Crate koth_ms 

koth_ff: A high-performance LC-MS feature finder for timsTOF and mzML data.

Pipeline

1. Hill detection – groups MS1 peaks across scans into chromatographic hills
2. Feature finding – groups hills into isotope envelopes with charge state assignment
3. Scoring – scores isotope pattern quality using the averagine model

Quick start

use std::path::Path;
use koth_ms::{run_hills, run_features, run_scoring, config::KothConfig};

let config = KothConfig::default();
let input = Path::new("data.mzML");

let spectra = koth_ms::read_spectra(input, &config.file).unwrap();
let hills = run_hills(&spectra, &config.hills, &config.file);
let features = run_features(&hills, &config.features, &config.file).unwrap();
let scored = run_scoring(&features, &config.scoring, &config.features, config.file.polarity);

Re-exports

pub use pipeline::group_ms2_hills_by_window;

pub use pipeline::run_pipeline;

pub use pipeline::run_pipeline_from_spectra;

pub use pipeline::run_pipeline_streaming;

pub use pipeline::run_pipeline_streaming_from_spectra;

pub use pipeline::run_pipeline_with_ms2;

pub use pipeline::FeatureFindingOutput;

pub use pipeline::PipelineOptions;

pub use pipeline::PipelineSink;

pub use models::Feature;

pub use models::Hill;

pub use models::IsolationWindow;

pub use models::Polarity;

pub use models::ScoredFeature;

pub use models::Spectrum;

Modules

alignment

config

error

features

hills

input

io

lfq

mem

models

output

pipeline

In-process / streaming feature-finding API.

scoring

stats

Small shared numeric helpers.

Constants

VERSION

Crate version with the git commit it was built from, e.g. 0.1.0 (a1b2c3d4e5f6), or 0.1.0 (a1b2c3d4e5f6-dirty) when the working tree had uncommitted changes.

Functions

read_spectra

Read MS1 spectra from an mzML file or Bruker .d directory.

run_features

Stage 2: Detect isotope features from hills.

run_hills

Stage 1: Detect chromatographic hills from MS1 spectra.

run_hills_streaming

Stage 1 (streaming): Detect hills from mzML, Bruker .d, or Thermo .raw using bounded spectrum buffers. Completed hills are retained for assembly; native readers also retain scan metadata. Decoy shuffling and optional TIC normalization still collect spectra. Gzipped mzML is decompressed as it is parsed, never held whole in memory.

run_ms2_hills_streaming

Stage 1 (MS2): Detect MS2 hills partitioned by the precursor isolation window of each MS2 spectrum (DIA channels).

run_scoring

Stage 3: Score isotope features using the averagine model.