text: user1542: I am interested on how profile hidden markov model is used for protein analysis in bioinformatics. Can you guys explain why phmm is more preferable method comparing to simple MSA(Multiple Sequence Alignment) scoring or simple MSA probability scoring? For example we have three MSA sequences as based model and a target to be scored.
  MSA Sequences
  A B C B C A
  A B B C C A
  A B C B A A
  Target Sequence
  A B B B C A
  By MSA scoring we can count score for target sequence comparing to a profile MSA. For example above target sequence protein column will counted as match(A), match(B), mismatch(B-majorityMSA(C)), match(B-majorityMSA(B)), match(C-majorityMSA(C)), match(A). Just give score for match and mismatch and we can get total score for a target sequence comparing to a profile MSA sequences.
  By MSA probability scoring we can count probability of each protein in each column so for example above 
  P(target_seq similar to MSA profile) = P(A in MSA sequences)*P(B in MSA sequences)*.... *P(A in MSA sequences) = 1*1*(1/3)*(2/3)*(2/3)*1 = 4/27 
  Isn't MSA scoring like above examples powerful enough?
  Thanks a lot !!
  
  user104: The simple probabilistic scoring scheme you describe to query a target sequence against a (protein) family represented as a multiple sequence alignment (MSA) is actually similar in essence to a Position Weight Matrix (PWM), although PWMs are more refined. PWMs are powerful rapid tools often used for motif analyses.
  However, your approach (and PWMs) don't handle insertions or deletions (indels), and you can't easily add these into your model (for example, consider that 2 1bp indels should not score the same as 1 2bp indel, and indels could occur within your MSA as well as in your target sequence). You could try and add some arbitrary gap opening and extension penalties somehow (like BLAST) but then you'd abandon your probabilistic model! By contrast, pHMMs have a fully fledged probabilistic indel model with insertion and deletion states.
  Finally, of practical note, there are some excellent implementations of pHMMs for protein sequence analysis, such as HMMER3 and HHSuite.
  
original_text: 1542: <p>I am interested on how profile hidden markov model is used for protein analysis in bioinformatics. Can you guys explain why phmm is more preferable method comparing to simple MSA(Multiple Sequence Alignment) scoring or simple MSA probability scoring? For example we have three MSA sequences as based model and a target to be scored.</p>
  
  <h3>MSA Sequences</h3>
  
  <p>A B C B C A<br>
  A B B C C A<br>
  A B C B A A<br></p>
  
  <h3>Target Sequence</h3>
  
  <p>A B B B C A</p>
  
  <p>By MSA scoring we can count score for target sequence comparing to a profile MSA. For example above target sequence protein column will counted as match(A), match(B), mismatch(B-majorityMSA(C)), match(B-majorityMSA(B)), match(C-majorityMSA(C)), match(A). Just give score for match and mismatch and we can get total score for a target sequence comparing to a profile MSA sequences.</p>
  
  <p>By MSA probability scoring we can count probability of each protein in each column so for example above <br> 
  <b>P(target_seq similar to MSA profile)</b> = P(A in MSA sequences)*P(B in MSA sequences)*.... *P(A in MSA sequences) = 1*1*(1/3)*(2/3)*(2/3)*1 = 4/27 <br><br></p>
  
  <p>Isn't MSA scoring like above examples powerful enough?</p>
  
  <p>Thanks a lot !!</p>
   104: <p>The simple probabilistic scoring scheme you describe to query a target sequence against a (protein) family represented as a multiple sequence alignment (MSA) is actually similar in essence to a <a href="https://en.wikipedia.org/wiki/Position_weight_matrix" rel="nofollow noreferrer">Position Weight Matrix</a> (PWM), although PWMs are more refined. PWMs are powerful rapid tools often used for motif analyses.</p>
  
  <p>However, your approach (and PWMs) don't handle insertions or deletions (indels), and you can't easily add these into your model (for example, consider that 2 1bp indels should not score the same as 1 2bp indel, and indels could occur within your MSA as well as in your target sequence). You could try and add some arbitrary gap opening and extension penalties somehow (like BLAST) but then you'd abandon your probabilistic model! By contrast, pHMMs have a fully fledged probabilistic indel model with insertion and deletion states.</p>
  
  <p>Finally, of practical note, there are some excellent implementations of pHMMs for protein sequence analysis, such as <a href="http://hmmer.org/" rel="nofollow noreferrer">HMMER3</a> and <a href="https://github.com/soedinglab/hh-suite" rel="nofollow noreferrer">HHSuite</a>.</p>
  