Best 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.

What a lab tech job does is build your resume by you doing research. Research is the ultimate goal of a bioinformatics career regardless of whether you are in academia or in the private sector. You will be doing research of some kind. I don't know of any bioinformatics jobs that are outside of science. You'd be just a regular statistician or computer programmer.

Anyone who wants to invest time and money into you wants to know you are capable of doing research and they don't know you outside of your resume. You're also competing against many, many people looking for graduate school or a job. People you have never met from all over your country/the world (thanks to the internet and online job postings). Many of them have better grades and better looking resumes than you, so you really have to stand out in something if you want to get hired in today's world.

There's no law stating you have to immediately grab your professional degree or research degree after graduating undergrad. And what you seem to not understand but will after doing your first real job hunt is that you must go through entry level jobs. Starting your career outside of an entry-level job these days is like winning the lottery. It's not going to happen. Don't bank on it.

The point of an entry level job like a laboratory tech is to get adjusted to the research environment by first taking care of the things that the PhD candidates and postdocs need done around the lab. You'll be running their experiments. If you have a good PI and you let them know what your goals are, they should let you start doing your own basic research projects. These are invaluable and will look great on your graduate school application or job application. It shows that you're able to apply your degree to actually doing useful work, which is what makes people offering the higher-level, better-paying jobs want to hire you.

Generally a masters degree lets you be a lab manager or get more senior research team positions which in the US usually gives you a pretty decent salary and do some of your own research, or work in a private firm and get more senior positions with time. You'll always start at the bottom unless you network in.

Additionally, for any PhD program worth a damn, they'll interview you and ask what your goals are and get a general idea of how competent you are. If you go into a PhD candidate interview and don't know what bioinformatics actually is, how the science world actually works, and some specifics of why you actually want to pursue the degree besides $$, they'll be more likely to see you as a potential burnout and more than likely won't pick you over someone with specific goals and a good idea of the industry and academic climate.

If you had said "I'm sure I'll get into a grad school program" I wouldn't be telling you this. You'd get a lot of experience there and it would be valuable to adding to your resume. But you are doubtful and I'm telling you what you don't want to hear but it's how life works for everyone who doesn't immediately get into grad school.

From what you wrote, it seems you are in the position a lot of about-to-be-graduates are in. They grew up with the promise of college=6 figure salary but that truth ended in the early 2000s after the dot com bubble burst (unless you're a mechanical, electrical, or petroleum engineer). Don't worry too much about money. Everyone gets by with what they have and you really don't end up with that much money after taxes when you're making the big bucks. It's more work and more stress and you're just giving it back to the government in the end. You obviously chose biochemistry for a reason, probably because the molecular world is fascinating. There's so much cool stuff you can do with your degree, money shouldn't be your main objective when looking for careers.

TL;DR: It's good that you're asking questions. If you're really really serious about pursuing a bioinformatics career, try picking up a book or two before proceeding any farther. It'll only take a day or two to power through an introduction to the field and some fundamental concepts. One I really like was one of my textbooks back in undergrad: Fundamental Concepts of Bioinformatics by Dan Krane & Michael Raymer

Edit: If you get that book get a used copy. New is an outrageous price.

Well, not to sound crass, but you are going to get good at statistics if you are going to interpret microarrays. You might think of microarrays in two parts, getting meaning from the signal, and getting biological meaning from what you that. Both parts require strong statistical knowledge, and with out a good understanding of the process you will really get over your head.

So if someone did the front end analysis (meaning from signal) then you should have a set of deferentially expressed genes, and a bunch of graphs describing the data where they got the DE genes. If your job is to find the biological meaning in the output of this data, then you do not need to know MA / volcano / intensity vs intensity plots, et cetera. You do need to know how to run programs like GSEA (which requires knowledge of the hypergeometric distribution, check Wikipedia, they have a really good explanation of it). There are massive suites of programs to do this, and are pretty study specific on what to do.

If you are looking at the plots you mentioned, you are looking at the front end, asking 'what does my data say?' So you do need to ask yourself, what task are you really trying to get done. If you are looking at MA plots, it is because you are interested in how noisy your data is and if you need to weight each array separately when you go to find DE genes. There are a lot of things that you don't need to fully understand (estimating bayesian priors, how to set up a linear model by hand), but you do need to know what the point each phase of the analysis.
BioConductor Case Studies is a good starting point
Other than that, like SupaFurry said, the bioconductor forums are great, but you will be searching through archived discussions, but that is the main way we get things done, lots of digging.

Hope that helps!

Sure, I can tell you how I did it.

First step, find companies/jobs you might be interested in. Biospace is a good place to start, but there's lots of other resources. Ask friends in industry, network at conferences, etc. Find a decent recruiting company that can hook you up with companies you've never heard of. Mirus Search was pretty good to me, and found a company/role that ended up giving me an offer. Figure out what you want --- small company or big company, public or private, location, field of work, and so forth. You're aiming for a list of at least a couple dozen companies and roles that look worth putting more work into researching. I stuck 'em all in a spreadsheet. A common error at this point is to miss lots of potentially good small companies, especially small private companies.

Second, research the company and the job. The goal here is to be able to answer the question "Why are you interested in this company" and "Why do you think you're a good fit for this job"? I had a row for each company/job and literally wrote the answers to these questions in a cell of my spreadsheet so I had them immediately on hand. If you can't answer either of those questions after some research, throw that company/job out.

Third, prepare resumes and cover letters for each one. Cover letters should be just a couple of sentences and personalized to each company. Mash them up out of your answers to the previous research. Again, I pasted this into my spreadsheet. Resumes should be short (1 page, maybe a second page for publications) and contain only stuff relevant to the job you're applying for. If you're applying to multiple different kinds of jobs, emphasize/cut different things. For any kind of job involving programming, link to your github/bitbucket/whatever account, assuming you've got something decent up there. Put something decent up there if you don't have it, just drop all your academic work in. I was told after being hired that my code sample from sourceforge is what got me the interview in the first place.

Fourth, send them off. I colored rows of my spreadsheet to keep track of everything. Blue for "sent off", green for "phone interview", red for "rejected". If you get rejected, be nice and say thanks. I got at least three follow-ups from companies about three months later saying that actually, they did have an opening for someone now. Expect a lot of rejections.

Fifth, prep for interviews. Expect technical questions. I got a lot of statistics questions and some programming questions. Prep for them. I spent a couple of months working through books like Cracking the Coding Interview and practicing questions on a whiteboard. I borrowed a whiteboard from work and did them at home on the board out loud. It helped a lot.

I started with ~30 companies of interest, had phone interviews with ~5, on-site interviews with 3, and offers from 2. Good luck! It's a lot of work.

I am a working R&D bioinformatician, and for the most part the people advising you are correct. There are jobs, and they do pay well. Doesn't pay as well as computer science, but it is a great use of a bio degree. Bioinformatics as a job is much more like computer science than wet lab biology. There are some really great things going on with this field - and with the aging population, I think it is a growth industry.

As for what you need to get a job:
You absolutely have to write code. You should learn R. You should also know a language like Perl / Python (my preference). You should also know your way around linux - at least a little bit. Statistics / Data Analysis classes are available on coursera.

For a good career in bioinformatics, I think the best thing to do would be to pursue a PhD in biostatistics.

edit:
This is a great book to pick up python for biologists.
http://www.amazon.com/Bioinformatics-Programming-Using-Python-Biological/dp/059615450X

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.

OK, well since you already know what species you're interested in, your next step would probably be to choose a gene to use. So, take your organisms of interest and see what's available on GenBank for them. It looks like the choroplast rbcL gene might be a good choice - it seems to be a barcode gene and there are multiple specimens of Avicennia germinans and Rhizophora mangle available on genbank. Unfortunately, it doesn't look like anyone's sequenced Maytenus phyllanthoides yet, but there are some Maytenus segovarium rbcL sequences on GenBank, so that might be a good substitute. Another approach would be to search for papers on the phylogeny of eudicots and see what genes authors in your area use - it tends to vary among different organisms, but usually there will be one or two widely sequenced genes. You can also combine two or more genes together for a more robust phylogeny. At this stage, I would probably search for the gene name "rbcL" (sometimes people use different names for the same gene, but GenBank usually knows all the synonyms, to be safe though, you can try searching for alternate names also) and the group I'm interested in e.g. "eudicotyledons" (you might call this group something different, but GenBank's naming system is very conservative, and you've got to use the name GenBank recognises). Now, that seems to have turned up around 47,000 so this approach probably won't work very well here, but I'd normally just download everything and then trim off the ones I don't want later. Some of the results will be different specimens of the same species, some might not be the rbcL gene, or at least not the part of it I want. There might also be a mix of complete genes and partial genes, but this is OK. It looks like there's a lot of partial rbcL genes that are exactly 583 bp long - they're almost certainly the same section of the same gene. Some are a bit shorter (e.g. 549 bp)- that could be due to actual deletions in the sequence or the use of different primers by the people who sequenced it. It could also mean that it's a different section of the gene, but that's fairly unlikely and should be clear when it doesn't align to the sequences of the other species - or to a different section of any complete genes.

So, after you've chosen your species and your gene/s you need to start inferring phylogenies. For help with that, I'd suggest seeing if your library has a copy of Phylogenetic Trees Made Easy.

okay, so it looks like you won't need any more machine learning related knowledge then, and most likely you already passed all your algorithms courses too, so you won't need to study that in more detail either. Getting used to the unix command line should be most useful for you at this point then, as DroDro already pointed out - learning to write small bash scripts and using tools like awk, sed etc. might come in very handy later., and maybe you want to look at R in more detail than you did so far too, as that's something that will continue to be useful for years to come.
These are two introductions that most likely contain more details than you need, but might be good for looking things up. regarding Linux: http://www.tldp.org/LDP/intro-linux/html/ and shell scripting, including a short introduction to awk and sed: http://www.tldp.org/LDP/abs/html/
For a more basic introduction to all the necessary computer-related skills, I'd recommend this book https://www.amazon.com/gp/product/1449367372 It explains all the basics you need to know about unix, shell scripts, useful things like git, useful tools, bioinformatics pipelines and contains a short intro to R etc., isn't too over the top and might be good if you're coming from a biology background and aren't too familiar with those yet.

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)

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!

You're already on the right track getting started with Python, it is the most popular language currently. I would also highly recommend getting experience working in a linux environment, so either macOS or Linux, and getting comfortable working through the terminal. To round off your computational skills, I think that R would be a very good second language to learn. I'm currently using R more than Python for my work, it's much better to use when performing statistical analysis.

You should also try and get a good understanding of the biology behind the data you'll be working with. I think that THIS BOOK does a very good job at covering most concepts you're like to encounter in the field. It's what much of the biology portion of my graduate program was based upon.

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
  

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!

Oh right. I slightly misunderstood what you were looking for.

This may or may not be helpful but here's another recommendation:

https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

While this book is pretty old and does not cover newer technologies and algorithms, I found it extremely helpful in understanding biological principles related to genetics and molecular evolution.

In this genomics era, we know so much more than just genetics now of course. But I mostly learned about genomics by reading tons of review papers, not a textbook. Once you study a bit on basic biology, I think reading review papers is the way to go if you want to delve into a more specific topic.

If you're interested in improving your programming skills, I recommend [python for biologists](https://www.amazon.com/Python-Biologists-complete-programming-beginners/dp/1492346136/ref=sr_1_2?ie=UTF8&qid=1523974147&sr=8-2&keywords=python+bioinformatics).

If you want to browse widely used genomic/bioinformatic resources then look at NCBI, Ensembl, and UCSC.

If you want to try some bioinformatics problems, then see Rosalind.

If you want to learn biology, then buy textbooks on genetics/molecular biology. There are many, I recommend [this] (https://www.amazon.com/Human-Evolutionary-Genetics-Origins-Peoples/dp/0815341857) for human evolution.

If you want to learn about methods and sequence analysis, then [Biological Sequence Analysis] (https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713) is excellent.

If you want to explore widely used bioinformatics tools, then start with BLAST if you don't know it already.

I'd start with this for a good general overview that also carries many great recommendations: Bioinformatics Data Skills, by Vince Buffalo.

A very good book: http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068

Campell Biology is generally the number one go to for intro bio. My AP class, and intro class in college used it.

https://www.amazon.com/Campbell-Biology-10th-Jane-Reece/dp/0321775651

For more molecular stuff, molecular biology of the cell is fairly popular:

https://www.amazon.com/Molecular-Biology-Cell-Bruce-Alberts/dp/0815344325/ref=pd_lpo_sbs_14_t_0?_encoding=UTF8&psc=1&refRID=D9ZRY4BKB4ECZ2PMQRRJ

Barry Halls Phylogenentic Trees Made Easy Link

While I'm not sure if there are any really good beginner books for bioinformatics or genetics, this book is absolutely incredible for learning how to use R and working with applied statistics: Book

The Bioinformatics Data Skills book is decent to start off after you acquire basic Unix command line skills along with some familiarity with a scripting language (preferably Python).

Generally the Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins is a preferred introductory textbook.

There are lots of online resources. To complement the other ones linked in the comments:

Learn R, in R

Programming: Pick up Python

Programming tools: Adventures with R

Due to non-existent biology background, you could start by reading Campbell Biology and Alberts Molecular Biology of the Cell.

You can try this one:

https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713

It introduces one of the key problems in bioinformatics and should be fairly readable for someone of your background. It primarily adresses your first bullet, and does not go into many details about implementations,