This pipeline is extracted from a previous project, where I needed to compute various features for human and yeast proteins, and look for correlation between protein half-life and these features.

The pipeline aims to organize scripts that handle different types of data, automate data extraction and integration, and improve extensibility.

I don't know if this would be useful for anyone, but I present this experimental pipeline here.

• Clone the repo

• At the project root, run python code/main.py and follow the prompt.

• create a subdirectory under raw_inputs/ for data of the new species

• add a module (a Python file) under code/species/, say sp1.py

• edit this species in the species_choices variable in code/main.py (more details there)

• include a MySPC class (in the module you create), that extends SpeciesConnectivityMixin of species.base (This enables the pipeline to dynamically determine what Data objects are available that are associated with the species)

• follow the instructions below to add features

• Create a new class in the species module, say code/species/sp1, by extending MySPC and additionally

  • if the feature score already is in csv/tsv file, where each row includes the quantitative measurements and the identifier that you intend to keep, extend the AbstractData class (refer to data.py)

  • otherwise, extend the AbstractComputedData class (refer to compute_data.py), AND edit main() in compute_data.py to instantiate the new class (which computes scores for the feature)

• run code/main.py

raw_inputs/species_name/

• ALL data gathered, AND feature scores computed from those data

input/species_name/

• automatically generated from the pipeline that serves as input for further analysis
• contains preprocessed data (feature scores for a select list of proteins)

code/

• main.py a command-line interface

• data.py and compute_data.py deal with extracting and computing various features from raw data. They contain AbstractData, AbstractComputedData, and various subclasses of them, which are extended and customized in species-specific modules under code/species/.

• DataRemap.py takes the master list of proteins as template and fill in feature scores for them (producing files with .derived_features.tsv suffix under input/species_name/)

code/species/

• customization for species-specific data handling

learning_setup.py

• combine various features for learning, normalize the features

linear.py

• tries to play with some learning algorithms

** required numpy scipy biopython # if computing features from Fasta files matplotlib # for plotting

** likely needed scikit-learn # for machine learning

if you have pip setup, all packages can be installed with: pip install package_name

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