(Part 2) Best products from r/bioinformatics

You won't get more mileage in CS per hour of effort than you will by studying discrete mathematics. You need to know about:


Mathematical logic: logical connectives, truth tables, axioms, common techniques of proof -- especially proof by induction, which comes in handy when you want to prove the correctness of an algorithm


Elementary counting techniques and combinatorics


Sets, functions (you need to be familiar with injections, surjections, and bijections), relations, cardinalities


Graphs and trees (bonus points for implementing them as classes in your favorite language). Eulerian and Hamiltonian paths. Recognize that many computational problems are equivalent to searching for a node or a certain kind of path through a graph.


Those ideas will get you surprisingly far in computer science. Once you're familiar with those things, you'll be able to see where a lot of the other ideas are coming from.

At the very least, you'll have some tools to guide you so that any computer program you hack together won't be completely awful, even if it isn't optimal (it almost never needs to be optimal; it just needs to get the job done in a reasonable amount of time). After that, you can do some linear algebra and probability theory. After probability theory you will be able to think clearly about statistics. I recommend starting with this book, which is great for self-study:

http://www.amazon.com/Discrete-Mathematics-Technology-Second-Edition/dp/0750306521

Move as slowly as you need to. Try to do any of the exercises for which the solutions aren't immediately obvious to you. Even if you only get through a chapter or two, you'll be much better off than you would have been.

I can tell you what I think was the most importent stuff we have been doing so far in my bachelor.

BioChemistry

• Properties of aminoacids, peptides and proteins
  
• Function of proteins and enzymes
  
• enzyme kinetics
  
  Cellbiology
  
  
• Organisation of eukaryotic cells
  
• Development from one celled organisms to multicelled orgaism and evolution
  
• Compartiments of the cell and their functions and morphology(this includes stuff like DNA replication and ATP Synthasis and translation and transcription of proteins)
  
• Transportmechanisms of small and big molecules from outside the cell to the inside and vice versa . transportation within the cell as well(eg endocythic pathway)
  
• Signaltransduction
  
  IT Basics
  
  
• Boolean Logic
  
• Understanding of the number representation systems(eg. binar or hex)
  
• Understanding of floating point representation and why it leads to rounding errors
  
• Understanding the Neuman Architecture
  
• Basics of graph theory
  
• Grammars
  
• Automata and Touring Machines
  
• Basics of InformationTheory(eg. Entropy)
  
• Basics of Datacompressions (not very important in your case)
  
• Basic Hashing Algorithms
  
• Runtime analysis(all the O notation stuff)
  
  Operating Systems
  
  
• Basics of linux(eg commands like cd, mkdir, ls, mv, check this out )
  
• basic programms within linux(eg grep, wget, nano )
  
• basics of bash programming
  
  BioinformaticsBasics
  
  
• Pairwise Sequence Alignment
  
• Database Similarity Search
  
• Multiple Sequence Alignment
  
• Hidden Markov Models
  
• Gene and promoter Prediction
  
• Phylogenetic basics
  
• Protein and RNA 3D structure prediction
  
  So this is just supposed to be some kind of reference you can use to learning. You probably don't need to work through all of this.
  But I strongly suggest reading about Biochemistry and Cellbiology(a nice book is Molecular Biology of the Cell) as it is really important for understanding bioinformatics.
  Also give the link I posted in the Operating System part a look. Try to just use linux for a month as a lot of bioinformatics applications are written for linux and its nice to see the contrast to windows.
  Regarding programming I suggest you search for a book that combines python + bioinformatics(something like this). If you want to focus on the programming part you would ideally start in ASM then switch to C then to Java and then to python.(Just to give you an impression why: ASM gives you a great insight into how the CPU works and how it acesses RAM. C is on a higher level and you start thinking about organising data and defining its structure in RAM. Java adds another layer onto that - you get objects, which make it easy for you to organize your data in blocks and there is no need for you to manage the RAM by hand with pointers like in C. But you still need to tell your variables specifically what they are. So if you have a variable that safes a Text in it you have to declare it as a string. Finally you arrived at python which is a scripting language. There is no more need for you to tell variables what they are - the compiler decides it automatically. All the annoying parts are automated. So your code becomes shorter as you don't need to type as much. The philosophy behind scripting languages is mostly to provide languages that are designed for humans not for machines).But it is kind of a overkill in your situation. Just focus on python. One final thing regarding programming just keep practicing. It is really hard at the beginning but once you get it, it starts making fun to programm as it becomes a creative way of expressing your logic.
  Let's get to the bioinforamtics part. I don't think you really need to study this really hard but it's nice to be ahead of your commilitones. I recommand reading this book. You might also check out Rosalind and practice your python on some bioinformatics problems.
  Edit: If you want I can send you some books as pdf files if you PM me your email adress
  

Broadly speaking, there's no other answer the one /u/pulsus_mortuss_equus gives, hit the literature. I guess for someone with little to no background, I might suggest the top down approach of just going the table of contents at places like Annual Reviews Genetics or Nature Reviews Genetics for the past few years (but even here you're going to have to be selective, simply because the volume of the literature is so vast). Then just apply the strategy of working backwards into the literature cited in these reviews.

Another suggestion, perhaps to get started, might be a book like Aravinda Chakravarti's new edited collection Human Variation: A Genetic Perspective on Diversity, Race, and Medicine. I literally just got a copy in my hands about 15 minutes ago, and after reading bits and pieces of a few chapters, it seems to be targeted toward the non-expert scientists, which sounds like exactly what you're looking for.

You mention 1000 genome project and the like. There are a lot of large research consortia focused on a lot of different things. Other diversity studies besides 1000G include "HapMap" and the HGDP, while consortia such as GIANT are focused on using massive datasets to understand the genetics of complex traits like height (e.g. http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3097.html), and things like GTEx are focused on understanding the biology of gene expression and its downstream effects.

I read this one: DNA by James Watson a few years back, and I thought it did a great job of giving a high-level explanation of all of the concepts you listed, except for next-gen sequencing. He also does a great job of giving the historical background of DNA research, which I really enjoyed.

With regard to textbooks, these are the ones I used during my undergraduate career (UCSD Bioinformatics major):

• General Biology: Campbell Biology
  
  
• Genetics: Essentials of Genetics
  
  
• Molecular Biology: Molecular Cell Biology
  
  
• Cell Biology: Same book as Molecular Biology (Molecular Cell Biology)
  
  
• Biochemistry: Lehninger Principles of Biochemistry
  
  I think out of these, the key ones for Bioinformatics are the genetics and molecular biology portions of the General Biology book, then the Genetics book, then the Molecular Biology book. Cell Biology can be useful for understanding the downstream pathways certain "big-name" genes are involved in, but it's information that's very easily google-able. Biochemistry isn't too relevant unless you specifically want to go into metabolomics or something
  
  EDIT: And with regard to reviews, I'm not too sure what "good sources" are; I usually read the Nature Review Journals, but hopefully someone else can chime in!

Consider learning some about the history of bioinformatics. This book is great: https://www.amazon.com/Life-Out-Sequence-Data-Driven-Bioinformatics/dp/022608020X

And, I’m always a huge advocate of the coursera courses. Take a look at the genomic data science and/or bioinformatics specializations on there.

Or, maybe even better, there’s this Harvard bioinformatics course that has lectures and materials available online. It’s really great. Some history, some statistics, and labs in R! Here: https://canvas.harvard.edu/courses/49497/pages/course-schedule

There are a few books out there that are "math refreshers", I think I even saw one titled "Math refresher for scientists and engineers"
https://www.amazon.com/Math-Refresher-Scientists-Engineers-Fanchi/dp/0471757152
I haven't personally read it myself but perhaps you could glance through and see if it interests you. It's pretty easy to find a pdf of as well

Finding a good maths book is harder than I thought. My favorite is a classic, Hamming's 'Methods of Mathematics Applied to Calculus, Probability, and Statistics'

https://www.amazon.com/gp/aw/d/0486439453/ref=mp_s_a_1_1?ie=UTF8&qid=1499896403&sr=8-1&pi=SL75_QL70&keywords=hamming+mathematics+book

It is the introductory part that I found the most exciting as it teaches mathematical thinking.

The most well known quote from Hamming is:

'The purpose of computation is insight, not numbers.'

This applies particularly well to bioinformatics.

Woah woah woah... there is a long and rich history of animal behavior and computer science. Decision making, optimality, and the use of Game Theory to model evolutionarily stable strategies (ESS) is a huge part of the field. When I was in graduate school I took a course that focused on a book (can't remember the exact book right now) and had to build our own models in C++. Do note that this is all heavily mathematical.

Here are some books that show what i'm talking about:

Game-Theoretical Models in Biology - https://www.amazon.com/Game-Theoretical-Biology-Chapman-Mathematical-Computational/dp/1439853215

Game Theory and Animal Behavior - https://global.oup.com/academic/product/game-theory-and-animal-behavior-9780195137903?cc=us&lang=en&

Here is a link to some research programs the professor I took the course from studies https://www.bio.purdue.edu/People/faculty/lucas/decision-making.html

Edit: I remembered the book! "Models of Adaptive Behaviour: An Approach Based on State" -
https://www.amazon.com/Models-Adaptive-Behaviour-Cambridge-Mathematical/dp/0521655390

If you're interested in phylogenetics I can recommend Inferring Phylogenies by Felsenstein. Its well written and easy to understand.

Also, lots of tools are open source and on github, so you could always contribute if you want to spend the time. Good code is always needed.

Bioethics in bioinformatics, especially in a clinical context, is a fairly active area. It can be viewed as a subfield of computing ethics or the ethics of information.. For e.g. see: Ethics in Computing and Information Ethics and Philosophy of Information. As bio-augmentation technologies proliferate, issues surrounding the personal, ethical, legal, and socio-philosophical implications of bioinformation - its generation, use, storage, handling, persistence , ownership - will get quite complex. So, your thoughts may be worth having!

The NIH Bioethics department may be good place to investigate. If you are an experienced professional, just go on and write a paper and ship it off to a journal. If you don't know where to start, put something on paper and find a collaborator that you can work with - they may be found in hospitals, law schools and/or departments of philosophy, social science .... endless options.

NIH Bioethics

Fellowships

I'm self-taught in the CS from a physics background, so maybe I can be helpful.

You'll want a background in discrete mathematics. I found this book to be helpful for self study. I went through the first few chapters by myself. Discrete math is at the heart of computer science. Once you understand the contents of this book well you'll be well-suited to learn things piecemeal. But I would say this should be the entry point.

Building Scientific Apparatus (ISBN 0521878586) does not speak to your question, directly, but it might stimulate your creativity.

I haven't reviewed the book, yet, so I cannot recommend it. It's something that I've been intending to investigate.

The /r/bioinformatics community seems to equate bioinformatics with sequence analysis. I prefer to conceive of bioinformatics a little more broadly than that. However, with respect to Arduino, Raspberry Pi, and Intel Galileo, my mind does tend to go first to do-it-yourself laboratory equipment, as /u/todeedee suggested.

You could make an app for a tablet that would consist of a grid that a 96 well plate, etc. could be placed on. Samples could be listed/entered on the side. The area under each well could light up to help keep track of when you pipetted into the well. In the unused space, calculations, concentrations, steps, etc. could be listed. If you wanted to get really fancy, you could try to make way to roughly calculate concentrations by using an external micro-usb camera (Example) and first making a screen test pattern to calibrate and then placing the camera over each lit well.

I just downloaded Life out of sequence: A Data-driven History of Bioinformatics and am just about done with it. It's history, not technical (and also like 4 years old) but it's really great so far. Good context.

For bioinformatics and one year I would stick with Python (and supplementing it with R). I would look into IPython for interactive use. There is some literature to kickstart your investigation http://www.amazon.de/Bioinformatics-Programming-Using-Python-Biological-ebook/dp/B0043D2D9Q/

Depending on your school, your Masters of bioinformatics may require a lot of exposure to math (Algorithm Theory) and CS in general. Make sure you know what you're getting yourself in, it may be not for the faint of heart though probably survivable.

Are you sure that bioinformatics is what actually will pay your bills later on?