This document covers all resources covered in the manuscript "Virtual Curriculum and Educational Resources for Computational Proteomics" by Mesuere et al.
Topics:
- Molecular Biology
- Computer Science: The Basics
- Computer Science: Advanced Topics
- Statistics
- Mass Spectrometry
- Bioinformatics
- Computational Proteomics
- Online edX course: https://www.edx.org/course/introduction-to-biology-the-secret-of-life-3
- Basics about genes, proteins and biochemistry
- No prior knowledge required
- 5-10 hours per week, for 15 weeks
- Textbook by Alberts et al.
- Undergraduate level textbook on molecular biology
- Accessible in the NCBI bookshelf: https://www.ncbi.nlm.nih.gov/books/NBK21054
- Textbook by Janeway et al.
- Undergraduate level textbook on immunology
- Accessible in the NCBI bookshelf: https://www.ncbi.nlm.nih.gov/books/NBK10757
- Online Coursera course: https://www.coursera.org/learn/learn-to-program
- Sticks with the basics in Python
- Both textual and audiovisual course material, supplemented with exercises
- 7 weeks program, 29 hours to complete
- Online edX/MITx course: https://courses.edx.org/courses/MITx/6.00.1_4x/3T2014/course
- Broader selection of topics including algorithms and data structures in Python
- 15 lectures
- Online Coursera course: https://www.coursera.org/learn/r-programming
- Basic introduction to R
- 20 hours to complete
- Free tutorials: https://www.rstudio.com/online-learning/
- Covers a broad variety of topics: R programming, Shiny, R markdown, data science, ...
- Book by Hadley Wickham based on the tidyverse packages
- Great start for beginning programmers who want to learn R
- Additional focus on data visualisation
- Available for free online: http://r4ds.had.co.nz/
- Online edX course: https://www.edx.org/course/python-for-data-science
- Data science using Python
- Covers Jupyter Notebooks, NumPy, Pandas, matplotlib and many other tools
- The first few lectures can be skipped if you already have python experience
- 8-10 hours per week, for 10 weeks
- Online edX course: https://www.edx.org/course/algorithmic-design-techniques-uc-san-diegox-algs200x
- Covers how to design algorithms, solve computational problems and implement efficient solutions
- Also includes testing and debugging
- 8-10 hours per week, for 6 weeks
- Online edX course: https://www.edx.org/course/data-structures-uc-san-diegox-algs201x
- Covers both basic and advanced data structures
- Uses a programming language of choice
- 8-10 hours per week, for 6 weeks
- Online edX course: https://www.edx.org/course/graph-algorithms-uc-san-diegox-algs202x
- Rather advanced course on graph algorithms
- Requires basic knowledge of algorithms and data structures, but not of graph theory
- Covers topics such as shortest path calculations, connectedness checks, minimal spanning trees, ...
- 8-10 hours per week, for 6 weeks
- Tutorial on Cytoscape: http://sdcsb.dreamhosters.com/sdcsb/wp-content/uploads/2012/12/workshop_tutorial.pdf
- This workshop contains a basic tutorial
- Cytoscape can be used to visualize graph structures
- Online edX course: https://www.edx.org/course/machine-learning-fundamentals-uc-san-diegox-dse220x
- Introductory course on machine learning
- Uses Python and Jupyter notebooks
- Covers topics such as classification, regression, ensemble methods and representation learning
- 8 hours per week, for 10 weeks
- Online edX/MITx course: https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2
- Introductory course to probabilistics
- Covers probabilistic models, random variables and their distributions, inference methods, ...
- 12 hours per week, for 18 weeks
- Online Coursera course: https://www.coursera.org/learn/probability-intro
- Introduction to probabilistics using R
- Covers basic probability theory and Bayes' rule, sampling methods, ...
- 5-7 hours per week, for 5 weeks
- Online edX course: https://www.edx.org/course/probability-and-statistics-in-data-science-using-python-2
- Introduction to statistical and probabilistic approaches using Python
- Covers random variables, dependence, correlation, regression, PCA, entropy and MDL
- 10-12 hours per week, for 10 weeks
- Online edX course: https://www.edx.org/course/biostatistics-big-data-applications-utmbx-stat101x#
- Basic data analysis for working with biomedial big data using R
- 2-3 hours per week, for 8 weeks
- Online video series by ASMS: https://www.asms.org/about-mass-spectrometry
- Covers the basics of mass spectometry
- 5 videos of 30-40 minutes each
- Youtube lecture series by Lennart Martens: https://www.youtube.com/playlist?list=PLXxp6nsBenSX_W8DiOocKJ0laNauYNdYl
- Covers the basis of mass spectrometry with a focus on proteomics
- 7 videos of 3 hours in total
- Online edX/MITx course: https://www.edx.org/course/quantitative-biology-workshop-mitx-7-qbwx-3
- Workshop-style introduction to tools used in biological research
- Basic knowledge of biochemistry, molecular biology and genetics is a prerequisite
- 4-8 hours per week, for 8 weeks
- Online edX/MITx course: https://www.edx.org/course/introduction-to-computational-thinking-and-data-science-2
- Hands-on course on how to use computation to accomplish a variety of goals
- Covers dynamic programming, Monte Carlo simulations, curve fitting, plotting graphs, ...
- 14-16 hours per week, for 9 weeks
- Online edX course: https://www.edx.org/course/statistical-analysis-bioinformatics-usmx-umuc-bif003x-0
- Analyze biological big data using R
- 8-10 hours per week, for 8 weeks
- Online tutorial from the CompOmics group: https://compomics.com/bioinformatics-for-proteomics/
- The basic steps to analyze mass spectrometry data
- Covers identifying peptides, proteins and their modifications, annotating the data with existing biological knowledge, sharing the data using online repositories, and different techniques for quantifying peptides and proteins
- Video capture a workshop by the Olga Vitek Lab: https://computationalproteomics.ccis.northeastern.edu/
- Covers a wide range of computational and statistical aspects of quantitative mass-spectrometry-based proteomics