Best bioinformatics books according to redditors
We found 16 Reddit comments discussing the best bioinformatics books. We ranked the 12 resulting products by number of redditors who mentioned them. Here are the top 20.
We found 16 Reddit comments discussing the best bioinformatics books. We ranked the 12 resulting products by number of redditors who mentioned them. Here are the top 20.
On mobile but this is copied and pasted from a comment I made awhile back.
Most of my personal R code for spatial analysis is largely uncommented but I will share some resources I base a lot it off of.
Books:
Websites:
Rpubs:
Python:
Edit: I used to work in public health academia so the links are geared towards that field.
As someone interested in CS you would probably be very interested in genomics/proteomics and bioinformatics, which are really the more applied aspects of molecular biology. Here's a really good introductory textbook that, while a few years old, is very informative and is an interesting read.
Here are some books that I feel have made me better professionally. They tend toward the comp sci side, some are more useful than others.
Lastly, find some open source projects and read their papers, then read their code (and then the paper again, then the code...etc)! Then find their blogs and read those too. Then find them on Twitter and follow them. As others have said, the field is evolving very quickly, so half the battle is information sourcing.
It sounds like you might be interested in systems biology, which emphasizes the use of simulation based on physical models of biological systems. A major goal within the discipline is to bridge the gap between genotype and phenotype.
Check out any of the techniques associated with modeling metabolism, such as flux balance analysis and metabolic flux analysis. Also, Bernhard Palsson has written some good books on the subject. I highly recommend looking at his groups publications.
If you're interested in image analysis, check out the last chapter in PLOS computational biology's online collection: translational bioinformatics. I think the collection really highlights the variety of subdiciplines within computational biology/bioinformatics.
I'm personally excited about developments in proteomics and metabolomics - fields that should provide a better understanding of the chemical processes that are responsible for phenotype.
I hope any of these fields spark your interest!
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.
Getting some experience in the domain (biology) is important. In most cases, you'll be supporting biologists who have PhD and are likely writing grants. So yeah, if you can do that in your school's labs, that's great!
Take a look at this book too:
http://www.amazon.com/Bioinformatics-Sequence-Analysis-David-Mount/dp/0879697121
it explains some science along with algorithms. It's been a while, so there may be more popular books by now.
Depending how serious you are about this field in the long term, I would recommend getting a PhD yourself. I was in this field in the non-profit sector and I was essentially a cost center. I had a masters in bioinformatics, but I couldn't actually write grants to get my own funding. As a result, it wasn't ever a priority to grow my role or focus on improving the organization's bioinformatics capabilities. There were a couple of CS/bioinformatics PhD's who were writing their own grants applications; however, they are much more rare.
It's a really challenging and really rewarding field!!
Lesk's Introduction to Bioinformatics seems to be the text most frequently recommended by bioinformatics courses. It is enjoyable to read and affordable, so I think that would be a good place to start.
I learned from Wackerly which is decent, though I think Devore's presentation is better, but not as deep. Both have plenty of exercises to work with.
Casella and Berger is the modern classic, which is pretty much standard in most graduate stats programs, and I've heard good things about Stat Labs, which uses hands-on projects to illuminate the topics.
It might be a good idea to try and follow something approaching a university curriculum for maths.
For example, my undergrad curriculum went something like this:
The other modules I took were Survival Analysis, which pretty closely followed this book , but may not be of much interest to you, and several operations research based modules: Linear Programming, Nonlinear Programming, and Combinatorial Optimisation. I really enjoyed Nonlinear Programming the most out of those, although doing the other two helped with that.
I also had some intro to computing type modules, learning to use MATLAB, R and Maple (seriously, learn to use MATLAB if you can; Octave is a free alternative); Python is also good to learn. There were some "general skills" type modules too, looking into number theory a bit, and a dissertation, of course, which involved choosing a subject and researching the hell out of it, then trying to make something of it.
I mean, this is a very broad overview. I'd recommend looking at some course structures on university websites and following them in terms of subjects - start with first year, of course. You may have to study a few things you don't like too much, but you'll miss them later on if you don't follow them (seriously, I hated differential equations when I first met them, it wasn't taught well at all and the notes were awful - but they're pretty damn important).
Suggestion: you can apply for a student loan at any age if you've not had one before. Consider going to an actual uni if you're really interested in it? I didn't do A level maths, I did some courses with the Open University instead, which was enough to get me in to uni. Speak to uni admissions hotlines to see what they'll accept.
And finally: do you have any sort of goal to work towards? Is there an area you'd like to one day understand/work in? You can tailor your choice of what to study based on that, if so. If it's just general interest, I'd suggest following the undergrad curriculum idea (mine or pick some unis and look at theirs, they'll all be fairly similar, might have different names for courses or break them into smaller pieces than mine did).
I'm in a similar situation (requiring to be proficient in statistics), and here's what I'm doing.
a. Stats 133 - Computing with Data: A course on using R, SQL, and other technologies useful in statistics.
b. Stats 102 - Intro to Statistics I found multiple versions of this course, but I'm going to pick this one because it uses this interesting book which emphasizes case studies
c. Stats 135 - Concepts of Statistics : More advanced treatment of the same concepts from 102.
d. If you want to brush up on probability, you should look at Stats 101 and Stats 134.
e. After this level, they have a series of electives, such as Stochastic Processes (Stats 150), Linear Modelling Lab (151A and 151B), Sampling Surveys Lab (152), Time Series Lab (153), Game Theory (155), and seminars.
The classes don't have videos or audios, but they have syllabuses, lecture notes and assignments. So far I've found them to be more than sufficient.
This. I'd like to see anyone pass a class with this as the textbook without reading it.
I think it's from last year... Here's the link at Amazon
Here is the mobile version of your link
Smells like bullshit. Let me check those prices.
Acta Philosophorum The First Journal of Philosophy: $270
Encyclopedia of International Media and Communications: $283
Management Science An Anthology: $37
History of Early Film: $224
Biostatistical Genetics and Genetic Epidemiology: $140
Companion Encyclopedia of Psychology: $40
Feminism and Politics: $4
Concepts and Design of Chemical Reactors: $149
Advanced Semiconductor and Organic Nano-Techniques: $195
Ethics in Business and Economics: $4
Environment in the New Global Economy: $400
Solid State Chemistry and Its Applications: $40
Of course, they're still a rip off.