[80.05455780029297, 81.06998443603516, 83.085...0.50.50.0000000.000000ANaN
11127747.3213602MO23S_027.mzML1571.429528571.4295043868[[80.05457305908203, 81.06989288330078, 83.085...0.50.50.0000000.000000ANaN
11128748.5337202MO23S_027.mzML1549.416251549.4162603874[[80.05448150634766, 81.06990814208984, 81.132...0.50.50.0000000.000000ANaN
11129749.5493402MO23S_027.mzML1527.403199527.4031983879[[77.15992736816406, 77.27590942382812, 80.054...0.50.50.0000000.000000ANaN
\n", "

11130 rows × 14 columns

\n", "
" ], "text/plain": [ " RT charge file_name polarity precursor_mz \\\n", "0 810.063540 1 MO23S_030.mzML 1 304.891639 \n", "1 34.545791 1 MO23S_030.mzML 1 241.999878 \n", "2 514.087512 1 MO23S_030.mzML 1 204.138540 \n", "3 810.776700 1 MO23S_027.mzML 1 328.915390 \n", "4 809.980320 1 MO23S_027.mzML 1 304.891539 \n", "... ... ... ... ... ... \n", "11125 745.386000 2 MO23S_027.mzML 1 615.455358 \n", "11126 745.794840 2 MO23S_027.mzML 1 593.442417 \n", "11127 747.321360 2 MO23S_027.mzML 1 571.429528 \n", "11128 748.533720 2 MO23S_027.mzML 1 549.416251 \n", "11129 749.549340 2 MO23S_027.mzML 1 527.403199 \n", "\n", " precursor_target_mz scan_number \\\n", "0 304.891632 4153 \n", "1 241.999878 133 \n", "2 204.138535 2640 \n", "3 328.915405 4179 \n", "4 304.891541 4175 \n", "... ... ... \n", "11125 615.455383 3859 \n", "11126 593.442444 3861 \n", "11127 571.429504 3868 \n", "11128 549.416260 3874 \n", "11129 527.403198 3879 \n", "\n", " spectrum window_lo \\\n", "0 [[81.07015991210938, 83.0491714477539, 84.9596... 0.5 \n", "1 [[81.07012939453125, 84.95979309082031, 84.964... 0.5 \n", "2 [[78.58903503417969, 79.05422973632812, 84.044... 0.5 \n", "3 [[81.06999206542969, 87.28488159179688, 90.947... 0.5 \n", "4 [[84.95977783203125, 85.2956314086914, 90.0553... 0.5 \n", "... ... ... \n", "11125 [[79.05448150634766, 80.05440521240234, 81.069... 0.5 \n", "11126 [[80.05455780029297, 81.06998443603516, 83.085... 0.5 \n", "11127 [[80.05457305908203, 81.06989288330078, 83.085... 0.5 \n", "11128 [[80.05448150634766, 81.06990814208984, 81.132... 0.5 \n", "11129 [[77.15992736816406, 77.27590942382812, 80.054... 0.5 \n", "\n", " window_uo F_preds_111k_steps F_preds_7k_steps dformat \\\n", "0 0.5 0.923519 0.210867 A \n", "1 0.5 0.921477 0.217751 A \n", "2 0.5 0.913897 0.604710 A \n", "3 0.5 0.903643 0.140945 A \n", "4 0.5 0.878663 0.227668 A \n", "... ... ... ... ... \n", "11125 0.5 0.000000 0.000000 A \n", "11126 0.5 0.000000 0.000000 A \n", "11127 0.5 0.000000 0.000000 A \n", "11128 0.5 0.000000 0.000000 A \n", "11129 0.5 0.000000 0.000000 A \n", "\n", " tag \n", "0 Only 111k checkpoint > 0.9 hit \n", "1 Only 111k checkpoint > 0.9 hit \n", "2 Only 111k checkpoint > 0.9 hit \n", "3 Only 111k checkpoint > 0.9 hit \n", "4 Only 111k checkpoint > 0.75 hit \n", "... ... \n", "11125 NaN \n", "11126 NaN \n", "11127 NaN \n", "11128 NaN \n", "11129 NaN \n", "\n", "[11130 rows x 14 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('../data/MSV000099559/dreams_fluorine_predictions.csv')\n", "df" ] }, { "cell_type": "markdown", "id": "52eff0d6", "metadata": {}, "source": [ "The prediction file contains standard metadata parsed from the input files, such as retention time (`RT`) and precursor m/z (`precursor_mz`), which can be used to reference the input spectra. In addition, it includes DreaMS-Fluorine predictions in the columns `F_preds_111k_steps` and `F_preds_7k_steps`. These columns represent fluorine presence probabilities predicted by two versions of the DreaMS-Fluorine model trained for different numbers of steps.\n", "\n", "We consider a prediction to be confident if both models predict a fluorine probability of at least 0.75. Such cases are annotated in the `tag` column as `> 0.75 hit`, `> 0.9 hit`, or `> 0.95 hit`, depending on the prediction scores. Less confident predictions are labeled as `Only 111k checkpoint > 0.75 hit`, `Only 111k checkpoint > 0.9 hit`, or `Only 111k checkpoint > 0.95 hit`, indicating that only the model trained for 111k steps produced a confident prediction.\n", "\n", "We further classify spectra into low- and high-quality categories (see [Fig. 2b](https://www.nature.com/articles/s41587-025-02663-3/figures/2)). If a spectrum is of low quality but still yields a confident prediction, the model may be hallucinating the result (for example, for spectra containing only a single signal). Such cases are therefore marked with a `Low quality` prefix in the `tag` column. If the `tag` value is NaN, the spectrum is not predicted to correspond to a fluorinated molecule.\n", "\n", "The plot below summarizes these tags for three files from the example [MSV000099559](https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=47c76080c5f345ed938f1ccc9234c7e0) dataset. While there are no highly confident predictions supported by both models, there are 18 predictions of fluorinated molecules supported by only one of the models (corresponding to the last two rows in the heatmap)." ] }, { "cell_type": "code", "execution_count": 13, "id": "8f1c6e82", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "df_plot = df[df['tag'] != '']\n", "tag_file_counts = df_plot.pivot_table(index='tag', columns='file_name', values='RT', aggfunc='count', fill_value=0)\n", "plt.figure(figsize=(5, 5))\n", "sns.heatmap(tag_file_counts, annot=True, fmt='d', cmap='Greens', cbar_kws={'label': 'Count'})\n", "plt.xlabel('File Name')\n", "plt.ylabel('Tag')\n", "plt.title('Counts of fluorine predictions per file')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "dreams", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }