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Format the document in a consistent manner

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coolneng 2020-10-07 23:57:27 +02:00
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@ -1,5 +1,7 @@
* Biology Meets Programming: Bioinformatics for Beginners * Biology Meets Programming: Bioinformatics for Beginners
** Week 1 ** Week 1
*** DNA replication *** DNA replication
**** Origin of replication (ori) **** 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. 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/. There is strong statistical evidence that our subsequences are /DnaA boxes/.
**** Computational approaches to find ori in any bacteria **** Computational approaches to find ori in any bacteria
Now that we're pretty confident about the /DnaA boxes/ sequences that we found, 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. find clusters of /k-mers/ repeated in a small interval.
** Week 2 ** Week 2
*** DNA replication (II) *** DNA replication (II)
**** Replication process **** Replication process
@ -257,7 +259,6 @@ def SkewArray(Genome):
return Skew return Skew
#+END_SRC #+END_SRC
**** Finding /DnaA boxes/ **** Finding /DnaA boxes/
When we look for /DnaA boxes/ in the minimal skew region, 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 ***** Exercise: Form the most frequent sequence of nucleotides
Finally, we can form a Consensus string, to get a candidate regulatory motif: Finally, we can form a Consensus string, to get a candidate regulatory motif:
#+BEGIN_SRC python #+BEGIN_SRC python
def Consensus(Motifs): def Consensus(Motifs):
consensus = "" consensus = ""
@ -493,8 +495,7 @@ def Pr(Text, Profile):
return probability return probability
#+END_SRC #+END_SRC
Now we're finally ready to assemble all the pieces and implement a Greedy Motif Now we're finally ready to assemble all the pieces and implement a Greedy Motif Search Algorithm:
Search Algorithm:
#+BEGIN_SRC python #+BEGIN_SRC python
def GreedyMotifSearch(Dna, k, t): def GreedyMotifSearch(Dna, k, t):
@ -583,9 +584,7 @@ def Pr(Text, Profile):
***** Motifs in tuberculosis ***** Motifs in tuberculosis
Tuberculosis is an infectious disease, caused by a bacteria called /Mycobacterium 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/. 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 Our Greedy Algorithm can help us identify a motif that might be involved
in the process. in the process.