Package: asari-metabolomics
AsariTrackable and scalable Python program for high-resolution LC-MS metabolomics data preprocessing (Li et al. Nature Communications 14.1 (2023): 4113):Taking advantage of high mass resolution to prioritize mass separation and alignmentPeak detection on a composite map instead of repeated on individual samplesStatistics guided peak dection, based on local maxima and prominence, selective use of smoothingReproducible, track and backtrack between features and EICsTracking peak quality, selectiviy metrics on m/z, chromatography and annotation databasesScalable, performance conscious, disciplined use of memory and CPUTransparent, JSON centric data structures, easy to chain other toolsA web server (https://asari.app) andfull pipelineare available now.InstallFrom PyPi repository:pip3 install asari-metabolomics. Add--upgradeto update to new versions.Or clone from source code:https://github.com/shuzhao-li/asari. One can run it as a Python module by calling Python interpreter. GitHub repo is often ahead of PyPi versions.Requires Python 3.8+. Installation time ~ 5 seconds if common libraries already exist.One can use the web version (https://asari.app) without local installation.InputInput data are centroied mzML files from LC-MS metabolomics.
We use ThermoRawFileParser (https://github.com/compomics/ThermoRawFileParser) to convert Thermo .RAW files to .mzML.
Msconvert in ProteoWizard (https://proteowizard.sourceforge.io/tools.shtml) can handle the conversion of most vendor data formats.MS/MS spectra are ignored by asari.
Our pipeline (https://pypi.org/project/pcpfm/) has annotation steps to use MS/MS data.UseIf installed from pip, one can runasarias a command in a terminal, followed by a subcommand for specific tasks.For help information:asari -hTo process all mzML files under directory mydir/projectx_dir:asari process --mode pos --input mydir/projectx_dirTo get statistical description on a single file (useful to understand data and parameters):asari analyze --input mydir/projectx_dir/file_to_analyze.mzMLTo get annotation on a tab delimited feature table:asari annotate --mode pos --ppm 10 --input mydir/projectx_dir/feature_table_file.tsvTo do automatic esitmation of min peak height, add this argument:--autoheight TrueTo output additional extraction table on a targeted list of m/z values from target_mzs.txt:asari extract --input mydir/projectx_dir --target target_mzs.txtThis is useful to add QC check during data processing, e.g. the target_mzs.txt file can be spike-in controls.To launch a dashboard in your web browser after the project is processed into directory process_result_dir:asari viz --input process_result_dirAlternative to a standalone command, to run as a module via Python interpreter, one needs to point to module location, e.g.:python3 -m asari.main process --mode pos --input mydir/projectx_dirOutputA typical run on disk may generatae a directory like thisrsvstudy_asari_project_427105156
├── Annotated_empricalCompounds.json
├── Feature_annotation.tsv
├── export
│   ├── _mass_grid_mapping.csv
│   ├── cmap.pickle
│   ├── full_Feature_table.tsv
│   └── unique_compound__Feature_table.tsv
├── pickle
│   ├── Blank_20210803_003.pickle
│   ├── ...
├── preferred_Feature_table.tsv
└── project.jsonThe recommended feature table ispreferred_Feature_table.tsv.All peaks are kept inexport/full_Feature_table.tsvif they meet signal (snr) and shape standards
(part of input parameters but default values are fine for most people).
That is, if a feature is only present in one sample, it will be reported,
as we think this is important for applications like exposome and personalized medicine.
The filtering decisions are left to end users.Thepicklefolder keeps intermediate files during processing.
They are removed after the processing by default, to save disk space.
Users can choose to keep them by specifying--pickle True.DashboardAfter data are processed, users can useasari viz --input process_result_dirto launch a dashboard to inspect data, where 'process_result_dir' refers to the result folder. The dashboard uses these files under the result folder: 'project.json', 'export/cmap.pickle', 'export/epd.pickle' and 'export/full_Feature_table.tsv'. Thus, one can move around the folder, but modification of these files is not a good idea. Please note that pickle files are for internal use, and one should not trust pickle files from other people.ParametersOnly one parameter in asari requires real attention, i.e., m/z precision is set at 5 ppm by default.
Most modern instruments are fine with 5 ppm, but one may want to change if needed.Default ionization mode ispos. Change tonegif needed, by specifying--mode negin command line.Users can supply a custom parameter filexyz.yaml, via--parameters xyz.yamlin command line.
A template YAML file can be found attest/parameters.yaml.When the above methods overlap, command line arguments take priority.
That is, commandline overwritesxyz.yaml, which overwrites default asari parameters indefaul_parameters.py.AlgorithmsBasic data concepts followhttps://github.com/shuzhao-li/metDataModel, organized as├── Experiment
   ├── Sample
       ├── MassTrack
           ├── Peak
           ├── Peak
       ├── MassTrack 
           ├── Peak
           ├── Peak
    ...
   ├── Sample 
    ...
   ├── SampleA sample here corresponds to an injection file in LC-MS experiments.
A MassTrack is an extracted chromatogram for a specific m/z measurement, governing full retention time.
Therefore, a MassTrack may include multiple mass traces, or EICs/XICs, as referred by literature.
Peak (an elution peak at specific m/z) is specific to a sample, but a feature is defined at the level of an experiment after correspondence.Additional details:Use of MassTracks simplifies m/z correspondence, which results in a MassGridTwo modes of m/z correspondence: a clustering method for studies >= N (default 10) samples;
and a slower method based on landmark peaks and verifying mass precision.Chromatogram construction is based on m/z values via flexible bins and frequency counts (in lieu histograms).Elution peak alignment is based on LOWESSUse integers for RT scan numbers and intensities for computing efficiencyAvoid mathematical curves whereas possible for computing efficiencySelectivity is tracked formSelectivity, how distinct are m/z measurementscSelectivity, how distinct are chromatograhic elution peaksStep-by-step algorithms are explained in doc/README.md.This package usesmass2chem,khipuandJMSfor mass search and annotation functions.PerformanceAsari is designed to run > 1000 samples on a laptop computer. The performance is achieved viaImplementation of basic functions using discrete mathematics and avoiding continuous curves.Main intensity values of each sample are not kept in memory.Simple (and transparent) peak detection based on local maxima (no curve fitting until evaluation)Composite mass tracks greatly reduce the run cycles on peak detectionUsing Python numerical libraries and vector operationsAlignment of mass tracks uses clustering in larger sample sizeWhen a study has N (default 10) or fewer samples, the MassGrid assembly uses a slower algorithm to compensate statistical distribution.If the individual files are large or the sample number is very high, it is easy to split the data and run asari separately.
One can then useasari jointo merge the results [in progress].Future improvement can be made by implementing some functions, e.g. chromatogram building, in C.Docker imageAthttps://hub.docker.com/r/shuzhao/asari.This image includes mono and ThermoRawFileParser, which converts Thermo .raw files to .mzML files.Example use
To launch with volume mapping$ docker run -v /Users/shuzhao/data:/home -ti shuzhao/asari.In the container, ThermoRawFileParser is under/usr/local/thermo/.# mono /usr/local/thermo/ThermoRawFileParser.exe -d my_data_dir

# asari analyze --input tmp/file_008.mzML 

# asari process --mode neg --input tmp --output test99LinksSource code:https://github.com/shuzhao-li/asariPackage Repository:https://pypi.org/project/asari-metabolomics/Test data:https://github.com/shuzhao-li/data/tree/main/dataNotebooks to reproduce publication figures:https://github.com/shuzhao-li/data/tree/main/notebooksHow accurate are my m/z values?The mass tracks are scaffolds to assemble data. Very close m/z values may not be distinguished on a mass track. For example, when mass tracks are constructed for 5 ppm resolution, two m/z values of 3 ppm apart will be reported on the same mass track. This leads to a situation where the m/z values are not optimal. Asari is designed for reliable information retrieval. If the data are processed under 5 ppm, the information can be retrieved by 5 ppm. The true m/z values will be recovered via annotation, if the features are resolved by LC, when asari features are matched to annotation libraries.As discussed in the manuscript, ppm is not perfect in modeling mass resolution and is not constant for all m/z ranges. It is a practical tool we currently work with. If two compounds are not resolved by LC and their m/z values are 4 ppm apart, asari processing by 5 ppm will treat them as one feature. If the mass resolution is justified, one can run asari using, for instance, 3 ppm. The default workflow in asari does not fine-tune the m/z values, because the split m/z peaks from centroiding are difficult to distinguish from real m/z peaks. We leave the fine-tuning to annotation or targeted extraction workflow.We thank reviewer #1 for valuable discussions on this topic.Related projectsMummichog: metabolomics pathway/network analysismetDataModel: data models for metabolomicsmass2chem: common utilities in interpreting mass spectrometry data, annotationkhipu: a Python library for generalized, low-level annotation of MS metabolomicsJMS: Json's Metabolite Services