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written 3.2 years ago by The Golm Metabolome Blog
I used to sign my applications (several GMD tools, GoBioSpace Search Application) with a certificate which was trusted up the chain by the MPG, the DFN and the Deutsche Telekom.This certificate was issued 24.04.2012 and was working fine until recently. Out of blue several users reported that they could not install any more application I signed. It turned out that something changed in the certificate store of windows and you need to except the root certificate for code signing.This post will guide you through.Normally, after clicking to install the application from the internet explorer you will see on the bottom of the page a dialog like this, asking for permission to run the installer. Now you will experience something similar to this error message 'The signature of setup.exe is corrupt or invalid.' or, if you click to see the detailsTo solve this, press the Windows Key + R and type "mmc" and click OK.you will see the Microsoft Management Consoleclick File and "Add/Remove snap-In" to add the certificate Plug-Inactivate "my user accout" and click finishyou will see the certificate plug-in on the right. now click OK. In the left panel activate Trusted Root Certification Authorities and then Certificates. Search for the 'Deutsche Telekom Root CA 2' certificate in the right hand panel. Right click this certificate and select properties. Activate the the purpose 'code signing' as shown below and press OK.Problem solved. If you retry to install some application my certificate will be accepted for code signing and, hence, the ...
written 3.2 years ago by The Golm Metabolome Blog
Our service is unavailable this morning due to network maintenance. We apologize for any inconvenience and we are working to bring the site back online as soon as possible.
written 3.2 years ago by MetaMapp- visualizing metabolomics networks
ChemMapp (a network created using chemical similarity calculations) of compounds present in MayoClinic catalogues. They catalogues can be downloaded here. There are almost 600 unique chemical compounds that are routinely measured by MayoClinic in patients. These compounds can include drugs, metabolites and pollutants.This is the most comprehensive picture of clinical chemistry tests on chemicals (excluding proteins and DNA tests). From an epidemiological point of view, it gives an idea where we can also look for risk factors for NCDs, as MayoClinic may have prospective data for several subjects for which we have these measurements.In a metabolomics assay, we get more than 2000 compounds measured in a blood specimen using a good instruments such as UPLC-QTOF. Though, we dont know identity for up to 70% of the compounds, we can see that untargeted assays do provide new compounds that can be included in this ChemMapp. Integration and comparison of this ChemMapp and MetaMapp from the Metabolon Inc (coming in next post) could be quite interesting.
written 3.4 years ago by MetaMapp- visualizing metabolomics networks
A chemical similarity map view of IARC monograph. Node colors are IARC classification. Red node are carcinogens.
written 4.0 years ago by The Golm Metabolome Blog
Last week I got this question asked:"... I might as well ask if you know of a source of peak identification for typical Arabidopsis rosette polar compounds derivatized to typical MeOX/TMS forms (sugars, organic acids, amino acids, etc. obtained from a typical MeOH/H20 extraction)? I am trying to identify the most abundant polar compounds (top 100 or so) in Arabidopsis leaf tissue but I have limited access to standards and I am sure this has already been done. Here is what I would ideally need: a list of the most abundant compounds in their elution order on a ms5 column or similar with spectra. Any idea if such a list can be found on the GMD site or elsewhere?"I took the identified analatyes from the experiment Mining for metabolic responses to long-term salt stress: a case study on Arabidopsis thaliana Col-0 (A)(see metabolite profile) and added the retention idexes. Null problemo!cheersJan Analyte Metabolite RI relative to alkane homologes on 5%-phenyl-95%-dimethylpolysiloxane capillary column Boric-acid_3TMS Boric-acid 971.63 Propane-1,2-diol (2TMS) Propane-1,2-diol 988.00 Decane, n- Decane, n- 1000.00 Siloxane Siloxane 1021.01 Pyridine, 2-hydroxy- (1TMS) 2-Hydroxypyridine 1031.31 Lactic acid (2TMS) Lactic acid 1044.47 Glycolic acid (2TMS) Glycolic acid 1062.89 Hydroxylamine (3TMS) Hydroxylamine 1104.93 similar to Cyclopentasiloxane, decamethyl Siloxane 1117.07 NA114002 (classified unknown) 1131.09 Furan-2-carboxylic acid (1TMS) Furan-2-carboxylic acid 1133.08 Pyridine, 3-hydroxy- (1TMS) 3-Hydroxypyridine 1136.97 Benzylalcohol (1TMS) Benzyl Alcohole 1152.05 D116201 1161.89 Proline (1TMS) Proline 1176.03 NA 1189.63 Dodecane Dodecane 1200.00 Valine (2TMS) Valine 1207.10 similar to Pentasiloxane, dodecamethyl Siloxane 1208.14 Diethylenglycol (2TMS) Diethyleneglycol 1236.16 NA ...
written 4.1 years ago by metaBlogOmics
Emerging Market Predicted to Double in the Next Five Years to Well Over One Billion Dollars
If you are going to the Metabolomics Society 2013 Conference in Glasgow next week then I hope to see you there. Stop by the poster P9-12 during poster session II and say hi! There is a lot going on in Metabolomics and we will be reporting on some of our latest developments towards Big Data science. I also post the link to our last year's poster from the Metabolomics Society 2012 Conference in Washington, DC.During this year's Metabolomics Society Award Ceremony our work on Decision tree supported substructure prediction of metabolites from GC-MS profiles will be award 2013 Best Paper Runner up for the second highest total number of citations during the previous three years. Wow! What a pleasant surprise.
The cross experiment control on the metabolite detail page has been updated to convey more information at glance. Instead of a big box plot comparing all experiments to one another, a list with experiments has been added. Each row consists of a heat map and a tiny box plot, as well as the experiment name, organism, the variance, the anova F score and the main experimental conditions. When hovering above a row, a bigger version of the box plot with more detailed experimental conditions appears.Example: http://gmd.mpimp-golm.mpg.de/Metabolites/37e8fffb-70da-4399-b724-476bd8715ef0.aspx#QuantitativeProfileData
I got this question asked today and thought this is worth be documented. I only have version 2.0 of the NIST ms search software at hand, but I assume that the way to import the reference library is pretty similar in later versions.Start the program.Click Librarian on the tab control at the bottom.Create a library by clicking the most right button in the toolbar.Type an appropriate library name - "GMD". Close the dialog by clicking OK. The click on the Import button, left in the toolbar.Select the msp-file downloaded from the GMD. Make sure to first select "All files" in the file type menu.In the option check "include Synonyms" Click "Import All".The import is starting.You can cancel the library matching process. The GMD reference library is imported and ready for use.cheersJan
written 4.4 years ago by metaBlogOmics
A new version of XCMS Online was released today. The upgraded website now includes an interactive cloud plot to visualize results from untargeted metabolomic experiments, a job sharing center, a parameter manager to organize existing parameter sets, support for Bruker raw data files and more. The detailed list of changes can be found here.Cloud Plot
written 4.4 years ago by metaBlogOmics
After four years working on numerous projects using untargeted metabolomics, called by some colleagues "global metabolite profiling", my vision of this scientific discipline has been evolving largely as a result of the difficulties I've been encountering. Starting with the sample preparation (i.e., extraction of metabolites), through data processing, and ending with the identification of metabolites, each of these steps has caused me its own headaches. Still, I must admit, I have greatly simplified the whole process and now I can conduct much more pragmatic metabolomics studies. From this blog, I would like to begin a series of dialogues aimed at discussing the various methodological aspects of metabolomics. And I want to start with, perhaps, the subject that has suffered the largest transformation in my methodological workflow: data processing. Those who work with TOF instruments coupled to liquid chromatography will be familiar with the massive amount of data obtained with programs like XCMS. Well, in my opinion it all comes down to understanding the term “feature”. A very simple definition of feature is “a molecular entity with a unique m/z and retention time”. However, one feature does not necessarily correspond to a metabolite. The number of features is always much higher than the number of metabolites. How much? How many features do you typically detect in a regular untargeted metabolomics study? How do you filter features to end up identifying metabolites?feature detection with XCMSThese are some of the points I would like to discuss here. I bet you'll read a lot ...
written 4.6 years ago by The Golm Metabolome Blog
Recently, I got this question on how to import GMD mass spectral reference libraries into the Automated Mass Spectral Deconvolution and Identification System (AMDIS). As I think this might be interesting for other people as well, I copy the question and my answer below:Hi, I am XXX in YYY lab at ZZZ university. I have just downloaded the following library GMD_20111121_VAR5_ALK_MSL.txt from Golm Metabolome DB. Because I would like to use it AMDIS software.However, the extension file of the library is in TXT and I need to converte it to MSL. I will appreciate a lot if you could tel me how to do it.XXXPS. I tried to rename the extention but it did not work in AMDISDear XXX,Thank you very much for using the Golm Metabolome Database (GMD).Please use the Amdis software to convert the downloaded file into a library. I try to list all necessary steps in the followingOpen Amdis :)Click Library ==> Build One Library (this option is only available if a data file is open)Click FilesClick Load Library Select the file downloaded from the GMD, you might need to change file type to "all files *.*" to see your file with file extension .txtThe import is now starting and as a result you should see a list of 2,594 imported spectraClick FilesClick "Save Library As"Give a appropriate file location and name and use the file extension msl The file is now exported and a ".cid" file (compound identification library) is generated, this is a crucial stepClick ...
written 5.2 years ago by MetaMapp- visualizing metabolomics networks
Abstract linkAbstractPantothenate kinase-associated neurodegeneration (PKAN) is a rare, inborn error of metabolism characterized by iron accumulation in the basal ganglia and by the presence of dystonia, dysarthria, and retinal degeneration. Mutations in pantothenate kinase 2 (PANK2), the rate-limiting enzyme in mitochondrial coenzyme A biosynthesis, represent the most common genetic cause of this disorder. How mutations in this core metabolic enzyme give rise to such a broad clinical spectrum of pathology remains a mystery. To systematically explore its pathogenesis, we performed global metabolic profiling on plasma from a cohort of 14 genetically defined patients and 18 controls. Notably, lactate is elevated in PKAN patients, suggesting dysfunctional mitochondrial metabolism. As predicted, but never previously reported, pantothenate levels are higher in patients with premature stop mutations in PANK2. Global metabolic profiling and follow-up studies in patient-derived fibroblasts also reveal defects in bile acid conjugation and lipid metabolism, pathways that require coenzyme A. These findings raise a novel therapeutic hypothesis, namely, that dietary fats and bile acid supplements may hold potential as disease-modifying interventions. Our study illustrates the value of metabolic profiling as a tool for systematically exploring the biochemical basis of inherited metabolic diseases.A MetaMapp mapping and visualization of the 227 metabolites from this study. Node size reflects the fold change in patient/control comparison. Only the the nodes that have passed the p-value criteria of 0.05 are labelled. Red means up and blue means down regulated. Bottom figure is the volcano plot to show the changes.
written 5.3 years ago by MetaMapp- visualizing metabolomics networks
A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. The revival of interest in cancer cell metabolism in recent years has prompted the need for quantitative analytical platforms for studying metabolites from in vivo sources. We implemented a quantitative polar metabolomics profiling platform using selected reaction monitoring with a 5500 QTRAP hybrid triple quadrupole mass spectrometer that covers all major metabolic pathways. The platform uses hydrophilic interaction liquid chromatography with positive/negative ion switching to analyze 258 metabolites (289 Q1/Q3 transitions) from a single 15-min liquid chromatography–mass spectrometry acquisition with a 3-ms dwell time and a 1.55-s duty cycle time. Previous platforms use more than one experiment to profile this number of metabolites from different ionization modes. The platform is compatible with polar metabolites from any biological source, including fresh tissues, cancer cells, bodily fluids and formalin-fixed paraffin-embedded tumor tissue. Relative quantification can be achieved without using internal standards, and integrated peak areas based on total ion current can be used for statistical analyses and pathway analyses across biological sample conditions. The procedure takes ~12 h from metabolite extraction to peak integration for a data set containing 15 total samples (~6 h for a single sample).Where are the lipids?
written 5.3 years ago by MetaMapp- visualizing metabolomics networks
Coordination of the transcriptome and metabolome by the circadian clockPNAS 2012The circadian clock governs a large array of physiological functions through the transcriptional control of a significant fraction of the genome. Disruption of the clock leads to metabolic disorders, including obesity and diabetes. As food is a potent zeitgeber (ZT) for peripheral clocks, metabolites are implicated as cellular transducers of circadian time for tissues such as the liver. From a comprehensive dataset of over 500 metabolites identified by mass spectrometry, we reveal the coordinate clock-controlled oscillation of many metabolites, including those within the amino acid and carbohydrate metabolic pathways as well as the lipid, nucleotide, and xenobiotic metabolic pathways. Using computational modeling, we present evidence of synergistic nodes between the circadian transcriptome and specific metabolic pathways. Validation of these nodes reveals that diverse metabolic pathways, including the uracil salvage pathway, oscillate in a circadian fashion and in a CLOCK-dependent manner. This integrated map illustrates the coherence within the circadian metabolome, transcriptome, and proteome and how these are connected through specific nodes that operate in concert to achieve metabolic homeostasis.
written 5.4 years ago by The Golm Metabolome Blog
I got this three questions and think I should answer those here because the topic might be interesting to other people as well...1.- Could I consider the following examples (VAR5-Alk-NA 170001 (Classified unknown); VAR5-Alk-NA and VAR5-Alk-unknown) as unknown or do they mean something different?[JH] These terms do all refer to a unknown compound. This just points to a lack in annotation. As we also ask other laboratories for their spectral libraries it may happen that users use different terms. However, in your example with NA170001 (classified unknown) Joachim Kopka tried to highlight, that there is a chance of identification either as "[C5H12O5 (5TMS)|C20H52O5Si5]" or "[Pentitol (5TMS)|C20H52O5Si5]".2.- Sometimes the compounds are label as True-VAR5-Alk, False-VAR5-Alk and Pred-VAR5-Alk. can I understand that the database was not curate? therefore there are some False. However the ones that are PRED (=predicted?) Can i trust in this prediction? or do i need only to pay attention to the compounds that says true? [JH] This "True", "False" and "pred" just refers to the Retention index. The retention index is specific to the chromatographic setup. As the GMD is a collection of reference spectra from different labs utilising different chromatographic setups we differentiate the quality of the retention index values."true" is the best and refers to the fact, that this RI was actually experimentally observed on this chromatographic setup."pred" means predicted and is the next lower quality level. Importing a library from a other laboratory we can correlate the retention indexes values for all compounds which were ...
written 5.4 years ago by metaBlogOmics
follow-up to my last posting: Today I was reading the review below and I found it amusing to read this:"... However, the relatively low sensitivity of NMR, and the spectral overlap that often occurs, limits the number and variety of metabolites than can be simultaneously observed. Hyphenated mass spectrometry (MS) methods, such as GC-MS, LC-MS, and CE-MS, currently provide higher sensitivity, and are the leading analytical platform for metabolite profiling." Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and AnalysisSugimoto, Masahiro; Kawakami, Masato; Robert, Martin; Soga, Tomoyoshi; Tomita, MasaruCurrent Bioinformatics, Volume 7, Number 1, March 2012 , pp. 96-108(13)
written 5.4 years ago by metaBlogOmics
Metabolomics of sepsis-induced acute lung injury: a new approach for biomarkers Paige LacyAm J Physiol Lung Cell Mol Physiol. 2011 Jan;300(1):L1-3. Epub 2010 Nov 5."... In particular, NMR is widely used for “classic” metabolic studies, because this approach has an exceptional capacity to rapidly identify and quantify multiple metabolites in biological fluids. Other approaches involving mass spectrometry are limited in their ability to identify more than a few small molecules in complex mixtures and are unable to quantify metabolites accurately."For a review, I think this is a more than questionable statement.
Ricardo Silva pointed me to a problem in the GMD spectrum export for the TargetSearch software:He wrote:I've started to work with GC-MS analysis on R, and the TargetSearch recomends the golm database http://gmd.mpimp-golm.mpg.de/download/, but the librarys don't have Retention Index, is this correct? How do a get a library with Retention Indexes?Indeed, I found a format error due to the globalisation which led TargetSearch fail to load the textfile.Thanks Ricardo!ps.: If you finde any problem, please drop me a line...
Patrik Rydberg posted some code to automatically scale a molecule in the ChemDoodle canvas. I was looking for something like this for quite some time. Now I could this improve for my settings having molFile from many different sources by first scaling the molecule with the scaleToAverageBondLength(Number length) function.See an example here:http://gmd.mpimp-golm.mpg.de/Analytes/0a2b3536-2245-4c0e-bdbc-495766eeec67.aspxMy code (taken from Patrik) is below:structure = ChemDoodle.readMOL(molFile);structure.scaleToAverageBondLength(10);size = structure.getDimension();scale = Math.min(canvas.width / size.x, canvas.height / size.y);canvas.loadMolecule(structure);canvas.specs.scale = scale * .9;canvas.repaint();cheers
We regret that GoBioSpace service is likely to be unavailable today 17th. Jan.2012 on account of maintenance work and for the import of the current PubChem Compound and Substance databases. More than 2.5 million structures from the IBM BAO (Business Analytics and Optimization) strategic IP insight platform (SIIP) are now available in PubChem and we think this is very valuable for matching potentially unknown mass peaks.Your GoBioSpace-Team[update 2012/01/18] We released a new data version of GoBioSpace, now including the latest version (yesterday, 2012/01/17) of PubChem Compound and Substance databases and adding 119,958 new unique formula to the GoBioSpace repository. However, approx. 190,000 formula are not referenced anymore and subsequently were purged from GoBioSpace.
GoBioSpace is a tool to turn measured masses into source tagged sum-formulas and with this blog entry I want to focus on the datasources.In contrast to combinatorial sum formula prediction tools coming up with a blank formula, the GoBioSpace user gets a formula tagged with many more information such as names, InChIs, hyperlinks and so on. To make this clear GoBioSpace heavily depends on its data sources, and PubChem (Compound and Substance) and ChemSpider are the biggest ones. First, I want to thank those data sources for making the data available to the community and I want to thank for the time and effort the people spend in developing this databases. Second, I want to give a short statistic with respect to the data sources. The table given below lists all databases sourced into GoBioSpace showing the total number of sum formula coming from this database and the number of sum-formula which are made available only from this depositor (no other depositor published this formula). depositor name total formula formula just here PubChem1,968,75932,049PubChem (ChemSpider)1,582,073251,834ChemSpider 2011.06.011,268,051105,062ChemSpider 2008.09.281,178,614124,046PubChem (DiscoveryGate)1,011,3458,049PubChem (NextBio)958,6961,417PubChem (Thomson Pharma)761,17642,357PubChem (MolPort)428,11710,980PubChem (ChemDB)421,222198PubChem (ZINC)362,51361,484PubChem (Ambinter)297,5309,077PubChem (ChEMBL)196,4964,182PubChem (ChemBank)195,10416,284PubChem (Vitas-M Laboratory)117,95135PubChem (ChemIDplus)117,8652,357PubChem (BindingDB)111,4522,458PubChem (DTP/NCI)96,9965,118PubChem (NIAID)87,5861,331PubChem (ChemBridge)81,5250PubChem (ASINEX)75,9160PubChem (MLSMR)75,731659PubChem (Specs)67,7223PubChem (LeadScope)65,416257PubChem (ICCB-Longwood/NSRB Screening Facility, Harvard Medical School)64,003493PubChem (ChemExper Chemical Directory)61,3310PubChem (NIST)53,5326PubChem (AAA Chemistry)50,03347PubChem (ChemBlock)48,9100PubChem (NovoSeek)43,80747PubChem (Emory University Molecular Libraries Screening Center)42,4816PubChem (Southern Research Institute)39,2241PubChem (MTDP)36,8492PubChem (NCGC)35,575291Metabolome.JP25,396806PubChem (Burnham Center for Chemical Genomics)23,96419PubChem (Abbott Labs)22,196287PubChem (Broad Institute)20,506187PubChem (Sigma-Aldrich)18,9202PubChem (NIST Chemistry WebBook)18,5010PubChem (NMRShiftDB)16,89620PubChem (UPCMLD)15,8366PubChem (IS Chemical Technology)15,583267PubChem (GLIDA, GPCR-Ligand Database)14,497318KNApSAcK 201113,869222PubChem (The Scripps Research Institute Molecular Screening ...
Today I exported the current mass spectral library and linked the files on the GMD download web page.We now permit the download of the GMD mass spectral reference library under the Creative Commons Attribution-ShareAlike 3.0 License (many thanks to Steffen Neumann for helping us to choose the right license). This overdue export was shifted again and again because I wanted to cleanup some issues in the export application. Also, considering the seven export formats, two retention index markers and two GC-columns, giving raise to 7*2*2=28 different files, I wanted to implement an fully automated export first.In comparison to the deprecated library from June 2010 we added the library (~660 spectra) from Prof. Schomburgs department at Technische Universität Carolo-Wilhelmina in Braunschweig.If you also want to see your spectra integrated within the GMD I strongly want to encourage you to submit the spectra per email in any library format you prefer. cheers,Jan
Jan Lisec pointed me to a severe bug in the functional group prediction feature implemented in the GMD. It turned out that I normalised spectra before decision tree training different than spectra for spectral classification. Jan pointed me to a spectrum where the Phosphoric Acid Deriv group was predicted present based on m/z 299 although this particular mass had only a minimal intensity in this GC-MS spectrum.The only good news is that the validation for the publication was not affected by this bug, because the cross validation is performed without this web interface. However, I removed this bug and want to apologise for this inconvenience.Thanks Jan!cheers,the other Jan ;-)
19 posts, last updated 3.2 years ago
MetaMapp is a novel way to map metabolomics datasets into network graphs of biological significance.
18 posts, last updated 3.2 years ago
a place to discuss and share new ideas within the field of metabolomics
19 posts, last updated 4.1 years ago
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