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--- oxfordfeb162012:start [2015/11/09 16:39] – magiero
+++ oxfordfeb162012:start [2015/11/09 19:59] (current) – magiero
@@ Line 260: / Line 260: @@
 === - Identification of Population and Sub-Population [0340]-[0343] ===
 • [0341] This example is worked through with simulated data.\\
+• [0341] A set of 60 mean current feature vectors of library component 13 is simulated.\\
+• [0341] 10 of these contain a SNP.\\
+• [0341] Gaussian noise of std. dev. 1-pA is added to each value and 5% of values within each vector are deleted at random.\\
+• [0342] Using this dataset a consensus is constructed using the **landmark process** outlined earlier.  The difference at SNP location 337 is clear.\\
+{{consensus.png?500}}
+• [0343] SNP of populations 51-60 gathered from consensus is clear in the figure below.\\
+{{snppop.png?500}}
 === - Identification of a Number of Populations [0344]-[0347] ===
+• [0345] Experiments on 2 and 3 simulated species were done.  That is populations of 2 and 3 different DNA samples were fed into the machine.  Pairwise alignment is used to obtain similarity scores and hence a **similarity tree** is made by **neighbour joining**.\\
+{{tree2.png?300 }} {{ tree3.png?300}}
+• [0347] Identifications (as in example 2) were run for both experiments identifying the population distributions (see histograms).
+{{hist2.png?250 }} {{ hist3.png?250}}
 === - Assembly of a Library [0348]-[0351] ===
+• [0349] Using the 1-18 overlapping strands mentioned above (but without the extra common pattern tacked on to both ends of each strand).\\
+• [0350] A tree by **neighbour joining** on **pairwise alignment** scores was constructed.\\
+• [0350] Since relatively large non-similar regions were expected, a scoring function that does not penalize gaps at the beginning or end of the alignment as strongly as those within alignment was used (see result below).\\
+{{libtree.png?300}}
+• [0350] All sequences have similar relation to two other sequences representing the ~100 base overlap on either side.\\
+• [0351] Progressing through the tree in order of relatedness:
+  - consensus landmarks for the aligned sequences are constructed
+  - the landmark from that alignment then serves as the feature vector for alignment with the next sequence
+  - output of the process is a fully assembled feature vector