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OrthOmics

This repository contains a collection of scripts for mining, analyzing, and visualizing omics data including:

  • Transcriptomics
  • Relative proteomics
  • Absolute proteomics

In addition, information is automatically pulled from DE analyses and matched to databases and custom analyses including:

  • Gene/Protein Conservation
  • Associated GO terms
  • Protein length/molecular weight

The included datasets were produced for three different fungal organisms:

  • S. cerevisiae
  • K. marxianus
  • Y. lipolytica

Growing on different experimental conditions in steady-state chemostats:

Conditions S. cervisiae CEN.PK 113 7-D K. marxianus CBS6556 Y. lipolytica W29
Reference 30°C / pH 5.5 30°C / pH 5.5 28°C / pH 5.5
High temperature 36°C / pH 5.5 40°C / pH 5.5 32°C / pH 5.5
Low pH 30°C / pH 3.5 30°C / pH 3.5 28°C / pH 3.5
Osmotic stress 30°C / pH 5.5 / 600 mM KCL 30°C / pH 5.5 / 600 mM KCL ---

The main script for this analysis is AnalysisPipeline_mainScript.R located on the complementaryScripts subfolder. It should be run using R studio. This script preprocesses raw data (via two filters), performs PCA and other dataset visualizations, and outputs DE data in .csv form for RNAseq, relative, and absolute proteomics datasets.

In addition, significant DE hits for all organisms and conditions are mapped to the SingleCopyOG_All.txt file that contains a list of 1:1:1 single copy orthologous proteins inferred using orthoFinder. This file allows the user to explore the evolutionary conserved stress-adaptation responses of the three organisms in this study at the transcript and protein levels.

Finally, an integrated table that contains results from DE RNAseq analysis, absolute proteomics levels, GO terms, gene names, molecular weights, AA sequence length, etc. is generated for the three organisms.

Integration into GEMs

The absolute proteomics datasets [NSAF] have also been incorporated to enzyme-constrained GEMs for the three organisms, available at:

Organism Model ID URL
S. cervisiae ecYeastGEM https://github.com/SysBioChalmers/ecModels/tree/chore/updateYeastGEM
K. marxianus ecKmarxGEM https://github.com/SysBioChalmers/ecModels/tree/chore/updateKmarx
Y. lipolytica ecYaliGEM https://github.com/SysBioChalmers/ecModels/tree/chore/update_iYali

KeyWords

Repo Category: Data Analysis; Utilisation: Multi-omics/multi-organisms datasets analysis; Field: Stress adaptation studies, Metabolic engineering, Omics, Evolutionary conservation;Omic Source: Transcriptomics, Proteomics, Genome-wide orthology; Taxonomy: S. cervisiae CEN.PK 113 /-D, K. marxianus CBS6556, Y. lipolytica W29

Last update: 2019-06-16

This repository is administered by @IVANDOMENZAIN, Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology

Installation

Required Software

  • R studio (version 1.0.136 or later)
  • The execution of AnalysisPipeline_mainScript.R installs all the necessary libraries and packages for running this pipeline as a first step.

Installation Instructions

Development Guidelines

Anybody is welcome to contribute to the development of OrthOmics, but please abide by the following guidelines.

Each function should start with a commented section describing the function and explaining the parameters. Existing functions can clarify what style should be used. When making any changes to an existing function (*.R-file), change the date and name of developer near the bottom of this commented section.

Bugfixes, new features and functions

  • For any development, whether bugfixes, introducing new functions or new/updated features for existing functions: make a separate branch from devel and name the branch for instance after the function/feature you are fixing/developing. If you work on a fix, start the branch name with fix/, if you work on a feature, start the branch name with feat/. Examples: fix/format_reactions or feat/new_algorithms.
  • Make commits to this branch while developing. Aim for backwards compatibility.
  • When you are happy with your new function/feature, make a pull request to the devel branch. Also, see Pull request below.

Semantic commits

Use semantic commit messages to make it easier to show what you are aiming to do:

  • chore: updating binaries (R workspaces), UniProt databases, omics data files, etc.
  • doc: updating documentation (README files) or explanatory comments in functions.
  • feat: new feature added, e.g. new function introduced / new parameters / new algorithm / etc.
  • fix: bugfix.
  • refactor: see code refactoring.
  • style: minor format changes of functions (spaces, semi-colons, etc., no code change).

Examples:

feat: add new proteins normalization method
chore: update UniProt database for CENPK113-7D
fix: variable name corrected in `load_ProtData` function

More detailed explanation or comments can be left in the commit description.

Pull request

  • No changes should be directly commited to the master or devel branches. Commits are made to side-branches, after which pull requests are made for merging with master or devel.
  • The person making the pull request and the one accepting the merge cannot be the same person.
  • A merge with the master branch invokes a new release.