Top products from r/bioinformatics

We found 39 product mentions on r/bioinformatics. We ranked the 73 resulting products by number of redditors who mentioned them. Here are the top 20.

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Top comments that mention products on r/bioinformatics:

u/LichJesus · 6 pointsr/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.
u/IllMatt · 5 pointsr/bioinformatics

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


u/TotalPerspective · 5 pointsr/bioinformatics

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.
u/BanefulPanda · 3 pointsr/bioinformatics

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.

u/mina-harker · 2 pointsr/bioinformatics

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.

u/BRAF-V600E · 3 pointsr/bioinformatics

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.

u/g0lmix · 9 pointsr/bioinformatics

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
u/niemasd · 5 pointsr/bioinformatics

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!
u/Le_petit_Nicolas · 1 pointr/bioinformatics

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

u/jottermeow · 4 pointsr/bioinformatics

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.

u/esqueletohrs · 2 pointsr/bioinformatics

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.

u/BrianCalves · 3 pointsr/bioinformatics

> 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.
u/mutationalMeltdown · 3 pointsr/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.

u/I_am_not_at_work · 19 pointsr/bioinformatics
  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.
u/EmergencyNewspaper · 0 pointsr/bioinformatics

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

u/ebenezer_caesar · 1 pointr/bioinformatics

Biological Sequence Analysis is a good book to have in your library as well.

u/biologyguy · 1 pointr/bioinformatics

Practical Computing for Biologists is a friendly introduction to the basics of using your computer to do things it didn't come pre-programmed to do.
https://www.amazon.com/Practical-Computing-Biologists-Steven-Haddock/dp/0878933913

u/drewinseries · 3 pointsr/bioinformatics

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

u/Moklomi · 1 pointr/bioinformatics

Barry Halls Phylogenentic Trees Made Easy Link

u/StreetLouis · 6 pointsr/bioinformatics

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

u/aristotle_of_stagira · 1 pointr/bioinformatics

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


u/tchnl · 2 pointsr/bioinformatics

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.

u/5heikki · 10 pointsr/bioinformatics

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

u/phryna · 2 pointsr/bioinformatics

I keep a copy of Practical Computing for Biologists in my lab as a reference for students startiong out working with genomic data.

u/Homeothermus · 2 pointsr/bioinformatics

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,

u/Simsmac · 1 pointr/bioinformatics

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.