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

Table of content

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

• Essence of linear algebra by threebrownoneblue
• 18.06 from Gilbert Strang
• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning from Gilbert Strang
• Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squaresusing Julia language.
• Common statistical tests are linear models (or: how to teach stats)
• Course materials for applied regression STATS191 stanford

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 expression http://www.math.mcgill.ca/yyang/regression/RMarkdown/example.html
• intro to Latex2 chapter 3
• The Bates LaTeX Manual

Linux commands

• Greg Wilson’s youtube videos on unix shell
• A Bioinformatician’s UNIX Toolbox from Heng Li
• Linux command line exercises for NGS data processing
• command line bootcamp teaches you unix command step by step
• Unix in your browser. Maybe useful for teaching bash?
• A Book for Anyone to Get Started with Unix
• bash one-liners for bioinformatics
• some of my bash one-liner collections
• Use the Unofficial Bash Strict Mode (Unless You Looove Debugging)
• Defensive BASH Programming very good read for bash programming.
• Better Bash Scripting in 15 Minutes
• bash pitfalls
• Advancing in the Bash Shell
• Bash tips
  
• Bash by example
• process substitution: Using Names Pipes and Process Substitution in Bioinformatics Handy Bash feature: Process Substitution
• NGS Advanced Beginner/Intermediate Shell
• Commonly used commands for PBS scheduler:Monitoring and Managing Your Job
• test your unix skills at cmd challenge
• people say awk is not part of bioinformats 🙂 Still very useful parsing plain text files. Steve’s Awk Academy
• intro-bioinformatics: Website and slides for intro to bioinformatics class at Fred Hutch
  
• tmate:Instant terminal sharing
• tmux is a terminal multiplexer similar to screen but have more features.
  tmux cheatsheet
  tmux config
  tmux install without root
• All about redirection

Theory and quick reference
There are 3 file descriptors, stdin, stdout and stderr (std=standard).
Basically you can:
redirect stdout to a file
redirect stderr to a file
redirect stdout to a stderr
redirect stderr to a stdout
redirect stderr and stdout to a file
redirect stderr and stdout to stdout
redirect stderr and stdout to stderr
1 ‘represents’ stdout and 2 stderr.
A little note for seeing this things: with the less command you can view both stdout (which will remain on the buffer) and the stderr that will be printed on the screen, but…