Skip to content

Aggregate results from cheminformatics and modelling analyses across many samples into a single report.

License

Notifications You must be signed in to change notification settings

tsenapathi/Montage

 
 

Repository files navigation

MultiQC

Montage

MultiQC for Chemical GlycoBiology

Aggregate chemical glycobiology results across many samples into a single report.

This fork includes computational chemistry, glycoinformatics and chemical biology tools.

Documentation Status DOI

-Find MultiQC documentation and example reports at http://multiqc.info

Montage is written in Python (tested with v 3.6).

Currently, supported computational, glycoinformatics and chemical biology tools include:

Molecular Dynamics Quantum Chemistry Post-dynamics analysis (e.g. Ring Pucker) Other
comp_qm montage_tessellate

Installation

Prerequisite dev environment

Install Ananconda python3, then create an environment for MultiQC

conda create -n multiqc python=3
source activate multiqc

clone the code

git clone git@github.com:scientificomputing/Montage.git
# OR git clone https://github.com/scientificomputing/Montage.git
pushd Montage

compile the code

make install

If make fails, look for required dependencies and install them, for example:

sudo yum install libyaml-devel libpng-devel -y
make install

Usage

Tessellate example

Install the tessellate tool

The tessellate part:

source activate multiqc
pip install tessellate
wget https://github.com/scientificomputing/tessellate/tree/master/data
tessellate data/usecase-timeseries --input-format=builtin --output-format=json --output-dir=output-usecase-timeseries

The Montage part:

export DATAPATH=`pwd`
multiqc $DATAPATH/output-usecase-timeseries -m montage_tessellate 
multiqc $DATAPATH/output-usecase-rnadna -m montage_tessellate 
multiqc $DATAPATH/output-usecase-cyclodextrin -m montage_tessellate 

QM example

Select log files from the data dir, only use the quantum analysis module and force creation of a report (overwrites existing):

multiqc data/*.log -m comp_qm -f

General usage

Once installed, you can use MultiQC by navigating to your analysis directory (or a parent directory) and running the tool:

multiqc .

Development

New modules

Create a new module, e.g. add module newmodule in multiqcc say multiqc/modules/newmodule/* A great example to work from is kallisto

Then include newmodule in:

  • multiqc/utils/search_patterns.yaml
  • setup.py

Future ideas (which may be fixed by core multiqc or highcharts dev)

  • render windows separately for large report. i.e. turn pieces on and off for memory intensive report
  • fix colour scales to include negative numbers
  • include all modules within the montage module, so usage will be -m montage and all submodule will be run
  • pip install that does not clash with multiqc. Ideally without renaming the main codebase.
    • If changes to the main code base become very exotic (some are in the pipeline), then this fork will diverge heavily from the base of multiqc and will need be renamed. Otherwise Montage will be a plugin for multiqc, a pull request will be submitted.

Credits

This package is a fork of MultiQC Project Lead & Contributors for MultiQC Montage credits @chrisbarnetttser

About

Aggregate results from cheminformatics and modelling analyses across many samples into a single report.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 82.9%
  • JavaScript 11.1%
  • HTML 4.1%
  • CSS 1.6%
  • Other 0.3%