layout: learning-pathway
tags: [beginner, data-science, contributing, sequence-analysis, transcriptomics]
type: use
editorial_board:
- fpsom
- shiltemann
- hexylena
funding:
- gallantries
title: Gallantries Grant - Intellectual Output 1 - Introduction to data analysis and -management, statistics, and coding
description: |
This Learning Pathway collects the results of Intellectual Output 1 in the Gallantries Project
cover-image: shared/images/Gallantries_logo.png
cover-image-alt: "Gallantries logo with the carpentries wrench in galaxy 2 stripes 1 strip colour scheme."
priority: 5
draft: true
pathway:
- section: "Year 1: Coding in Python"
description: |
Intro to Coding in Python. Covers variables, functions, and data structures [SC1.1,2]
tutorials:
- name: python-basics
topic: data-science
- name: python-advanced-np-pd
topic: data-science
- section: "Year 1: Coding in Python Modular (Avans)"
description: |
Intro to Coding in Python. Covers variables, functions, and data structures [SC1.1,2]
In collaboration with Avans Hogeschool, an associated Partner we produced the following lessons
tutorials:
- topic: data-science
name: python-math
- topic: data-science
name: python-functions
- topic: data-science
name: python-types
- topic: data-science
name: python-iterables
- topic: data-science
name: python-flow
- topic: data-science
name: python-loops
- topic: data-science
name: python-exceptions
- topic: data-science
name: python-files
- topic: data-science
name: python-basics-recap
- topic: data-science
name: python-glob
- topic: data-science
name: python-argparse
- topic: data-science
name: python-subprocess
- topic: data-science
name: python-venv
- topic: data-science
name: python-conda
- section: "Year 1: Coding in R"
description: |
Intro to Coding in R. Covers variables, functions, and data structures [SC1.1,2]
tutorials:
- name: r-basics
topic: data-science
- name: r-advanced
topic: data-science
- name: r-dplyr
topic: data-science
- section: "Year 1: Intro to Command Line"
description: |
This submodule will cover the basics of the shell (variables, for loops), needed for data handling [SC1.1,2,6]
tutorials:
- name: cli-basics
topic: data-science
- name: cli-advanced
topic: data-science
- name: cli-bashcrawl
topic: data-science
- name: snakemake
topic: data-science
- section: "Year 1: Intro to Git and GitHub"
description: |
This submodule will cover the basics of research software development and sharing (committing, branching, forking, GitHub, etc.) [SC1.1,2,6]
tutorials:
- name: bash-git
topic: data-science
- name: git-cli
topic: data-science
- name: github-contribution
topic: contributing
- name: github-interface-contribution
topic: contributing
- section: "Year 2: Introduction to Genomics"
description: |
This submodule covers the biological background, as well as the technological concepts involved in genome sequencing, and their effects on downstream data analysis. [SC1.3,4,6]
- section: "Year 2: Quality Control"
description: |
This submodule will cover the evaluation of the quality of datasets, and how to improve quality by a cyclic process of cleaning, trimming and filtering datasets and re-evaluating the quality. [SC1.3-5]
tutorials:
- name: quality-control
topic: sequence-analysis
- section: "Year 2: Mapping"
description: |
This submodule will cover the comparison of genome sequencing samples to a reference genome. The concept of reference data is relevant in many data analyses across life sciences; connecting to online databases and incorporating this data into an analysis. [SC1.3,4]
tutorials:
- name: mapping
topic: sequence-analysis
- section: "Year 3: Variant Analysis"
description: |
This submodule will cover the topic of variant calling; after mapping of sequences to the reference genome, the regions that are different from the reference genome (variants) must be determined, and evaluated for impact. As any two individuals will by definition show many differences, the challenge of distinguishing between healthy variation and potential disease-causing variants is one of the main challenges in variant calling. [SC1.3-5]
tutorials:
- name: bash-variant-calling
topic: data-science
- section: "Year 3: Transcriptomics"
description: |
DNA only describes the potential of the genome; which genes are actually active within the cell and impacting the health and function of the organism, is determined via transcriptomics (RNA sequencing). By integrating data from these two levels of analysis (DNA and RNA), a clearer picture of the state of the cell can be obtained. [SC1.3-5]
tutorials:
- name: rna-seq-bash-star-align
topic: transcriptomics
