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Thierry Schüpbach

dblp:08/7930 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2 · 2 first-authorSystems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%
Computer architecture, parallel and distributed computing, and storage systems
2 papers
Parallel and multicore computing · 57% High-performance computing · 43%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis › sequence profile analysis
profile search
0.212013
pfsearchV3: a code acceleration and heuristic to search PROSITE profiles · Bioinform. 2013
Bioinformatics and computational biology › protein structure analysis
protein domain identification
0.212013
pfsearchV3: a code acceleration and heuristic to search PROSITE profiles · Bioinform. 2013
Bioinformatics and computational biology › statistical genetics › gene-gene interaction
epistasis analysis
0.112010
FastEpistasis: a high performance computing solution for quantitative trait epistasis · Bioinform. 2010
Bioinformatics and computational biology › genomics
genome-wide association study
0.112010
FastEpistasis: a high performance computing solution for quantitative trait epistasis · Bioinform. 2010
High-performance computing
performance optimization
0.012013
pfsearchV3: a code acceleration and heuristic to search PROSITE profiles · Bioinform. 2013
Parallel and multicore computing › parallel computing
parallel bioinformatics
0.012010
FastEpistasis: a high performance computing solution for quantitative trait epistasis · Bioinform. 2010
Parallel and multicore computing
parallel computing
0.012010
FastEpistasis: a high performance computing solution for quantitative trait epistasis · Bioinform. 2010

Methods — techniques the papers use, named apart from their topics

profile hidden markov model · 0.3heuristic search · 0.3quantitative trait analysis · 0.2parallelization · 0.2
YearPublicationVenuePosition
2018 Towards in the Field Fast Pathogens Detection Using FPGAs
abstract
The rapid detection and identification of infectious pathogens are a critical need for healthcare in both developed and developing countries. There is a need for portable diagnostics and epidemiological surveillance systems that can be deployed in remote field settings for infectious diseases testing, such as pandemic "hotspots" in Africa or southeast Asia. Together with the Vital-IT and Swiss-Prot groups, we developed a fast pathogen detection embedded hardware accelerator (no bigger than 75x50mm) capable of analyzing up to 250Mnt/s of DNA sequence. To deploy this solution, an embedded DNA Next Generation Sequencer (NGS) is used for retrieving the genetic material from patient's sample at the point of care (e.g., the Oxford Nanopore Technologies MinION). Successively, the collected DNA sequences are analized in order to detect the presence of known pathogen patterns. Currently, up to 100 different pathogens can be analized at the same time using one single embedded accelerator. Due to the low-power consumption of this hardware accelerator, it is possible to give access to worldwide health organizations and local administrations to a complete real-time embedded and battery powered surveillance solution. Moreover, this solution can greatly scale to big computing clusters, where a larger pathogens number can be analized in parallel, outperforming current state of art computationally expensive solutions.
Simone Casale Brunet, Thierry Schüpbach, Nicolas Guex, Christian Iseli, Alan J. Bridge, Dmitry Kuznetsov, Christian J. A. Sigrist, Phillippe Lemercier, Ioannis Xenarios, Endri Bezati
FPL2
2013 pfsearchV3: a code acceleration and heuristic to search PROSITE profiles
abstract
SUMMARY: The PROSITE resource provides a rich and well annotated source of signatures in the form of generalized profiles that allow protein domain detection and functional annotation. One of the major limiting factors in the application of PROSITE in genome and metagenome annotation pipelines is the time required to search protein sequence databases for putative matches. We describe an improved and optimized implementation of the PROSITE search tool pfsearch that, combined with a newly developed heuristic, addresses this limitation. On a modern x86_64 hyper-threaded quad-core desktop computer, the new pfsearchV3 is two orders of magnitude faster than the original algorithm. AVAILABILITY AND IMPLEMENTATION: Source code and binaries of pfsearchV3 are freely available for download at http://web.expasy.org/pftools/#pfsearchV3, implemented in C and supported on Linux. PROSITE generalized profiles including the heuristic cut-off scores are available at the same address.
Thierry Schüpbach, Marco Pagni, Alan J. Bridge, Lydie Bougueleret, Ioannis Xenarios, Lorenzo Cerutti
Bioinform.1
2010 FastEpistasis: a high performance computing solution for quantitative trait epistasis
abstract
MOTIVATION: Genome-wide association studies have become widely used tools to study effects of genetic variants on complex diseases. While it is of great interest to extend existing analysis methods by considering interaction effects between pairs of loci, the large number of possible tests presents a significant computational challenge. The number of computations is further multiplied in the study of gene expression quantitative trait mapping, in which tests are performed for thousands of gene phenotypes simultaneously. RESULTS: We present FastEpistasis, an efficient parallel solution extending the PLINK epistasis module, designed to test for epistasis effects when analyzing continuous phenotypes. Our results show that the algorithm scales with the number of processors and offers a reduction in computation time when several phenotypes are analyzed simultaneously. FastEpistasis is capable of testing the association of a continuous trait with all single nucleotide polymorphism (SNP) pairs from 500 000 SNPs, totaling 125 billion tests, in a population of 5000 individuals in 29, 4 or 0.5 days using 8, 64 or 512 processors. AVAILABILITY: FastEpistasis is open source and available free of charge only for non-commercial users from http://www.vital-it.ch/software/FastEpistasis.
Thierry Schüpbach, Ioannis Xenarios, Sven Bergmann, Karen Kapur
Bioinform.1