Infrastructure research and service

At this place we refer to our web presence on the de.NBI website. de.NBI is the German network for bioinformatics infrastructure. One of the centres is the CIBI (Center for Integrative Bioinformatics) of which our lab is a part of. The CIBI supports KNIME as integration platform and OpenMS [1] and SeqAn [2].

[1] J. Pfeuffer, T. Sachsenberg, O. Alka, M. Walzer, A. Fillbrunn, L. Nilse, O. Schilling, K. Reinert, and O. Kohlbacher, “OpenMS ? A platform for reproducible analysis of mass spectrometry data,” Journal of Biotechnology, vol. 261, p. 142–148, 2017.
[Bibtex]
@article{fu_mi_publications2116,
year = {2017},
month = {November},
pages = {142--148},
title = {OpenMS ? A platform for reproducible analysis of mass spectrometry data},
journal = {Journal of Biotechnology},
author = {Julianus Pfeuffer and Timo Sachsenberg and Oliver Alka and Mathias Walzer and Alexander Fillbrunn and Lars Nilse and Oliver Schilling and Knut Reinert and Oliver Kohlbacher},
volume = {261},
publisher = {ELSEVIER},
url = {http://publications.imp.fu-berlin.de/2116/},
abstract = {Background
In recent years, several mass spectrometry-based omics technologies emerged to investigate qualitative and quantitative changes within thousands of biologically active components such as proteins, lipids and metabolites. The research enabled through these methods potentially contributes to the diagnosis and pathophysiology of human diseases as well as to the clarification of structures and interactions between biomolecules. Simultaneously, technological advances in the field of mass spectrometry leading to an ever increasing amount of data, demand high standards in efficiency, accuracy and reproducibility of potential analysis software.
Results
This article presents the current state and ongoing developments in OpenMS, a versatile open-source framework aimed at enabling reproducible analyses of high-throughput mass spectrometry data. It provides implementations of frequently occurring processing operations on MS data through a clean application programming interface in C++ and Python. A collection of 185 tools and ready-made workflows for typical MS-based experiments enable convenient analyses for non-developers and facilitate reproducible research without losing flexibility.
Conclusions
OpenMS will continue to increase its ease of use for developers as well as users with improved continuous integration/deployment strategies, regular trainings with updated training materials and multiple sources of support. The active developer community ensures the incorporation of new features to support state of the art research.}
}
[2] K. Reinert, T. H. Dadi, M. Ehrhardt, H. Hauswedell, S. Mehringer, R. Rahn, J. Kim, C. Pockrandt, J. Winkler, E. Siragusa, G. Urgese, and D. Weese, “The SeqAn C++ template library for efficient sequence analysis: A resource for programmers,” Journal of Biotechnology, vol. 261, p. 157–168, 2017.
[Bibtex]
@article{fu_mi_publications2103,
publisher = {ELSEVIER},
volume = {261},
author = {Knut Reinert and Temesgen Hailemariam Dadi and Marcel Ehrhardt and Hannes Hauswedell and Svenja Mehringer and Ren{\'e} Rahn and Jongkyu Kim and Christopher Pockrandt and J{\"o}rg Winkler and Enrico Siragusa and Gianvito Urgese and David Weese},
journal = {Journal of Biotechnology},
title = {The SeqAn C++ template library for efficient sequence analysis: A resource for programmers},
month = {November},
pages = {157--168},
year = {2017},
url = {http://publications.imp.fu-berlin.de/2103/},
keywords = {NGS analysis; Software libraries; C++; Data structures},
abstract = {Background
The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 (D{\"o}ring et al., 2008).
Results
The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools.
Conclusions
We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.
Keywords
NGS analysis; Software libraries; C++; Data structures}
}