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Topics - joergbuescher
The Max Planck Institute of Immunobiology and Epigenetics in Freiburg, Germany, invites applications for the position of a Data Engineer (Proteomics / Metabolomics) in the area of Biological Mass Spectrometry. The position is immediately available for an initial period of two years.
Our Institute investigates the molecular basis of the immune response and other topics of the developmental biology, such as the origin and differentiation of the immune cells as well as the development of vertebrate embryo. Another main focus of the Institute is Epigenetic. This area deals with inheritable traits, which are not caused by changes in the DNA sequence.
We are seeking an innovative and highly motivated data engineer to support the mass spectrometry platform at our institute. The latter consists of two independent core units, metabolomics and proteomics. The successful candidate will develop data analysis and visualization pipelines and apply or adopt existing tools and workflows in order to support statistical and model-based analysis of mass spectrometry-based data. Additional duties will include the maintenance of data bases and servers as well as data archiving and data analysis.
The ideal candidate holds a degree in computer sciences (e.g. information technology, applied informatics, bioinformatics) or life sciences (e.g. biology, biotechnology) with at least three years of hands-on expertise in industry or an academic setting. Programming experience in at least two of the following languages (Python, C++, R, and Perl) and a sound knowledge of Linux/Unix operating systems is essential. Likewise, basic know-how in the design and maintenance of data bases (preferentially SQL) is equally important.
Further knowledge in machine learning (e.g. random forests) or additional programming languages (Visual Basic, Matlab, Fortran, HTML5, PHP) is considered as a plus.
A basic understanding of biochemistry and omics technologies (metabolomics, proteomics or trancriptomics) would be desirable. Strong communication skills in english language are required. This position is suitable for a person who is well organized and able to work independently as well as in a team environment.
A challenging and international work environment with close interactions to cutting edge fundamental biological research. The mass spectrometry platform uses proven-to-work analytical workflows that are implemented on state-of-the-art equipment to generate reliable omics data. Moreover we continue to push the boundaries of what is possible by improving both wetlab and drylab methods. As data engineer you will interact closely with the proteomics and metabolomics core facilities and will have additional exchange with bioinformatics and IT.
Salaries will be based on previous experience according to TVöD guidelines.
Handicapped applicants with equal qualifications will be given preferential treatment. The Max Planck Society seeks to increase the number of women in areas, where they are underrepresented, and therefore explicitly encourages women to apply. A childcare facility is directly attached to the institute.
Have we sparked your interest? Please submit your complete application, including a CV via our online application portal until December 1st, 2017.
Max Planck Institute of Immunobiology and Epigenetics
Looking at the forum, I'm not the only one who seems to struggle with parsing MRM data in R:
However those posts are quite old, and I heard that there is now a package that can import LC-QqQ (aka MRM) data in R. Unfortunately I cannot find it. Any hint would be highly welcome.
My current workaround is to use Proteowizard to convert my .d files to .txt files, for which I then wrote a very simple parser in R. But this is obviously less nice than using a well defined file format like .mzML.
Thanks for your help,
I'm currently analyzing data that I recorded on an Agilent GC-MS (single quad) with XCMS and CAMERA. I use TMS derivatization and a temperature gradient that goes up to 300 C. Because of the temperature I see quite a bit of column bleeding towards the end of the chromatograms, most prominently the masses 207 and 281. Looking at the raw data the peaks that are detected in those two mass traces are quite high above zero, but only slightly above the local baseline. So as long as I do the analysis without peakfill and intb as intensity value, this is not much of an issue.
However if I use peakfill I have the impression that it only fills into. So it adds all those really high intensity signals with the effect that there is a high background signal even in blanks. I tried to get rid of those background signals using the correlation filtering function from CAMERA afterwards, but that did not help much.
I think that ideally peakfill would also do a baseline detection and fill the intb column. Is there a way to do that already?
Do you have any other suggestions how to tackle this issue?
Any hint is more than welcome.