(Part 2) Top products from r/bioinformatics

Generally speaking, the following are the main considerations when evaluating a computer:

• CPU: CPUs are generally the primary performant components of a computer. That means that, for the average use case of, say, browsing the internet and word processing, CPU speed is roughly synonymous with computer speed. That doesn't hold in all cases (and really falls apart for niche stuff like machine learning), but it's a decent rule of thumb.
  
  
• RAM: RAM is basically the component that allows you to have lots of stuff open at once. If you have 2GB of RAM, trying to run Chrome on top of Windows 10 will probably crash your computer almost immediately. If you have 32-64GB of RAM, you can probably run Photoshop, Chrome, and some high-performance video game like The Witcher III all at the same time without issue.
  
  
• RAM is also the component that allows you to manipulate larger datasets. If you're processing, say, single cell rnaSeq data locally, you're probably going to need a fair bit of ram.
  
  
• Disk: Most people think of disk (AKA hard drive) in terms of storage, but performance can be a factor here. Basically there are two types of disk; SSDs are more performant but have smaller storage, and HDDs have more storage but are less performant. An SSD will allow your machine to boot and open programs quicker, but for the price won't have as much storage space as an HDD. Often what people will do is boot off of a smaller SSD (say 256GB) for speed, and store their data on a 1TB HDD to get the best of both worlds.
  
  
• GPU: GPUs are (again, generally speaking) good for two things: display/graphics and hugely parallelizable tasks. In layman's terms: video games and deep learning. They can easily be the single most expensive component of a machine, but a good GPU will crank out tasks that require hours of CPU time in minutes or even seconds.
  
  All that being said, without having a very good idea of what your day-to-day computing needs are, and what the heaviest-duty tasks you'll be doing will be, it's hard to know what to recommend.
  
  If you have very good computing infrastructure, and won't need to do any development or heavy-lifting locally, then most of what you'll be doing with your personal computer is browsing the Internet and word processing. For that I honestly recommend a budget option, because it's difficult to tell the difference between a $500 machine and a $2000 machine with those tasks. A middle of the road Chromebook should do just fine, as will something like a Dell Latitude -- which is my personal machine, and I can recommend for both the performance/reliability of the computer itself and the efficacy of the refurbishing process. If you want a little more oomph and have a little more cash, the baseline MacBook Air should do just fine as well.
  
  If you'll need to be doing development locally, and mild-to-moderate compute -- for instance if you're taking computational classes that require you to do homework on your own machine -- the best bang for your buck in a single machine is probably a MacBook Pro.
  
  If you're pretty much on your own for all of your computing needs -- research that'll involve large datasets and/or ML, coursework, etc -- and/or you've got money to burn, I recommend building your own desktop for heavy computing and getting a budget laptop for mobility (remote access can be set up to give you access to your desktop from your laptop). This is where selecting components and knowing what you're doing is imperative, so this recommendation is totally spitballing, but something like this build should be able to tackle anything you can reasonably be expected to do on your own.
  
  Bear in mind again that your needs may vary significantly from the needs that build is designed to address -- and that the price of video cards/GPUs is way inflated at the moment -- but that's a decent skeleton for a high-performance machine, and the walkthrough of setting it up is pretty good.
  
  If you can give me more specifics I can try to refine my recommendations, but without any context I think this post should be a fair guideline as you're shopping for a machine.

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.

• Bioinformatics: An Active Learning Approach: Excellent exercises and references. I think most chapters evolved out of blog posts if you don't want to buy the book.
  
• Higher Order Perl: I like perl to start with, so your mileage may vary. But learning how to implement an iterator in a language that doesn't have that concept was enlightening. There is a similar book for Python but I don't remember what it's called. Also, you are likely to run into some Perl at some point.
  
• SICP: Power through it, it's worth it. I did not do all the exercises, but do at least some of the first ones to get the ideas behind Scheme. Free PDFs exist, also free youtube vids.
  
• The C Programming Language: Everyone should know at least a little C. Plus so much has evolved from it that it helps to understand your foundations. Free PDFs exist
  
• The Rust Programming Language: Read this after the C book and after SICP. It explains a lot of complex topics very well, even if you don't use Rust. And by the end, you will want to use Rust! :) It's free!
  
  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.

Chapter 7 of Chris Bishop's book Pattern Recognition and Machine Learning has a nice intro to SVMs.

Here is a list of papers where SVMs were used in a computational biology

> Gene Function from microarray expression data
>
> Knowledge-based analysis of microarray gene expression data by using support vector machines, Michael P. S. Brown, William Noble Grundy, David Lin, Nello Cristianini, Charles Walsh Sugnet, Terence S. Furey, Manuel Ares, Jr., David Haussler, Proc. Natl. Acad. Sci. USA, vol. 97, pages 262-267
> pdf
> http://www.pnas.org/cgi/reprint/97/1/262.pdf
>
> Support Vector Machine Classification of Microarray Gene Expression Data, Michael P. S. Brown William Noble Grundy, David Lin, Nello Cristianini, Charles Sugnet, Manuel Ares, Jr., David Haussler
> ps.gz
> http://www.cse.ucsc.edu/research/compbio/genex/genex.ps
>
> Gene functional classification from heterogeneous data Paul Pavlidis, Jason Weston, Jinsong Cai and William Noble Grundy, Proceedings of RECOMB 2001
> pdf
> http://www.cs.columbia.edu/compbio/exp-phylo/exp-phylo.pdf
>
> Cancer Tissue classification
> from microarray expression data, and gene selection:
>
> Support vector machine classification of microarray data, S. Mukherjee, P. Tamayo, J.P. Mesirov, D. Slonim, A. Verri, and T. Poggio, Technical Report 182, AI Memo 1676, CBCL, 1999.
> ps.gz
> PS file here
>
> Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data, Terrence S. Furey, Nigel Duffy, Nello Cristianini, David Bednarski, Michel Schummer, and David Haussler, Bioinformatics. 2000, 16(10):906-914.
> pdf
> http://bioinformatics.oupjournals.org/cgi/reprint/16/10/906.pdf
>
> Gene Selection for Cancer Classification using Support Vector Machines, I. Guyon, J. Weston, S. Barnhill and V. Vapnik, Machine Learning 46(1/3): 389-422, January 2002
> pdf
> http://homepages.nyu.edu/~jaw281/genesel.pdf
>
> Molecular classification of multiple tumor types ( C. Yeang, S. Ramaswamy, P. Tamayo, Sayan Mukerjee, R. Rifkin, M Angelo, M. Reich, E. Lander, J. Mesirov, and T. Golub) Intelligent Systems in Molecular Biology
>
> Combining HMM and SVM : the Fisher Kernel
>
> Exploiting generative models in discriminative classifiers, T. Jaakkola and D. Haussler, Preprint, Dept. of Computer Science, Univ. of California, 1998
> ps.gz
> http://www.cse.ucsc.edu/research/ml/papers/Jaakola.ps
>
> A discrimitive framework for detecting remote protein homologies, T. Jaakkola, M. Diekhans, and D. Haussler, Journal of Computational Biology, Vol. 7 No. 1,2 pp. 95-114, (2000)
> ps.gz
> PS file here
>
> Classifying G-Protein Coupled Receptors with Support Vector Machines, Rachel Karchin, Master's Thesis, June 2000
> ps.gz
> PSgz here
>
> The Fisher Kernel for classification of genes
>
> Promoter region-based classification of genes, Paul Pavlidis, Terrence S. Furey, Muriel Liberto, David Haussler and William Noble Grundy, Proceedings of the Pacific Symposium on Biocomputing, January 3-7, 2001. pp. 151-163.
> pdf
> http://www.cs.columbia.edu/~bgrundy/papers/prom-svm.pdf
>
> String Matching Kernels
>
> David Haussler: "Convolution kernels on discrete structures"
> ps.gz
> Chris Watkins: "Dynamic alignment kernels"
> ps.gz
> J.-P. Vert; "Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings"
> pdf
>
> Translation initiation site recognition in DNA
>
> Engineering support vector machine kernels that recognize translation initiation sites, A. Zien, G. Ratsch, S. Mika, B. Scholkopf, T. Lengauer, and K.-R. Muller, BioInformatics, 16(9):799-807, 2000.
> pdf.gz
> http://bioinformatics.oupjournals.org/cgi/reprint/16/9/799.pdf
>
> Protein fold recognition
>
> Multi-class protein fold recognition using support vector machines and neural networks, Chris Ding and Inna Dubchak, Bioinformatics, 17:349-358, 2001
> ps.gz
> http://www.kernel-machines.org/papers/upload_4192_bioinfo.ps
>
> Support Vector Machines for predicting protein structural class Yu-Dong Cai*1 , Xiao-Jun Liu 2 , Xue-biao Xu 3 and Guo-Ping Zhou 4
> BMC Bioinformatics (2001) 2:3
> http://www.biomedcentral.com/content/pdf/1471-2105-2-3.pdf
>
> The spectrum kernel: A string kernel for SVM protein classification Christina Leslie, Eleazar Eskin and William Stafford Noble Proceedings of the Pacific Symposium on Biocomputing, 2002
> http://www.cs.columbia.edu/~bgrundy/papers/spectrum.html
>
> Protein-protein interactions
>
> Predicting protein-protein interactions from primary structure w, Joel R. Bock and David A. Gough, Bioinformatics 2001 17: 455-460
> pdf
> http://bioinformatics.oupjournals.org/cgi/reprint/17/5/455.pdf
>
> Protein secondary structure prediction
>
> A Novel Method of Protein Secondary Structure Prediction with High Segment Overlap Measure: Support Vector Machine Approach, Sujun Hua and Zhirong Sun, Journal of Molecular Biology, vol. 308 n.2, pages 397-407, April 2001.
>
> Protein Localization
>
>
> Sujun Hua and Zhirong Sun Support vector machine approach for protein subcellular localization prediction Bioinformatics 2001 17: 721-728
>
>
> Various
>
> Rapid discrimination among individual DNA hairpin molecules at single-nucleotide resolution using an ion channel
> Wenonah Vercoutere, Stephen Winters-Hilt, Hugh Olsen, David Deamer, David Haussler, Mark Akeson
> Nature Biotechnology 19, 248 - 252 (01 Mar 2001)
>
> Making the most of microarray data
> Terry Gaasterland, Stefan Bekiranov
> Nature Genetics 24, 204 - 206 (01 Mar 2000)

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
  

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

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'd recommend focusing more on basic biology textbooks

This is a really good idea. I would recommend Campbell Biology for general biology at the intro level and Concepts of Genetics for genetics

Yeah, I think it's pretty wild stuff. It just blows my mind that the biochemical network within a cell can be influenced by the emergent properties of the network itself (instead of any physical or chemical properties). The behavior of a network translates across applications - it's weird. Here's what got me into it - it's a pretty approachable read for the topic.

> What kind of testing do you use for data analysis (ie when you don't know what the result should be)? Do you go so far as to make fake data where you know the answer?

Yes. You craft fake data which will yield a known outcome. Then you run the analysis. Then you compare the results of analysis against the expected outcome. This can become tedious, so you automate the testing with scripts or frameworks for that purpose. In general:

• Craft data sets you know will cause analysis to succeed.
  
• Craft data sets you know will cause analysis to fail.
  
• Craft data sets you believe will test the boundaries of analysis.
  
  Perhaps you are further along in your understanding, but it sounds like you may still be trying to wrap your mind around the entire woolly concept of testing. If that is the case, you might benefit from How to Break Software. Unfortunately, that book is not about data analysis, specifically, but testing analytical code is a special case of testing other kinds of code.

1. Download RStudio
   
2. Try online tutorials like this, this, here, and this pdf.
   
3. R can produce amazingly ugly or beautiful graphs. ggplot2 is my favorite and these books 1,2,3 will give you solid foundation on how to use it.
   
4. Are you just interested in RNAseq or ChIPseq? Are you running the entire bioinformatic pipeline from QC through to RPKM/counts generation? This blog post can give you a decent idea on a basic workflow for differential gene expression analysis. Most of that is R and unix based tools. But there is also a lot else out there that you can google and then learn from.
   
5. Keep in mind that any error message that you can't figure out has already happened to many other people. A google search will find you a stack overflow or biostars post asking how to solve whatever problem you have encounter. So don't be discourage when you can't figure out something.

Many, I would recommend this book in particular: http://www.amazon.co.uk/Bioinformatics-Learning-Approach-Adaptive-Computation/dp/026202506X

There's a book about it, called Rivals

\>> Authors getting shafted of major credits

What about Raymond Gosling, Franklin's Phd student who took Photo 51?

My stats introduction was with 'Statistics for the life sciences', by Samuels, Witmer, Schaffner: https://www.amazon.com/Statistics-Life-Sciences-Myra-Samuels/dp/0321989589.

If you want more entry-level ML stuff, go for 'Elements of statistical learning' that /u/gamazeps also linked.

Remembering to distinguish bioinformatics from biomedical informatics, if you are looking for the latter, I'd suggest extending what you already know - this is a good Link to that start. https://www.coursera.org/learn/clinical-data-management

The reference book on biomedical informatics is the Shortliffe text: https://www.amazon.com/Biomedical-Informatics-Computer-Applications-Biomedicine/dp/0387289860

If you have zero stats background, try getting a textbook from a college level intro course (I used this one) and going through the chapters and doing the problems. If you are past the intro level, find a higher level course text.