TY - JOUR A1 - Aiche, Stephan A1 - Reinert, Knut A1 - Schütte, Christof A1 - Hildebrand, Diana A1 - Schlüter, Hartmut A1 - Conrad, Tim T1 - Inferring Proteolytic Processes from Mass Spectrometry Time Series Data Using Degradation Graphs JF - PLoS ONE Y1 - 2012 UR - http://publications.imp.fu-berlin.de/1143/ U6 - https://doi.org/10.1371/journal.pone.0040656 VL - 7 IS - 7 SP - e40656 PB - Public Library of Science ER - TY - JOUR A1 - Bauer, Raphael A1 - Rother, Kristian A1 - Moor, Peter A1 - Reinert, Knut A1 - Steinke, Thomas A1 - Bujnicki, Janusz A1 - Preissner, Robert T1 - Fast Structural Alignment of Biomolecules Using a Hash Table, N-Grams and String Descriptors JF - Algorithms Y1 - 2009 U6 - https://doi.org/10.3390/a2020692 VL - 2 IS - 2 SP - 692 EP - 709 ER - TY - JOUR A1 - Leser, Ulf A1 - Hilbrich, Marcus A1 - Draxl, Claudia A1 - Eisert, Peter A1 - Grunske, Lars A1 - Hostert, Patrick A1 - Kainmüller, Dagmar A1 - Kao, Odej A1 - Kehr, Birte A1 - Kehrer, Timo A1 - Koch, Christoph A1 - Markl, Volker A1 - Meyerhenke, Henning A1 - Rabl, Tilmann A1 - Reinefeld, Alexander A1 - Reinert, Knut A1 - Ritter, Kerstin A1 - Scheuermann, Björn A1 - Schintke, Florian A1 - Schweikardt, Nicole A1 - Weidlich, Matthias T1 - The Collaborative Research Center FONDA JF - Datenbank-Spektrum Y1 - 2021 U6 - https://doi.org/10.1007/s13222-021-00397-5 VL - 21 IS - 3 SP - 255 EP - 260 ER - TY - CHAP A1 - Knaust, Marius A1 - Seiler, Enrico A1 - Reinert, Knut A1 - Steinke, Thomas T1 - Co-Design for Energy Efficient and Fast Genomic Search: Interleaved Bloom Filter on FPGA T2 - FPGA '22: Proceedings of the 2022 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays N2 - Next-Generation Sequencing technologies generate a vast and exponentially increasing amount of sequence data. The Interleaved Bloom Filter (IBF) is a novel indexing data structure which is state-of-the-art for distributing approximate queries with an in-memory data structure. With it, a main task of sequence analysis pipelines, (approximately) searching large reference data sets for sequencing reads or short sequence patterns like genes, can be significantly accelerated. To meet performance and energy-efficiency requirements, we chose a co-design approach of the IBF data structure on the FPGA platform. Further, our OpenCL-based implementation allows a seamless integration into the widely used SeqAn C++ library for biological sequence analysis. Our algorithmic design and optimization strategy takes advantage of FPGA-specific features like shift register and the parallelization potential of many bitwise operations. We designed a well-chosen schema to partition data across the different memory domains on the FPGA platform using the Shared Virtual Memory concept. We can demonstrate significant improvements in energy efficiency of up to 19x and in performance of up to 5.6x, respectively, compared to a well-tuned, multithreaded CPU reference. Y1 - 2022 U6 - https://doi.org/10.1145/3490422.3502366 SP - 180 EP - 189 ER -