@article{ZuehlkeRiebeBeitzetal.2016,
  author    = {Z{\"u}hlke, Martin and Riebe, Daniel and Beitz, Toralf and L{\"o}hmannsr{\"o}ben, Hans-Gerd and Andreotti, Sandro and Reinert, Knut and Zenichowski, Karl and Diener, Marc},
  title     = {High-performance liquid chromatography with electrospray ionization ion mobility spectrometry: Characterization, data management, and applications},
  series = {Journal of separation science},
  volume    = {39},
  journal   = {Journal of separation science},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {1615-9306},
  doi       = {10.1002/jssc.201600749},
  pages     = {4756 -- 4764},
  year      = {2016},
  abstract  = {The combination of high-performance liquid chromatography and electrospray ionization ion mobility spectrometry facilitates the two-dimensional separation of complex mixtures in the retention and drift time plane. The ion mobility spectrometer presented here was optimized for flow rates customarily used in high-performance liquid chromatography between 100 and 1500 mu L/min. The characterization of the system with respect to such parameters as the peak capacity of each time dimension and of the 2D spectrum was carried out based on a separation of a pesticide mixture containing 24 substances. While the total ion current chromatogram is coarsely resolved, exhibiting coelutions for a number of compounds, all substances can be separately detected in the 2D plane due to the orthogonality of the separations in retention and drift dimensions. Another major advantage of the ion mobility detector is the identification of substances based on their characteristic mobilities. Electrospray ionization allows the detection of substances lacking a chromophore. As an example, the separation of a mixture of 18 amino acids is presented. A software built upon the free mass spectrometry package OpenMS was developed for processing the extensive 2D data. The different processing steps are implemented as separate modules which can be arranged in a graphic workflow facilitating automated processing of data.},
  language  = {en}
}
@article{PiroDadiSeileretal.2020,
  author    = {Piro, Vitor C. and Dadi, Temesgen H. and Seiler, Enrico and Reinert, Knut and Renard, Bernhard Y.},
  title     = {ganon},
  series = {Bioinformatics},
  volume    = {36},
  journal   = {Bioinformatics},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {1367-4811},
  doi       = {https://doi.org/10.1093/bioinformatics/btaa458},
  pages     = {12 -- 20},
  year      = {2020},
  abstract  = {Motivation: The exponential growth of assembled genome sequences greatly benefits metagenomics studies. However, currently available methods struggle to manage the increasing amount of sequences and their frequent updates. Indexing the current RefSeq can take days and hundreds of GB of memory on large servers. Few methods address these issues thus far, and even though many can theoretically handle large amounts of references, time/memory requirements are prohibitive in practice. As a result, many studies that require sequence classification use often outdated and almost never truly up-to-date indices. Results: Motivated by those limitations, we created ganon, a k-mer-based read classification tool that uses Interleaved Bloom Filters in conjunction with a taxonomic clustering and a k-mer counting/filtering scheme. Ganon provides an efficient method for indexing references, keeping them updated. It requires <55 min to index the complete RefSeq of bacteria, archaea, fungi and viruses. The tool can further keep these indices up-to-date in a fraction of the time necessary to create them. Ganon makes it possible to query against very large reference sets and therefore it classifies significantly more reads and identifies more species than similar methods. When classifying a high-complexity CAMI challenge dataset against complete genomes from RefSeq, ganon shows strongly increased precision with equal or better sensitivity compared with state-of-the-art tools. With the same dataset against the complete RefSeq, ganon improved the F1-score by 65\% at the genus level. It supports taxonomy- and assembly-level classification, multiple indices and hierarchical classification.},
  language  = {en}
}