Skip to main content

A software to extract and analyze the structure and associated metadata from a Nextflow workflow.

Project description

BioFlow-Insight

License: GPL v3 Version 2.0.9

Description

BioFlow-Insight is a Python-based open-source command-line tool designed to automatically analyse Nextflow workflow code, gathering useful information, particularly in the form of visual graphs that illustrate the workflow's structure and its various steps. Additionally, it is capable of detecting certain programming errors and generates a RO-Crate JSON-LD file that describes the workflow. It also generates a json describing the workflow's srtucture in a simplified metro-map form which can be visualised with MetroFlow.

BioFlow-Insight is easily installable as a CLI (see here). It is also freely accessible as a free web service. For more information and to start using BioFlow-Insight, visit here (https://bioflow-insight.pasteur.cloud/).

Table of Contents

Installation

Installing via pip

BioFlow-Insight is easily installable as a CLI.

To install it using pip, use the following command :

pip install bioflow-insight

Using from source

To access its source code, simply clone its GitLab repository. BioFlow-Insight is developed using Python 3

BioFlow-Insight's dependencies are given in the requirements.txt file.

Note : To install graphviz, in linux you might need to execute this command sudo apt install graphviz

Usage

BioFlow-Insight is a Python-based open-source command-line tool designed to automatically analyse Nextflow workflow code, gathering useful information, particularly in the form of visual graphs that illustrate the workflow's structure and its various steps. Additionally, it is capable of detecting certain programming errors and generates a RO-Crate JSON-LD file that describes the workflow. It also generates a json describing the workflow's srtucture in a simplified metro-map form which can be visualised with MetroFlow.

For an explanation of the different elements composing a Nextflow workflow, see its documentation.

The graphs generated by BioFlow-Insight are :

  • Specification graph: BioFlow-Insight reconstructs the workflow’s specification graph from its source code without having to execute it. The specification graph is defined as a directed graph where nodes are processes and operations, and edges are channels that are directed from one vertex to another (steps of the workflow are ordered). This graph represents all the possible interactions between processes and operations through channels that are defined in the workflow code. Within the specification graph, we define two types of operations: operations are categorised in two groups: the following operations defined as operations that have at least one input, and the starting operations defined as operations without any inputs.

  • Process dependency graph: BioFlow-Insight generates the process dependency graph which represents only processes (nodes) and their dependencies (edges). Similar to the dependency graph, this graph is constructed by removing all operations, leaving only processes, and linking them based on their dependencies in the original specification graph. Again in this representation, the edges no longer represent interaction between its elements, but their dependencies.

  • Metro-Map Json file: BioFlow-Insight also generates the metro-map json file, which describes the workflow in metro-map form (with process code, conditions represented by colour, etc..). This needs to be updated

To run BioFlow-Insight to obtain all the outputs, run this command:

bioflow-insight --analysis bioflow my_workflow/main.nf

If you want to simply obtain the json file which describes the Metro-Map, run this command:

bioflow-insight --analysis metroflow my_workflow/main.nf

For a more in-depth explanation of BioFlow-Insight functionnalities, visit its webpage here (https://bioflow-insight.pasteur.cloud/specification/).

Using BioFlow-Insight directly through the source

You can also use BioFlow-Insight directly through the source code without having to install the command line but using a python environment. Here is an example to so (after cloning the repo):

#Importing the Workflow class
import os
current_path= os.getcwd()
os.chdir("bioflow-insight/")
from src.workflow import Workflow
os.chdir(current_path)

#Initialising the workflow
w = Workflow("../MyWorkflow/")
w.initialise_with_bioflow()
w.generate_specification_graph(render_graphs=False)
w.generate_process_dependency_graph(render_graphs=True, with_conditions = True)
#Get the processes called
processes_called = w.get_processes_called()
#Generate RO-Crate and Prov-Crate 
w.get_rocrate()
w.get_prov_rocrate()
w.get_history()

Citing BioFlow-Insight

Please cite BioFlow-Insight in any research that uses or extends BioFlow-Insight.

To cite BioFlow-Insight, please use the following publication:

George Marchment, Bryan Brancotte, Marie Schmit, Frédéric Lemoine, Sarah Cohen-Boulakia, BioFlow-Insight: facilitating reuse of Nextflow workflows with structure reconstruction and visualization, NAR Genomics and Bioinformatics, Volume 6, Issue 3, September 2024, lqae092, https://doi.org/10.1093/nargab/lqae092

License

This project is licensed under the GNU Affero General Public License.

Funding

This work received support from the National Research Agency under the France 2030 program, with reference to ANR-22-PESN-0007.







Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

bioflow_insight-2.0.14.tar.gz (13.8 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bioflow_insight-2.0.14-py3-none-any.whl (13.8 MB view details)

Uploaded Python 3

File details

Details for the file bioflow_insight-2.0.14.tar.gz.

File metadata

  • Download URL: bioflow_insight-2.0.14.tar.gz
  • Upload date:
  • Size: 13.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.10

File hashes

Hashes for bioflow_insight-2.0.14.tar.gz
Algorithm Hash digest
SHA256 d88a86a26968fce54f7f25e5561f250df8c959df0c45af99a41940c266d9a36f
MD5 600bde7f914b2c6aeed5e757400e921d
BLAKE2b-256 13f3fb9dd52e4c9e42e49267c502e539acd9bc578c5e1a3ffa11464f52f9b371

See more details on using hashes here.

File details

Details for the file bioflow_insight-2.0.14-py3-none-any.whl.

File metadata

File hashes

Hashes for bioflow_insight-2.0.14-py3-none-any.whl
Algorithm Hash digest
SHA256 9e3af28e662d642d402395603cc3894a4a2ac8319fd61ad83458408e1c36ef88
MD5 96565e60d733a9ee5626097ed6f04694
BLAKE2b-256 cf6428e545b77badebe8afb1387e03bf918af73233ee267197bbebafff23248e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page