getting-started-with-genomics-tools-and-resources

Table of content

General
Courses
Some biology
Some statistics
linear algebra
- Bayesian Statistics
Learning Latex
Linux commands
Do not give me excel files!
How to name files
parallelization
Statistics
Data transfer
Website
profile R code
updating R
Better R code
Shiny App
R tools for data wrangling, tidying and visualizing.
Genomic data visulization
Sankey graph
Handling big data in R
Write your own R package
Documentation
handling arguments at the command line
visualization in general
Javascript
python tips and tools
machine learning
Amazon cloud computing
Genomics-visualization-tools
Databases
Large data consortium data mining
Integrative analysis
Interactive visualization
Tutorials
MOOC(Massive Open Online Courses)
git and version control
blogs
data management
Automate your workflow, open science and reproducible research
Survival curve
Organize research for a group
Clustering
CRISPR related
vector arts for life sciences

General

So you want to be a computational biologist?
Ten simple rules for biologists learning to program
Scientific computing: Code alert Nature News.
Some drawings about programming Very nice cartoon demonstrating useful concepts. https://wizardzines.com/
Practical computing for biologist. One of my first books to get me started in coding.
ModernDive An Introduction to Statistical and Data Sciences via R
Introduction to Data Science by Rafael A. Irizarry.
Learning Statistics with R
Hands-on Machine Learning with R
Reproducible Research Workflows with Snakemake and R
The Biologist’s Guide to Computing A book written by @tjelvar_olsson
A Primer for Computational Biology A nice book from Oregon State University. You can get a hard copy on Amazon https://www.amazon.com/Primer-Computational-Biology-Shawn-ONeil/dp/0870719262.
Computational Genomics With R A nice book from Altuna Akalin.
Modern Statistics for Modern Biology written by Prof. Susan Holmes from Stanford. I plan to read through it. a nice book using R for modern biology! looks awesome!
Introduction to Data Science A book by Tiffany-Anne. TimbersTrevor and CampbellMelissa Lee.
An Introduction To Applied Bioinformatics Interactive lessons in bioinformatics. http://www.readiab.org/introduction.html
Feature Engineering and Selection: A Practical Approach for Predictive Models by Kuhn and Johnson https://bookdown.org/max/FES
Agile Data Science with R
Offensieve programming book in R.
The Biostar Handbook: A Beginner’s Guide to Bioinformatics I am honored to be a co-author of this book. My ChIP-seq section was released by the mid of 2017.
Beginner’s Handbook to Next Generation Sequencing Everything you need to know about starting a sequencing project
Another Book on Data Science:Learn R and Python in Parallel compares R and python side by side.
A New Online Computational Biology Curriculum PLOS genetics paper.
Bioinformatics core competencies for undergraduate life sciences education
PH525x series – Biomedical Data Science The best course to get you started with genomics using R. I have taken 3 times for the same course to get a deep understanding of the concepts and R commands. Now everything can be found here from Rafael Irizarry lab: http://rafalab.github.io/pages/harvardx.html
The Bioconductor 2018 Workshop Compilation very rich!
Bioconductor for Genomics Data sciences Coursera course.
bioc workflow genomic annotation
Expanding the computational toolbox for mining cancer genomes Nature Review.
some repos from command line to rstats and github
2016 review Coming of age: ten years of next-generation sequencing technologies
Cancer genomics — from bench to bedside: review papers from Nature
SequencEnG: an Interactive Knowledge Base of Sequencing Techniques
Research Software Engineering with Python Building software that makes research possible. From Greg Wilson and Carpentries folks.
Research Software Engineering with R Building software that makes research possible

Courses

The Missing Semester of Your CS Education These MIT Classes teach you all about advanced topics within CS, from operating systems to machine learning, but there’s one critical subject that’s rarely covered, and is instead left to students to figure out on their own: proficiency with their tools. We’ll teach you how to master the command-line, use a powerful text editor, use fancy features of version control systems, and much more!
applied computational genomics by Aaron Quinlan, the creator of bedtools and many other cool tools.
BMMB 852: Applied Bioinformatics (Fall, 2016) by Istvan Albert, the creator of biostars.
JHU EN.600.649: Computational Genomics: Applied Comparative Genomics by Michael Schatz.
Introduction to Computational Biology by Mike Love.
Advanced Data Science by Jeff Leek.
Data Science for Biological, Medical and Health Research: Notes for 431: R focused
Various TeachingMaterial collected by Laurent Gatto.
NGS sequence analysis
bioinformatics-workbook
Reproducible Quantitative Methods from Mozilla science lab.
bio-info courses
MIT Computational Biology: Genomes, Networks, Evolution, Health – Fall 2018 – 6.047/6.878/HST.507by Manolis Kellis
MIT machine learning in Genomics by Manolis Kellis.
MIT linear algebra course by Gilbert Strang
A 2020 Vision of Linear Algebra by Gilbert Strang
Generalized Additive Models in R This short course will teach you how to use these flexible, powerful tools to model data and solve data science problems. GAMs offer offer a middle ground between simple linear models and complex machine-learning techniques, allowing you to model and understand complex systems.
Feature Engineering and Selection: A Practical Approach for Predictive Models
Tidy Modeling with R

Some biology

If you are from fields outside of biology, places to get you started:

Tales from the Genome A course by Udacity and 23andMe.
The Biology of Cancer A classic text book by Robert A. Weinberg. A must read for all cancer biologists.
Molecular Biology of the Cell A text book
Learn Genetics from University of Utah learning center.
iBiology offers several different types of courses
courses from khanacademy.org

Some statistics

Elements of Statistical Modeling for Experimental Biology by Jeffrey A. Walker.I plan to read this one!!
seeing theory The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations.
Points of Significance: Interpreting P values
statistics for biologists
Advanced Statistical Computing by Roger Peng.
fiveMinuteStats
Learning Statistics with R
Statistical Modeling of High Dimensional Counts by Mike love on RNAseq counts modeling.
Mixed models in R: a primer
Introduction to linear mixed models
MIXED EFFECTS COX REGRESSION
mixed model/hierachical model visualized
A brief introduction to mixed effects modelling and multi-model inference in ecology

linear algebra

Bayesian Statistics

Bayes Rules! An Introduction to Bayesian Modeling with R
Introduction to Bayesian Statistics STATS331 from Brendon Brewer.
Introduction to Empirical Bayes by David Robinson using baseball examples.
Statistical Rethinking github link https://github.com/rmcelreath/statrethinking_winter2019 Julia code https://shmuma.github.io/rethinking-2ed-julia/
Bayesian Data Analysis demos for R
Doing Bayesian Data Analysis in brms and the tidyverse a book.

Learning Latex

draw your symbols
The Best Way to Support LaTeX Math in Markdown with MathJax
TinyTeX A lightweight, cross-platform, portable, and easy-to-maintain LaTeX distribution based on TeX Live
Math…