Skip to main content

Recent Posts

1
Post: Postdoctoral Fellow in Mass Spectrometry-based Metabolomics
Department: IU Bloomington Public & Environmental Affairs

Full text: https://indiana.peopleadmin.com/postings/6871

Position Summary:
We are recruiting a postdoctoral fellow in Mass Spectrometry-based Metabolomics at Indiana University Bloomington to participate in an exciting, new collaborative project called PhyloTox, which seeks to identify the evolutionary origins of molecular toxicity pathways. Using transcriptomics and metabolomics data collected from a group of model species/cells exposed to a carefully selected suite of chemicals, biological insights will be drawn from the perturbation of entire genetic and biochemical networks via chemical ablation. The ultimate goal of the project is to develop a novel precision environmental health program to help solve the enormous environmental health crisis caused by environmental pollution.

For this position, we seek a postdoctoral fellow in Mass Spectrometry Metabolomics to focus on the application of metabolic phenotyping across a six model organisms/cell lines, applying primarily non-targeted LC-MS strategies. The post-holder will perform sample preparation applying manual and robotic approaches, LC-MS instrument maintenance and operation to acquire highly reproducible data in a high-throughput laboratory, and metabolite identification. They will contribute to study design and analytical method development in cutting edge, biomedical computational and statistical analysis.

The project involves working with several PIs and laboratories and, thus, collaborative skills and results-oriented project management are required. The position is localized at Indiana University Bloomington in the School of Public and Environmental Affairs, Department of Chemistry, Department of Environmental Health, and Department of Intelligent Systems Engineering. The position also includes a unique training opportunity under the guidance of Prof Mark Viant in the world class metabolomics facility of the University of Birmingham, UK (https://www.birmingham.ac.uk/staff/profiles/biosciences/viant-mark.aspx). This position will either be assigned to the Department of Chemistry or the School of Public and Environmental Affairs, whichever is the best fit for the successful candidate.

Questions regarding the position or application process can be directed to: Drs. Joseph Shaw (joeshaw@indiana.edu) or Stephen Jacobson (jacobson@indiana.edu).

To apply: Interested candidates should review the application requirements and submit their application online: https://indiana.peopleadmin.com/. The application should consist of a cover letter stating your accomplishments and interest in the project's research, curriculum vitae, and letters of support from at least two references. Review of applications will begin immediately and continue until the position is filled. Applications received by November 15, 2018 will receive full consideration.
2
XCMS Online / Re: Questions about LCMS data preprocessing R vignette
Last post by sneumann -
So,

COURTNEY SCHIFFMAN wrote:
> In the vignette you say "We set it to 20,80 for the present example
> data set" when referring to the peakwidth parameter for centWave, but
> in the actual function you use 30,80.

Ah, that discrepancy is clearly a typo then.

> This makes a big difference, which do you recommend?

That really depends on the chromatography and gradient used.
E.g., on a 20 minute UPLC gradient we went down to c(5,12).

One way to check is to plot what you actually get:

        hist(peaks(xs)[,"rtmax"]-peaks(xs)[,"rtmin"], breaks=100)

This shows the peakwidths distribution found in your data set,
and you can try a few different peakwidths ranges to see
what peakwidths are then found. Beware: if you select blatantly wrong,
e.g. c(30,80) on the above UPLC gradient,
you will still find "something". But the histogram helps to figure out
whether the majority of peakwidths is within your peakwidth range.

> Why do you use minFraction = 0.8 for PeakDensityParam
> but minFraction = 0.85 for PeakGroupsParam?

I was not aware of that difference, but that threshhold
does depend on the size(s) of your sample groups, and how
homogenous you'd expect them to be, and how much "noise"
you'd accept after grouping.

On Tue, 2018-10-16 at 19:55 -0700, COURTNEY SCHIFFMAN wrote:
> ...
> Why with the snthresh=10 in "CentWaveParam" are there still
> chromatographic peaks with an sn less than 10 after running
> "findChromPeaks"?

I had to dig the exact answer from the code:
https://github.com/sneumann/xcms/blob/eb6c61d2f081ea7ac6aeb1aa958f8a52fb70a91d/R/do_findChromPeaks-functions.R#L950

The summary is that the threshhold is calculated as
  sdthr <- sdnoise * snthresh

and the SN you see in the peaks table is

  https://github.com/sneumann/xcms/blob/eb6c61d2f081ea7ac6aeb1aa958f8a52fb70a91d/R/do_findChromPeaks-functions.R#L1066
  round((maxint - baseline) / sdnoise), ##  S/N Ratio

So indeed there is some room for confusion.

Yours,
Steffen

3
XCMS Online / Questions about LCMS data preprocessing R vignette
Last post by courtneys -

I have two questions on the vignette for the xcms R package, 'LCMS data preprocessing and analysis with xcms.'

In the vignette it says "We set it to 20,80 for the present example data set" when referring to the peakwidth parameter for centWave, but in the actual function they use 30,80. This makes a big difference in the Faahko data set, which is recommended?

Why do they use minFraction = 0.8 for PeakDensityParam but minFraction = 0.85 for PeakGroupsParam?
4
Other / Re: Feature intensity drift correction suggestions
Last post by Jan Stanstrup -
Here are some more options. I have only tried the one from Wehrens that seems to work just fine for me.

https://github.com/rwehrens/BatchCorrMetabolomics
https://gitlab.com/CarlBrunius/batchCorr
http://www.metabolomics-shanghai.org/softwaredetail.php?id=39
5
Other / Feature intensity drift correction suggestions
Last post by ab123 -
I've got around 100 samples that suffer from feature intensity drift. Even the first five QCs (so in a matter of 30 injections) show drift in feature intensity sequentially (so from QC1 to QC5 there's drift visible in the PCA).

Apart from intCor, which didn't really help, does anyone have any good suggestions on how to correct this please?

Appreciate any help at this point.

Thank you.
6
Venue: Online
Date: 8 October 2018 - 2 November 2018 (~4 hours per week)
Level: The course would be ideally suited to MSc / PhD students or scientists who are in the early stages of analysing metabolomics data. No previous knowledge of the data processing and statistical analysis approaches is assumed, but a basic understanding of the metabolome, and the analytical techniques applied in the metabolomics field would be beneficial. A pre-course recommended reading list will be provided.

Overview
This online course explores the tools and approaches that are used to process and analyse metabolomics data. You will investigate the challenges that are typically encountered in the analysis of metabolomics data, and provide solutions to overcome these problems. The course is delivered using a combination of short videos, articles, discussions, and online workshops with step-by-step instructions and test data sets. We provide quizzes, polls and peer review exercises each week, so that you can review your learning throughout the course.

The material is delivered over a four week period, with an estimated learning time of four hours per week. We support your learning via social discussions where you will be able post questions and comments to the team of educators and the other learners on the course. In the final week of the course there is a live question and answer session with the entire team of educators.  If you do not have time to complete the course during the 4-week period you will retain access to the course material to revisit, as you are able.

Topics include:
  • An introduction to metabolomics
  • An overview of the untargeted metabolomics workflow
  • The influence of experimental design and data acquisition on data analysis and data quality
  • Processing of NMR data
  • Processing direct infusion mass spectrometry data
  • Processing liquid chromatography-mass spectrometry data
  • Reporting standards and data repositories
  • Data analysis, detecting outliers and drift, and pre-treatment methods
  • Univariate data analysis
  • Multivariate data analysis (including unsupervised and supervised approaches)
  • The importance of statistical validation of results
  • Computational approaches for metabolite identification and translation of results into biological knowledge
  • What are the future challenges for data processing and analysis in metabolomics

For further information and registration details, please visit https://www.birmingham.ac.uk/facilities/metabolomics-training-centre/courses/Metabolomics-Data-Processing-and-Data-Analysis.aspx or contact bmtc@contacts.bham.ac.uk.
7
Venue: Birmingham Metabolomics Training Centre, School of Biosciences, University of Birmingham, Birmingham, UK.
Date: 22-23 November 2018
Level: The course is suitable for PhD students and post-doctoral researchers who have been actively applying metabolomics for a minimum of 6 months. If students or researchers would like to take the course but do not have the recommended level of experience please contact the course administrator for advice.

Overview
This 2-day course provides a hands-on approach to teach attendees about the latest techniques and tools available to perform metabolite identification in non-targeted metabolomics studies. The course is led by experts working within the field of metabolomics, and will include a significant proportion of hands-on experience of using mass spectrometers, software tools and databases. A maximum of four people will be working on each mass spectrometer (Q Exactive and LTQ-Orbitrap Elite) in a session.

Topics include:
  • Importance of mass spectral interpretation
  • Types of data which can be collected on the QE and LTQ-Orbitrap Elite (m/z, retention time, MS/MS, MSn)
  • Conversion of raw data to molecular formula and putative metabolite annotations
  • MS/MS experiments in metabolic phenotyping for on-line data acquisition using the QE (Data Dependent Analysis, Data Independent Analysis)
  • MS/MS and MSn experiments for sample fractions using the LTQ-Orbitrap Elite
  • Mass spectral libraries (using mzCloud)
  • Searching mass spectral libraries
  • Tools for mass spectral interpretation
  • In silico fragmentation (using MassFrontier)
  • Reporting standards for metabolite identification
  • Question and answer session with the experts

For further information and registration details, please visit https://www.birmingham.ac.uk/facilities/metabolomics-training-centre/courses/metabolite-identification.aspx or contact bmtc@contacts.bham.ac.uk.
8
Venue: Birmingham Metabolomics Training Centre, School of Biosciences, University of Birmingham, Birmingham, UK.
Date: 19-21 November 2018
Level: The course is aimed at individuals with minimal previous experience of applying LC-MS in metabolomics studies. A working knowledge of LC-MS would be advantageous.

Overview
This 3-day course introduces you to using the Q Exactive mass spectrometer in your metabolomics investigations. The course is led by experts in the field of metabolomics and includes lectures, laboratory sessions and computer workshops to provide a detailed overview of the metabolomics pipeline applying the Q Exactive mass spectrometer.

Topics include:
  • Introduction to Metabolomics on the Q Exactive, the metabolomics workflow, and case studies using the Q Exactive
  • Using the Q Exactive family of instruments in your metabolomics investigations
  • Experimental design and the importance of quality control samples
  • Sample preparation including polar and non-polar preparation methods on biofluids (urine and plasma) and tissue samples
  • Preparation of samples for profiling and targeted analyses on the Q Exactive
  • Hands-on data acquisition for profiling and targeted studies, setting up the Vanquish UHPLC coupled to the Q Exactive MS
  • Data processing workshop
  • Data analysis workshop (univariate and multivariate analysis)
  • Introduction to metabolite identification applying Data Dependent Analysis and Data Independent Analysis
  • Question and answer session with a panel of experts
  • Tips and tricks
  • Problem solving

For further information and registration details, please visit https://www.birmingham.ac.uk/facilities/metabolomics-training-centre/courses/q-exactive.aspx or contact bmtc@contacts.bham.ac.uk.
9
Venue: Birmingham Metabolomics Training Centre, School of Biosciences, University of Birmingham, Birmingham, UK.
Date: 22 October 2018
Level: The course is suitable for individuals with no previous experience of metabolomics.

Overview
This 1-day course in partnership with the Phenome Centre Birmingham provides clinicians with an overview of the metabolomics pipeline highlighting the benefits of this technique to the medical field and an introduction to the Phenome Centre Birmingham and the MRC-NIHR National Phenome Centre.

The course provides a suitable introduction to metabolomics prior to taking additional training courses at either the Birmingham Metabolomics Training Centre or the Imperial International Phenome Training Centre.  

Topics include:

  • Introduction to the Phenome Centre Birmingham, showcasing facilities and expertise available.
  • Introduction to metabolomics
  • Importance of experimental design and sample collection
  • Overview of the technologies available for data acquisition including discovery phase profiling and targeted analysis for the validation of biomarkers
  • Overview of data analysis approaches
  • Case studies - large-scale metabolic phenotyping, translation to targeted assays and clinical practice
  • Question and answer session with the experts

For further information and registration details, please visit https://www.birmingham.ac.uk/facilities/metabolomics-training-centre/courses/introduction-metabolomics.aspx or contact bmtc@contacts.bham.ac.uk.
10
XCMS - FAQ / xcms does not execute
Last post by jcmartin -
I changed my computer and re-installed the whole packages, with the latest R version (3.5.1). Since then, xcms does not work anymore, remaining stacked at the first line

xset<-xcmsSet(method="centWave", peakwidth=c(3,20), snthresh=5, mzdiff=0.01, ppm=3, prefilter=c(5,100000), noise=20000, integrate=1)
xset

a warning message indicate :
Scanning files in directory C:/users/.... (blablabla until the correct file directory).. found 99 files

I also tried with the old R version that I installed and that used to work, but no success either

I'm not a beginner with xcms, and I tried to process my files using another computer in the lab and everything is alright.

I'm using windows 10 with an dell computer equipped with a Xeon processor

any idea of what'sgoing wrong?

many thanks
jc