text: user4803: I have a strong mathematical and computer background (also with machine learning) and would like to switch to bioinformatics. I know a fair amount of biology. But I find it hard to find real world practical training in this field. So I would like to try to replicate some bioinformatics papers (with their provided data). Good beginner papers for me would be shorter ones where the biological problem is well stated and the problem is also fitted in to the bigger scope (or is just easily understandable). Do you have any papers to recommend?
  
  user2108: Bioinformatics is a big and very heterogeneous field with a lot of variation, so it's hard to recommend something that covers everything. That said, I think the best you'll do is to build familiarity with some of the common tools with some canned analyses. 
  One possible option would be some of the scRNA-seq workflows from the Pachter group, they seem to be fairly conscientious about documentation. Here is one such canned analysis, here is the paper, here is a blog post about it that suggests next steps and discusses at a high level. 
  scRNA-seq is just what's been trendy for a few years, next year it will be something different. But it at least gets you familiar with some of the file formats and concepts such as alignment, etc.
  Hope that helps.
  
original_text: 4803: <p>I have a strong mathematical and computer background (also with machine learning) and would like to switch to bioinformatics. I know a fair amount of biology. But I find it hard to find real world practical training in this field. So I would like to try to replicate some bioinformatics papers (with their provided data). Good beginner papers for me would be shorter ones where the biological problem is well stated and the problem is also fitted in to the bigger scope (or is just easily understandable). Do you have any papers to recommend?</p>
   2108: <p>Bioinformatics is a big and very heterogeneous field with a lot of variation, so it's hard to recommend something that covers everything. That said, I think the best you'll do is to build familiarity with some of the common tools with some canned analyses. </p>
  
  <p>One possible option would be some of the scRNA-seq workflows from the Pachter group, they seem to be fairly conscientious about documentation. <a href="https://www.kallistobus.tools/getting_started" rel="nofollow noreferrer">Here is one such canned analysis</a>, <a href="https://academic.oup.com/bioinformatics/article-abstract/35/21/4472/5487510?redirectedFrom=fulltext" rel="nofollow noreferrer">here is the paper</a>, <a href="https://liorpachter.wordpress.com/2019/06/21/near-optimal-single-cell-rna-seq-pre-processing/" rel="nofollow noreferrer">here is a blog post about it that suggests next steps and discusses at a high level</a>. </p>
  
  <p>scRNA-seq is just what's been trendy for a few years, next year it will be something different. But it at least gets you familiar with some of the file formats and concepts such as alignment, etc.</p>
  
  <p>Hope that helps.</p>
  