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1
XCMS / Re: Spectra versus XCMS/Camera
Last post by ab123 -
Thank you.
The problem I find most often with peak picking is that it picks random low intensity peaks within a certain retention time. And the other issue I seem to find are separated mzs, where you get essentially the same or very similar mz for the same RT.
2
Compound identification / Re: Fragmentation at 4.9 mz
Last post by ab123 -
Hi Jan,
Yes, I did think NH4 to Na made sense, but it would occur at least three times in a row.

The 26 difference is 26.0225, also at least 3 times in a row...

And why would it all be shifted by 5 mz between MS1 and MS2?
3
XCMS / Re: Spectra versus XCMS/Camera
Last post by Jan Stanstrup -
Given that the peak picking went well and didn't merge several masses it shouldn't have, yes that is my experience. If you see big differences you know something is not going right.
4
Compound identification / Re: Fragmentation at 4.9 mz
Last post by Jan Stanstrup -
4.9554 could be a Na+<-> NH4+ difference. https://github.com/stanstrup/commonMZ/blob/master/inst/extdata/adducts_fragments.tsv

26 I don't have a good idea for. What is it accurately?
5
Compound identification / Fragmentation at 4.9 mz
Last post by ab123 -
Dear Forum members,

My MS1 files are showing multiple fragments at 26 mz apart with a strong base peak in between them. The more fragmented MS2 (I mean MSe here since this isn't MSMS) files are then showing the same peaks shifted by 4.9 mz each backwards.

Any idea what this may be?

6
XCMS / Re: Spectra versus XCMS/Camera
Last post by ab123 -
HI Jan,
Thank you!
Yes, I mean mz values. So the peaktable mz has a better accuracy than the peak mz shown in an actual spectrum at the same retention time?
7
XCMS / Re: Spectra versus XCMS/Camera
Last post by Jan Stanstrup -
Are you talking about which m/z value to use? The one from the peaktable or from the raw data?
In xcms the m/z for each feature in individual samples is the intensity weighted mean across the peak. Then when you group features across samples it uses the median m/z of those mean values.

Because of this averaging the m/z in your peaktable should have a bit better accuracy. That is under the assumption that the parameters were sane enough not to group things that are NOT the same compound. So using this value you should normally be able to restrict your m/z range more when you search.
8
XCMS / Spectra versus XCMS/Camera
Last post by ab123 -
Hello,

I just wanted to double check the following approach is not incorrect. I may be overthinking, but better safe than sorry.

For metabolite identification, I am searching for the feature xcms spat out, but I predominantly search the peaks that turn up in my spectra. Occasionally, these may not perfectly match either Camera or Xcms features.
I guess the question is: what's deemed more reliable - the xcms feature detection or the spectrum in identifying molec ions for compound identification?

Many thanks!
9
Job opportunities / Research Fellow in Bioinformatics/Biostatistics
Last post by eppsd -
Role: Research Fellow in Bioinformatics/Biostatistics
Salary: Full time starting salary is normally in the range £29,799 to £38,832. With potential progression once in post to £41,212 a year.

Job Purpose

To contribute significantly to the excellence of the 'metabolomics ecosystem' at the University of Birmingham, primarily contributing to the NERC Biomolecular Analysis Facility - Birmingham (NBAF-B), the UK's national research centre for Environmental Metabolomics. Additionally, to engage with scientists in Phenome Centre Birmingham as well as Viant's, Dunn's and Weber's research teams. The postholder will develop and apply expert analysis of large-scale mass spectrometry and/or NMR spectroscopy based metabolomics datasets, including experimental design, data processing, statistical analysis and/or network analysis; to contribute to the Birmingham Metabolomics Training Centre; and to undertake independent research in bioinformatics/biostatistics.

Person Specification
•   PhD or equivalent experience in Bioinformatics, Biostatistics, Chemometrics or Metabolomics (all with metabolomics or related specialism).
•   Experience in the application of statistical / computational methods to the analysis of metabolomics datasets (either mass spectrometry and/or NMR spectroscopy based).
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•   A high level of accuracy and attention to detail.
•   Ability to work on own initiative, manage time effectively, progress tasks concurrently and work to deadlines.
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10
Metabolomics: Understanding Metabolism in the 21st Century

We are delighted to announce the 5th run of our free online course

An online course by the Birmingham Metabolomics Training Centre, University of Birmingham, UK, hosted by Futurelearn

Date: 15 January - 2 February 2018 (estimated learning time 3 hours per week)

Overview
This free online course will provide an introduction to metabolomics, describe the tools and techniques we use to study the metabolome and explain why we want to study the metabolome. The course will be delivered using a combination of short videos, articles and discussions. We will provide quizzes and tests, so that you can review your learning throughout the course. The material will be delivered over a four week period, with an estimated learning time of three hours per week.

Course Syllabus:
  • Metabolomics and the interaction of the metabolome with the genome, proteome and the environment
  • The advantages of studying the metabolome
  • The application of hypothesis generating studies versus the use of traditional hypothesis directed research
  • The use of targeted and non-targeted studies in metabolomics
  • An interdisciplinary approach with case-studies from clinical and environmental scientific areas
  • Important considerations in studying the metabolome
  • An interdisciplinary approach with case-studies from clinical and environmental scientific areas
  • Important considerations in studying the metabolome
  • Experimental design and sample preparation
  • Multivariate data analysis (including unsupervised and supervised approaches)
  • The importance of statistical validation of results
  • The application of mass spectrometry in metabolomics
  • An introduction to data processing and analysis
  • Metabolite identification

Level: The course is primarily aimed at final year undergraduate science students and research scientists who are interested in learning about the application of metabolomics to understand metabolism. However, the course will provide a valuable introduction to metabolomics to scientists at any stage in their career.

For further information and registration details, please visit https://www.futurelearn.com/courses/metabolomics or contact bmtc@contacts.bham.ac.uk.