Select best OG from output table

[StefanReuscher]

Dear All,

First congrats for the emapper tool. It is fast, delivers comprehensive results (as far is I checked) and the website look like it has great potential.

I have ran emapper with about 26,000 rice proteins and standard settings and got about 90% assigned. Now from the output, how can I proceed ? Specifically I want to put human readable terms on my proteins

There are six OGs for each of the identified proteins ? I understand that in the download sections there are mapping files for each of the @xyzNOGs. But which of those is the best OG ? There must be some kind of score or hierarchy to choose from. Or maybe via the seed ortholog ?

Gruß,

Stefan

[jhcepas]

Hi Stefan, thanks for your message. If you used diamond mode, it will be hard to predict which is the best OG. You could take the most specific one (i.e. the one belonging to the most specific taxonomic level). The last one in the list if I recall correctly.

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On 25 April 2017 at 03:30, Stefan Reuscher ***@***.***> wrote: Dear All, First congrats for the emapper tool. It is fast, delivers comprehensive results (as far is I checked) and the website look like it has great potential. I have ran emapper with about 26,000 rice proteins and standard settings and got about 90% assigned. Now from the output, how can I proceed ? Specifically I want to put human readable terms on my proteins There are six OGs for each of the identified proteins ? I understand that in the download sections there are mapping files for each of the @xyzNOGs. But which of those is the best OG ? There must be some kind of score or hierarchy to choose from. Or maybe via the seed ortholog ? Gruß, Stefan — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#42>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AAJ_Sraipp13mwVLXRJ695Gw50m1whkcks5rzUy8gaJpZM4NG5Zl> .

[StefanReuscher]

Just a follow up.

I did select the most specific OG (by taxonomy level) and it seemed to make sense, at least the cases that I checked. I am still amazed by the speed and the coverage of terms. About 85% of all proteins have a human readable annotation now.