fulcrum-ms 1.10.0

Fulcrum Pipeline™ is a search pipeline for extreme-scale proteomics experiments. It's based on composable, modular implementations using Spark to attain near-infinite scalability.

Installation

This library requires Python 3.10+ and can be installed with pip:

pip install fulcrum-ms

You may also need to install Java if you intend to run workflows locally.

Using Fulcrum on Databricks

Fulcrum is built to quickly run in a Databricks notebook environment. After setting up a cluster, you can install directly from your notebook:

%pip install fulcrum-ms

When invoking Fulcrum you should specify the SparkSession in use using the spark keyword parameter:

from fulcrum import fulcrum

fulcrum(spark=spark, **params)

CLI Usage

Fulcrum includes a CLI that permits running a workflow using TOML parameters:

fulcrum -v --param-toml '
workflow = "v0"

[search]
backend = "read_existing"
engine = "encyclopedia"
location = "data/2017dec27_overlap_dia_6b_rep1_604to616.dia.features.txt"
'

The CLI will accept JSON or TOML as either a string or a file:

# JSON string
fulcrum --param-json '{
    "workflow": "v0",
    "search": {
      "backend": "read_existing",
      "engine": "encyclopedia",
      "location": "data/2017dec27_overlap_dia_6b_rep1_604to616.dia.features.txt"
    }
}'

# JSON file
fulcrum --json-file path/to/file.json

# TOML file
fulcrum --toml-file path/to/file.toml

Python Usage

The full flexibility of Fulcrum is available through the Python library's fulcrum function. Usage is similar from a REPL or notebook interface:

>>> import logging; logging.getLogger().setLevel("INFO")
>>> from fulcrum import fulcrum
>>> fulcrum(
...   workflow = "v0",
...   search = dict(
...     backend = "read_existing",
...     engine = "encyclopedia",
...     location = "data/2017dec27_overlap_dia_6b_rep1_604to616.dia.features.txt",
...   )
... )
INFO:fulcrum.workflow.v0:Search stage found 1770 PSMs in 4.24 sec
INFO:fulcrum.workflow.v0:Built rescoring model in 3.57 sec
INFO:fulcrum.workflow.v0:Assigning confidence across the dataset using "mokapot score" (ascending)
INFO:fulcrum.workflow.v0:Assigned confidence to 832 PSMs or peptides in 2.81 sec
INFO:fulcrum.workflow.v0:Found 522 PSMs or peptides at 1% FDR

Configuring Spark

You may configure a connection to a Spark cluster by providing an appropriate spark_config section in the workflow parameters:

[spark_config]
"spark.master"="local[*]"
"driver.memory"="4g"

When calling Fulcrum from Python, you can either specify a spark_config dict or pass a SparkSession using the spark parameter.

fulcrum(
    spark=spark_session,
)

# OR

fulcrum(
    spark_config={
        "spark.master": "local[*]",
        "driver.memory": "4g",
    },
)