computational-metabolomics / metfrag-galaxy Goto Github PK

It would be great if we could provide a more extended list of adducts to use with the MetFrag Galaxy tool. Currently we support only a few adduct types

In the MetFrag documentation "Further parameters" section in mentions "PrecursorIonMode" should be set for different adducts i.e.

Further Parameters
PrecursorIonMode
The adduct type of the precursor is used to calculate fragment masses. Following adduct types can be set by their appropriate numerical value encoding the following types:

positive (IsPositiveIonMode = True)
1	-	[M+H]+
18	-	[M+NH4]+
23	-	[M+Na]+
39	-	[M+K]+
33	-	[M+CH3OH+H]+
42	-	[M+ACN+H]+
64	-	[M+ACN+Na]+
83	-	[M+2ACN+H]+

negative (IsPositiveIonMode = False)
-1	-	[M-H]-
35	-	[M+Cl]-
45	-	[M+HCOO]-
59	-	[M+CH3COO]-

no adduct (IsPositiveIonMode = True/False)
0	-	[M]+/-

So I am wondering if these adduct types are hardcoded? or would it be safe to presume we could add in any other adducts as long as "PrecursorIonMode" is set to the rounded value of the adduct (e.g. [M+Na] is 23) and the correct neutral mass has been calculated for "NeutralPrecursorMass".

Do you know anything more about this @korseby?

@RJMW

As discussed at our MetaRbolomics hackathon, move repo to https://github.com/ipb-halle .

Since either a local db or a metfrag db has to be chosen this shoud be realised with a conditional.

Hi @korseby,

There were some default weights given in the Galaxy tool development for the scoring approaches used for FragmenterScore, OfflineMetFusionScore and SuspectListScore.

These were originally given the weights of 0.4,0.6 and 1.0.

These were added when the Galaxy tool was updated from the MetFrag Galaxy tool PhenoMeNal wrapper - I just wanted to see if you know where these weights came from?

The Metfrag tool runs fine when running with a local executor. However, when I use a Docker executor, Metfrag fails. The reason seems to be that it requires Python which is not included in quay.io/biocontainers/metfrag:2.4.5.

I imagine the fix would be to add a Python requirement in <requirements> and Galaxy would make a "mulled container", at least that's what I hope...

As part of the "Hackathon on Galaxy Tools and Workflows for Metabolomics" at EMBL-EBI - we would like to make this tool compatible with the IUC guidelines

Currently getting the following messages from usegalaxy.eu

Error loading JNI InChI native code.
You may need to compile the native code for your platform.
See http://jni-inchi.sourceforge.net for instructions.

org.openscience.cdk.exception.CDKException: Unable to load native code; net.sf.jnati.NativeCodeException: Error creating directory: /opt/galaxy/.jnati/repo/jniinchi/1.03_1/LINUX-AMD64

Do not get the same issue when running locally, on travis and bham.

When submitting with to less memory I get:

Error: A JNI error has occurred, please check your installation and try again
Exception in thread "main" java.lang.UnsatisfiedLinkError: /gpfs1/data/galaxy_server/galaxy/database/dependencies/_conda/envs/[email protected]/lib/libnio.so: /gpfs1/data/galaxy_server/galaxy/database/dependencies/_conda/envs/[email protected]/lib/libnio.so: failed to map segment from shared object: Cannot allocate memory

in stderr.

Maybe detect Cannot allocate memory as out of memory error, then Galaxy can react on this.

After some discussion with @korseby we are going to update the galaxy tool here (https://github.com/computational-metabolomics/metfrag-galaxy) with the functionality that has been developed as part of PhenoMenal (script and xml). Keeping the tool as python though.

The plan will be that we will later migrate this tool to a central Metfrag repository

@jsaintvanne: What is the status with your pull requests? Can you update or create a new pull request that merges them with the branch "phen_msp_to_metrag_update".

FYI: @RJMW