Answers.Body: <p>You need to understand the biological significance of the 33-mer locus. </p>
  
  <ol>
  <li>I'd blast your 33-mer on <a href="https://blast.ncbi.nlm.nih.gov/Blast.cgi" rel="nofollow noreferrer">NCBI</a> and see what the biological significane is of the locus. If might be a transposon/ promoter region. </li>
  <li>Look at the translation of the protein and if its part of a protein the location within the protein structure.   </li>
  <li>Blast the entire locus including 5' and 3' regions of the 33-mer target</li>
  <li>Carefully assess the results for chance, e.g. conserved protein and the synonymous mutations just happened to drop out (Poison distribution).</li>
  </ol>
  
  <p>Once you gain a greater understanding, it is likely to have been identified before and what you might contribute is a different method to obtain a known result.</p>
  
  <p>In summary, you need the next level part of the course about translating a genetic correlation into a biological result/ mechanism. This isn't research grade work at present in my opinion.</p>
  
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Answers.CreationDate: 2020-05-10T19:50:55.763Z
Answers.Text: Thank you for your answer, that NCBI is a really nice resource.
Answers.UserDisplayName: Beurtschipper
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Body: <p>I've might have created a new algorithm for finding patterns in DNA. The technique has not been used before, and it managed to find a 33-mer with corresponding complement that exists both in E. Coli and in Salmonella. I think it's pretty neat but I don't know how to evaluate the results. In addition, I've developed this algorithm after a bioinformatics course where only the DNA sequences of E. Coli and Salmonella were provided, so I would like to test it on other sequences and have it evaluated by people who know what they are doing.</p>
  
  <p>How would one get in contact with the right people for evaluating amateur bioinformatics result?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-10T19:17:35.297Z
Comments.Text: If you took a course, start by chatting with the Professor/Instructor who taught it. They will either be able to chat about things with you or put you in touch with someone locally.
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-10T19:20:25.853Z
Comments.Text: It was one of those online Coursera courses. There are no real options to get in contact with the professor who created it.
Comments.UserDisplayName: Beurtschipper
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-11T11:52:00.537Z
Comments.Text: What is the benefit of your approach? Is it faster than the myriad existing algorithms for pattern finding? Does it have better pattern-definition syntax? What kind of "patterns" are we talking about? What is this 33,er supposed to be? What makes it special?
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-11T12:37:47.873Z
Comments.Text: I'm exploring the existance of superpermutations (of n = 4 = 33-mers) in DNA. I've created an array of all possible nucleotide superpermutations and I'm checking if sequences exist with a certain error margin. I've found a 33-mer that according to NCBI exists in 1839 unique organisms. I think this is interesting but I don't know if it means anything (with significance). The reason I'm exploring superpermutations is because they are the mother of all permutations (for which we look in DNA).
Comments.UserDisplayName: Beurtschipper
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-11T13:42:10.517Z
Comments.Text: Wait, so you mean you just found some random sequence of 33nts whose only distinguishing feature is that you find it in many organisms? That doesn't seem strange, 33 is tiny, so you'll find loads of sequences that can be found all over the place at that length. Especially if you're not limiting to exact hits. Try generating some random 33nt sequences and searching for those, I bet many or even most of them will be found in hundreds of species.
Comments.UserDisplayName: terdon
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-11T14:00:09.677Z
Comments.Text: Thank you. I was looking for an answer like that.
Comments.UserDisplayName: Beurtschipper
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-05-12T17:03:46.907Z
Comments.Text: @terdon *33 is tiny, so you'll find loads of sequences* I guess that depends how you look at it... with 33 nt you can make 4^33 ~ 7e19 combinations equivalent to about ~25 billion human genomes. If you pick one at random chances are it's never been seen anywhere (do I get it right?)
Comments.UserDisplayName: dariober
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Comments.CreationDate: 2020-05-12T17:08:01.740Z
Comments.Text: @dariober ah, but this isn't random at all. It's a [superpermutation](https://en.wikipedia.org/wiki/Superpermutation) which I suspect will mean the sequence is relatively repetitive, making it far more likely to appear randomly in blast results. I admit I'm just speaking off the top of my head though (hence the comment instead of an answer). But yes, my assumption would be that finding a 33nt sequence present (as an imperfect match, mind you, as the OP described) in multiple genomes is not surprising per se. Finding _exact_ matches in hundreds of genomes would be more intriguing, yes.
Comments.UserDisplayName: terdon
Tags: <sequence-analysis><algorithms>
Title: Evaluating amateur bioinformatics results
ThreadText: # Evaluating amateur bioinformatics results
  
  (Asked by: username_0 on 2020-05-10)
  
  I've might have created a new algorithm for finding patterns in DNA. The technique has not been used before, and it managed to find a 33-mer with corresponding complement that exists both in E. Coli and in Salmonella. I think it's pretty neat but I don't know how to evaluate the results. In addition, I've developed this algorithm after a bioinformatics course where only the DNA sequences of E. Coli and Salmonella were provided, so I would like to test it on other sequences and have it evaluated by people who know what they are doing.
  
  How would one get in contact with the right people for evaluating amateur bioinformatics result?
  
  **Tags:** sequence-analysis, algorithms
  
  > **Comments:**
  > **username_1**: If you took a course, start by chatting with the Professor/Instructor who taught it. They will either be able to chat about things with you or put you in touch with someone locally.  
  > **username_0**: It was one of those online Coursera courses. There are no real options to get in contact with the professor who created it.  
  > **username_2**: What is the benefit of your approach? Is it faster than the myriad existing algorithms for pattern finding? Does it have better pattern-definition syntax? What kind of "patterns" are we talking about? What is this 33,er supposed to be? What makes it special?  
  > **username_0**: I'm exploring the existance of superpermutations (of n = 4 = 33-mers) in DNA. I've created an array of all possible nucleotide superpermutations and I'm checking if sequences exist with a certain error margin. I've found a 33-mer that according to NCBI exists in 1839 unique organisms. I think this is interesting but I don't know if it means anything (with significance). The reason I'm exploring superpermutations is because they are the mother of all permutations (for which we look in DNA).  
  > **username_2**: Wait, so you mean you just found some random sequence of 33nts whose only distinguishing feature is that you find it in many organisms? That doesn't seem strange, 33 is tiny, so you'll find loads of sequences that can be found all over the place at that length. Especially if you're not limiting to exact hits. Try generating some random 33nt sequences and searching for those, I bet many or even most of them will be found in hundreds of species.  
  > **username_0**: Thank you. I was looking for an answer like that.  
  > **username_3**: @username_2 *33 is tiny, so you'll find loads of sequences* I guess that depends how you look at it... with 33 nt you can make 4^33 ~ 7e19 combinations equivalent to about ~25 billion human genomes. If you pick one at random chances are it's never been seen anywhere (do I get it right?)  
  > **username_2**: @username_3 ah, but this isn't random at all. It's a [superpermutation](https://en.wikipedia.org/wiki/Superpermutation) which I suspect will mean the sequence is relatively repetitive, making it far more likely to appear randomly in blast results. I admit I'm just speaking off the top of my head though (hence the comment instead of an answer). But yes, my assumption would be that finding a 33nt sequence present (as an imperfect match, mind you, as the OP described) in multiple genomes is not surprising per se. Finding _exact_ matches in hundreds of genomes would be more intriguing, yes.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_4 on 2020-05-10. Score: 1 | Accepted answer
  
  You need to understand the biological significance of the 33-mer locus.
  
  1. I'd blast your 33-mer on [NCBI](https://blast.ncbi.nlm.nih.gov/Blast.cgi) and see what the biological significane is of the locus. If might be a transposon/ promoter region.
  2. Look at the translation of the protein and if its part of a protein the location within the protein structure.
  3. Blast the entire locus including 5' and 3' regions of the 33-mer target
  4. Carefully assess the results for chance, e.g. conserved protein and the synonymous mutations just happened to drop out (Poison distribution).
  
  Once you gain a greater understanding, it is likely to have been identified before and what you might contribute is a different method to obtain a known result.
  
  In summary, you need the next level part of the course about translating a genetic correlation into a biological result/ mechanism. This isn't research grade work at present in my opinion.
  
  > **Comments:**
  > **username_0**: Thank you for your answer, that NCBI is a really nice resource.  
  
  ---
  
  