Format the document in a consistent manner
This commit is contained in:
		
							parent
							
								
									0150c54284
								
							
						
					
					
						commit
						9e561d4479
					
				
							
								
								
									
										13
									
								
								Notebook.org
									
									
									
									
									
								
							
							
						
						
									
										13
									
								
								Notebook.org
									
									
									
									
									
								
							| @ -1,5 +1,7 @@ | ||||
| * Biology Meets Programming: Bioinformatics for Beginners | ||||
| 
 | ||||
| ** Week 1 | ||||
| 
 | ||||
| *** DNA replication | ||||
| 
 | ||||
| **** Origin of replication (ori) | ||||
| @ -95,7 +97,6 @@ def PatternMatching(Pattern, Genome): | ||||
| We find out that the /9-mers/ with the highest frequency appear in cluster. | ||||
| There is strong statistical evidence that our subsequences are /DnaA boxes/. | ||||
| 
 | ||||
|        | ||||
| **** Computational approaches to find ori in any bacteria | ||||
|       | ||||
| Now that we're pretty confident about the /DnaA boxes/ sequences that we found, | ||||
| @ -119,6 +120,7 @@ We have to try another computational approach, | ||||
| find clusters of /k-mers/ repeated in a small interval. | ||||
| 
 | ||||
| ** Week 2 | ||||
| 
 | ||||
| *** DNA replication (II) | ||||
| 
 | ||||
| **** Replication process | ||||
| @ -257,7 +259,6 @@ def SkewArray(Genome): | ||||
|     return Skew | ||||
| #+END_SRC | ||||
| 
 | ||||
| 
 | ||||
| **** Finding /DnaA boxes/ | ||||
|       | ||||
| When we look for /DnaA boxes/ in the minimal skew region, | ||||
| @ -383,6 +384,7 @@ def Count(Motifs): | ||||
| ***** Exercise: Form the most frequent sequence of nucleotides | ||||
|        | ||||
| Finally, we can form a Consensus string, to get a candidate regulatory motif: | ||||
| 
 | ||||
| #+BEGIN_SRC python | ||||
| def Consensus(Motifs): | ||||
|     consensus = "" | ||||
| @ -493,8 +495,7 @@ def Pr(Text, Profile): | ||||
|     return probability | ||||
| #+END_SRC | ||||
| 
 | ||||
| Now we're finally ready to assemble all the pieces and implement a Greedy Motif | ||||
| Search Algorithm: | ||||
| Now we're finally ready to assemble all the pieces and implement a Greedy Motif Search Algorithm: | ||||
| 
 | ||||
| #+BEGIN_SRC python | ||||
| def GreedyMotifSearch(Dna, k, t): | ||||
| @ -583,9 +584,7 @@ def Pr(Text, Profile): | ||||
| 
 | ||||
| ***** Motifs in tuberculosis | ||||
| 
 | ||||
| Tuberculosis is an infectious disease, caused by a bacteria called /Mycobacterium | ||||
| tuberculosis/. The bacteria can stay latent in the host for decades, in hypoxic | ||||
| environments. | ||||
| Tuberculosis is an infectious disease, caused by a bacteria called /Mycobacterium tuberculosis/. The bacteria can stay latent in the host for decades, in hypoxic environments. | ||||
| Our Greedy Algorithm can help us identify a motif that might be involved | ||||
| in the process. | ||||
| 
 | ||||
|  | ||||
		Loading…
	
	
			
			x
			
			
		
	
		Reference in New Issue
	
	Block a user