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NGS for natural scientist
  • 1. Preface
    • How to use this book
    • Motivation
    • Genomic data science as a tool to biologist
    • Next Generation Science (also NGS)
  • 2. Getting started
    • A step by step pipeline tutorial
    • Sequencing chemistry explained by Illumina
    • Joining a course
    • RNA quality and Library prep
    • (optional) My click moment about "Why Linux"
  • 3. Good-to-know beforehand
    • Experiment design
    • Single-end and Paired-end
    • Read per sample and data size
    • Normalization - RPKM/FPKM/TPM
    • Gene annotation
  • 4. Setting up terminal
    • My Linux terminal
    • Linux environment
    • R and RStudio
    • PATH
  • 5. FASTQ and quality control
    • Getting FASTQ files from online database
    • FASTQ quality assessment
  • 6. Mapping/alignment and quantification
    • Salmon
    • DESeq2
  • 7. Visualization
  • 8. Single cell RNA-Seq
  • 9. AWS cloud and Machine Learning
    • Machine Learning in a nutshell
    • R vs Python
    • Setting up ML terminal
    • Data exploration
  • (pending material)
    • graphPad
    • readings for ML
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  1. 1. Preface

Motivation

I am not applying to a PhD, but I do am looking for a better life.

At the beginning I plan to start from setting up the computer to visualizing data. This is essentially my log book. The main motivation is simple - to provide an alternative to tutorials written by programmers.

Like many others I have tried to skip the trouble by using web application such as GERIN or more famous and resourceful Galaxy but at the end of the day geek are all more comfortable with things under our full control. One got to answer the questions from reviewers #2 isn't it?

I am not confining myself to RNA-Seq but I consider this is a pretty good start in bioinformatics, and I do not need a fortune teller to tell me I will definitely move on to other NGS platforms. This is also the nature of working in commercial research - that you will be working on multiple projects on short notice with impossible time frame. This is what actually pushed me into computational biology. Destiny.

One can also see this as a diary of a struggling hard-core natural scientist who transited from academia to industry, and trying to transform the (my) infrastructure to adapt to the change of the world. Not to become someone else, but a better man.

Then at the end of the day, I decided not to publish this but just keep this until I don't need this. And every time when one decided not to push, things that are legitimate will flow like water and flush itself out without giving in. I quitted my then job and an important friend that I made at that job paved the way, like usual, to allow this skill to pass along and therefore I edited and published this as a training material for self-taught conventional mRNA-Seq.

By the way I am open for hiring from start-up to global renowned pharmaceuticals/personal care/FMCG entities. Essentially anywhere that find my skills to be useful, for a tiny amount of token. Feel free to contact me for a discussion. tsemays at hotmail dot com.

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Last updated 2 months ago