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There are multiple ways to filter our data, based on chemical structures, biological organisms, or references.
One important filter seems to consider only 2D chemical structures.
For non-chemists, here is a small explanation:
In nature, multiple chemical structures are chiral (3D consideration).
The bonds in bold and dashed lines are those "3D" bonds. As you can see it greatly increase the number of possible stereoisomers.
Using Mass Spectrometry (MS) as a validation tool, we will only be able to annotate 2D structures (the one at the bottom).
It thus appears reasonable to keep a single 2D structure among all 3D structures reported in LOTUS.
Moreover, since we will use a chromatography step (most probably C18 stationary phase), only given compounds will be detected, some others being too polar, too fatty, too big, too small to be detected.
I implemented to functions make_2D.R and make_chromatographiable.R to filter our data accordingly.
Below are some examples of data before filtering (left) and after filtering (right).
If something is unclear, you have other filtering suggestions, or else, please do not hesitate!
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There are multiple ways to filter our data, based on chemical structures, biological organisms, or references.
One important filter seems to consider only 2D chemical structures.
For non-chemists, here is a small explanation:
In nature, multiple chemical structures are chiral (3D consideration).
The bonds in bold and dashed lines are those "3D" bonds. As you can see it greatly increase the number of possible stereoisomers.
Using Mass Spectrometry (MS) as a validation tool, we will only be able to annotate 2D structures (the one at the bottom).
It thus appears reasonable to keep a single 2D structure among all 3D structures reported in LOTUS.
Moreover, since we will use a chromatography step (most probably C18 stationary phase), only given compounds will be detected, some others being too polar, too fatty, too big, too small to be detected.
I implemented to functions
make_2D.R
andmake_chromatographiable.R
to filter our data accordingly.Below are some examples of data before filtering (left) and after filtering (right).
If something is unclear, you have other filtering suggestions, or else, please do not hesitate!
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