Answers.Body: <p>The formal answer is O'Reilly "Bioinformatics programming, using Python". Check out the classic O'Reilly texts on "Mastering Perl for bioinformatics", and possibly"Beginning Perl for bioinformatics" (which is likely far too basic), which establish some core principles and have a solid reputation in teaching. However, the Perl/Python thing might get confusing (I dunno).</p>
  
  <p>If you are statistics orientated at a guess, O'Reilly's, Bioinformatics data skills... I don't know, but something that would focus on R.</p>
  
  <p>The real goal is core-technical competance because the biological understanding will be taught by your supervisor. Thereafter you might turn your attention to teaming up with a lab for project training.</p>
  
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Answers.CreationDate: 2019-05-13T07:31:29.997Z
Answers.Text: I'd advise against learning Perl for bio-informatics these days. The only places I've run into Perl in bio-informatics is in legacy code.
Answers.UserDisplayName: Pallie
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Answers.Text: @Pallie you really shouldn't ever advise anyone against learning something. Especially not something as useful as Perl. Learning is always good. In any case, while Perl is indeed not as popular as it used to be, it's still around (have a look at the [Ensemble API](http://ensemblgenomes.org/info/access/api), for instance), and it's still useful. And the specific language used is only a minor detail: whatever language you learn, you need to learn the same general concepts.
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Answers.LastEditDate: 2019-05-12T09:28:28.910Z
Answers.UserDisplayName: M__
Body: <p>I know this is a broad question and I'm sorry if this is not the right place to post (this is my first post ever), but is there some sort of roadmap for bioinformatics?</p>
  
  <p>I'm an undergraduate student and I have a strong background in statistics, computer science, and molecular biology so I feel like I should be able to get around, but every time I talk or read about bioinformatics I feel lost. It's such a vast area that I don't even know where to begin or what even exists.
  Is there some kind of all-encompassing textbook or roadmap for me to follow?</p>
  
  <p>Most textbooks I could find expend way to much time explaining really basic stuff like what is a cell and how to program and I feel they fail to convey the bigger picture of bioinformatics. Things like what exists in the area, what are the current technologies, conventions, and challenges that we face. What should I know in order to call myself a bioinformatician?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-12T05:14:04.547Z
Comments.Text: Welcome to the forum @B-Aral. How good is your Python? A good programmer (eg. could you implement method chaining?) who is keen on extending their biological understanding is a fantastic start. However, if you are statistics orientated R is a better option. Thereafter you need to pick an area and hone your skills within that area, Python vs. R will shape which area on some instances. It doesn't matter if you're projects, post-grad training don't fit, core R or Python skills are the primary skill set IMO.
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-12T12:57:19.533Z
Comments.Text: Indeed, This is an extremely broad question. There would be great disagreement about what even bioinformatics means among people in this community and I think that the only common denominator of all of us is at least a basic knowledge of programming. So, is there a way how narrow your question? Why do you want to learn bioinformatics? Are you interested in sequence analysis (the largest bioinfo community, but certainly not the only)?
Comments.UserDisplayName: Kamil S Jaron
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-13T09:04:20.870Z
Comments.Text: Have you looked at other sites? Like this [post](https://www.biostars.org/p/379314/) with links to other relevant discussions but you can find [some](https://www.biostars.org/p/98983/) other posts by searching around.
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-05-13T10:00:57.430Z
Comments.Text: I find Rosalind (http://rosalind.info/problems/locations/) a great resource, which in addition to programming exercises will explain biology and common bioinformatics algorithms.
Comments.UserDisplayName: Wouter De Coster
Tags: <python>
Title: How to learn/get started bioinformatics?
ThreadText: # How to learn/get started bioinformatics?
  
  (Asked by: username_0 on 2019-05-12)
  
  I know this is a broad question and I'm sorry if this is not the right place to post (this is my first post ever), but is there some sort of roadmap for bioinformatics?
  
  I'm an undergraduate student and I have a strong background in statistics, computer science, and molecular biology so I feel like I should be able to get around, but every time I talk or read about bioinformatics I feel lost. It's such a vast area that I don't even know where to begin or what even exists.
  Is there some kind of all-encompassing textbook or roadmap for me to follow?
  
  Most textbooks I could find expend way to much time explaining really basic stuff like what is a cell and how to program and I feel they fail to convey the bigger picture of bioinformatics. Things like what exists in the area, what are the current technologies, conventions, and challenges that we face. What should I know in order to call myself a bioinformatician?
  
  **Tags:** python
  
  > **Comments:**
  > **username_1**: Welcome to the forum @B-Aral. How good is your Python? A good programmer (eg. could you implement method chaining?) who is keen on extending their biological understanding is a fantastic start. However, if you are statistics orientated R is a better option. Thereafter you need to pick an area and hone your skills within that area, Python vs. R will shape which area on some instances. It doesn't matter if you're projects, post-grad training don't fit, core R or Python skills are the primary skill set IMO.  
  > **username_2**: Indeed, This is an extremely broad question. There would be great disagreement about what even bioinformatics means among people in this community and I think that the only common denominator of all of us is at least a basic knowledge of programming. So, is there a way how narrow your question? Why do you want to learn bioinformatics? Are you interested in sequence analysis (the largest bioinfo community, but certainly not the only)?  
  > **username_3**: Have you looked at other sites? Like this [post](https://www.biostars.org/p/379314/) with links to other relevant discussions but you can find [some](https://www.biostars.org/p/98983/) other posts by searching around.  
  > **username_4**: I find Rosalind (http://rosalind.info/problems/locations/) a great resource, which in addition to programming exercises will explain biology and common bioinformatics algorithms.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-05-12. Score: 2
  
  The formal answer is O'Reilly "Bioinformatics programming, using Python". Check out the classic O'Reilly texts on "Mastering Perl for bioinformatics", and possibly"Beginning Perl for bioinformatics" (which is likely far too basic), which establish some core principles and have a solid reputation in teaching. However, the Perl/Python thing might get confusing (I dunno).
  
  If you are statistics orientated at a guess, O'Reilly's, Bioinformatics data skills... I don't know, but something that would focus on R.
  
  The real goal is core-technical competance because the biological understanding will be taught by your supervisor. Thereafter you might turn your attention to teaming up with a lab for project training.
  
  > **Comments:**
  > **username_5**: I'd advise against learning Perl for bio-informatics these days. The only places I've run into Perl in bio-informatics is in legacy code.  
  > **username_6**: @username_5 you really shouldn't ever advise anyone against learning something. Especially not something as useful as Perl. Learning is always good. In any case, while Perl is indeed not as popular as it used to be, it's still around (have a look at the [Ensemble API](http://ensemblgenomes.org/info/access/api), for instance), and it's still useful. And the specific language used is only a minor detail: whatever language you learn, you need to learn the same general concepts.  
  
  ---
  
  