The Experts below are selected from a list of 92361 Experts worldwide ranked by ideXlab platform

Florian Kronenberg - One of the best experts on this subject based on the ideXlab platform.

  • haplogrep a fast and reliable Algorithm for automatic classification of mitochondrial dna haplogroups
    Human Mutation, 2011
    Co-Authors: Anita Klossbrandstatter, Dominic Pacher, Sebastian Schonherr, Hansi Weissensteiner, Robert Binna, Gunther Specht, Florian Kronenberg
    Abstract:

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-Forward Algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at.

  • haplogrep a fast and reliable Algorithm for automatic classification of mitochondrial dna haplogroups
    Human Mutation, 2011
    Co-Authors: Anita Klossbrandstatter, Dominic Pacher, Sebastian Schonherr, Hansi Weissensteiner, Robert Binna, Gunther Specht, Florian Kronenberg
    Abstract:

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-Forward Algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at. Hum Mutat 31:–8, 2010. © 2010 Wiley-Liss, Inc.

Sebastian Schonherr - One of the best experts on this subject based on the ideXlab platform.

  • haplogrep a fast and reliable Algorithm for automatic classification of mitochondrial dna haplogroups
    Human Mutation, 2011
    Co-Authors: Anita Klossbrandstatter, Dominic Pacher, Sebastian Schonherr, Hansi Weissensteiner, Robert Binna, Gunther Specht, Florian Kronenberg
    Abstract:

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-Forward Algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at.

  • haplogrep a fast and reliable Algorithm for automatic classification of mitochondrial dna haplogroups
    Human Mutation, 2011
    Co-Authors: Anita Klossbrandstatter, Dominic Pacher, Sebastian Schonherr, Hansi Weissensteiner, Robert Binna, Gunther Specht, Florian Kronenberg
    Abstract:

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-Forward Algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at. Hum Mutat 31:–8, 2010. © 2010 Wiley-Liss, Inc.

Tongshun Liu - One of the best experts on this subject based on the ideXlab platform.

  • time varying and condition adaptive hidden markov model for tool wear state estimation and remaining useful life prediction in micro milling
    Mechanical Systems and Signal Processing, 2019
    Co-Authors: Tongshun Liu
    Abstract:

    Abstract The tool wear monitoring (TWM) system which can estimate the tool wear state and predict remaining useful life (RUL) of the tool plays an important role in micro-milling because of the high precision requirement for work-pieces and the high tool wear rate. Due to its ability in modelling the non-stationary physical process, hidden Markov model (HMM) has been broadly used in TWM, but almost all of researches have been done under fixed cutting conditions. In order to monitor tool wear under switching cutting conditions, an improved HMM is proposed in this paper. A hazard model is constructed to describe the time varying and condition adaptive state transition probability. Multilayer perceptron (MLP) which is powerful in approximating a nonlinear function is adopted to compute the observation probability. Then, the state transition probability and observation probability are integrated to estimate the tool wear state and predict the RUL online using Forward Algorithm. Experiments on variant cutting conditions are conducted to verify effectiveness of the proposed model.

  • online tool wear monitoring via hidden semi markov model with dependent durations
    IEEE Transactions on Industrial Informatics, 2018
    Co-Authors: Kunpeng Zhu, Tongshun Liu
    Abstract:

    The tool wear monitoring (TWM) system that could estimate tool wear conditions and predict remaining useful life (RUL) is important to meet the high precision requirement and improve productivity in automated machining. Due to its good properties in representing nonstationary and complex physical process, hidden semi-Markov Model (HSMM) is adapted to model the progressive tool wear in this paper. In order to describe the time-variant transition probability of tool wear states and the state duration dependency, the HSMM is improved by learning the duration parameters and RUL distribution database. The Forward Algorithm is utilized for online tool wear estimation and remaining life prognosis, and an online implementation approach is developed to reduce computational cost. Experimental results show that the approach is effective and the proposed method of duration dependency modeling leads to more accurate TWM in high speed milling.

Anita Klossbrandstatter - One of the best experts on this subject based on the ideXlab platform.

  • haplogrep a fast and reliable Algorithm for automatic classification of mitochondrial dna haplogroups
    Human Mutation, 2011
    Co-Authors: Anita Klossbrandstatter, Dominic Pacher, Sebastian Schonherr, Hansi Weissensteiner, Robert Binna, Gunther Specht, Florian Kronenberg
    Abstract:

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-Forward Algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at.

  • haplogrep a fast and reliable Algorithm for automatic classification of mitochondrial dna haplogroups
    Human Mutation, 2011
    Co-Authors: Anita Klossbrandstatter, Dominic Pacher, Sebastian Schonherr, Hansi Weissensteiner, Robert Binna, Gunther Specht, Florian Kronenberg
    Abstract:

    An ongoing source of controversy in mitochondrial DNA (mtDNA) research is based on the detection of numerous errors in mtDNA profiles that led to erroneous conclusions and false disease associations. Most of these controversies could be avoided if the samples' haplogroup status would be taken into consideration. Knowing the mtDNA haplogroup affiliation is a critical prerequisite for studying mechanisms of human evolution and discovering genes involved in complex diseases, and validating phylogenetic consistency using haplogroup classification is an important step in quality control. However, despite the availability of Phylotree, a regularly updated classification tree of global mtDNA variation, the process of haplogroup classification is still time-consuming and error-prone, as researchers have to manually compare the polymorphisms found in a population sample to those summarized in Phylotree, polymorphism by polymorphism, sample by sample. We present HaploGrep, a fast, reliable and straight-Forward Algorithm implemented in a Web application to determine the haplogroup affiliation of thousands of mtDNA profiles genotyped for the entire mtDNA or any part of it. HaploGrep uses the latest version of Phylotree and offers an all-in-one solution for quality assessment of mtDNA profiles in clinical genetics, population genetics and forensics. HaploGrep can be accessed freely at http://haplogrep.uibk.ac.at. Hum Mutat 31:–8, 2010. © 2010 Wiley-Liss, Inc.

Zaid Alars - One of the best experts on this subject based on the ideXlab platform.

  • efficient acceleration of the pair hmms Forward Algorithm for gatk haplotypecaller on graphics processing units
    Evolutionary Bioinformatics, 2018
    Co-Authors: Shanshan Ren, Koen Bertels, Zaid Alars
    Abstract:

    GATK HaplotypeCaller (HC) is a popular variant caller, which is widely used to identify variants in complex genomes. However, due to its high variants detection accuracy, it suffers from long execution time. In GATK HC, the pair-HMMs Forward Algorithm accounts for a large percentage of the total execution time. This article proposes to accelerate the pair-HMMs Forward Algorithm on graphics processing units (GPUs) to improve the performance of GATK HC. This article presents several GPU-based implementations of the pair-HMMs Forward Algorithm. It also analyzes the performance bottlenecks of the implementations on an NVIDIA Tesla K40 card with various data sets. Based on these results and the characteristics of GATK HC, we are able to identify the GPU-based implementations with the highest performance for the various analyzed data sets. Experimental results show that the GPU-based implementations of the pair-HMMs Forward Algorithm achieve a speedup of up to 5.47× over existing GPU-based implementations.

  • maximizing systolic array efficiency to accelerate the pairhmm Forward Algorithm
    Bioinformatics and Biomedicine, 2016
    Co-Authors: Johan Peltenburg, Shanshan Ren, Zaid Alars
    Abstract:

    In the analysis of next-generation DNA sequencing data, Hidden Markov Models (HMMs) are used to perform variant calling between DNA sequences and a reference genome. The PairHMM model is solved by the Forward Algorithm, for which the performance and power efficiency can be increased tremendously using systolic arrays (SAs) in FPGAs. We model the performance characteristics of such SAs, and propose a novel architecture that allows the computational units to continuously perform useful work on the input data. The implementation achieves up to 90% of the theoretical throughput for a real dataset. The implementation of the proposed architecture achieves more than 2.5× throughput over the state-of-the-art on a similar contemporary platform.

  • fpga acceleration of the pair hmms Forward Algorithm for dna sequence analysis
    Bioinformatics and Biomedicine, 2015
    Co-Authors: Shanshan Ren, Vladmihai Sima, Zaid Alars
    Abstract:

    Many DNA sequence analysis tools have been developed to turn the massive raw DNA sequencing data generated by NGS (Next Generation Sequencing) platforms into biologically meaningful information. The pair-HMMs Forward Algorithm is widely used to calculate the overall alignment probability needed by a number of DNA analysis tools. In this paper, we propose a novel systolic array design to accelerate the pair-HMMs Forward Algorithm on FPGAs. A number of architectural features have been implemented to improve the performance of the design, such as early exit points to increase the utilization of the array for small sequence sizes, as well as on-chip buffering to enable the processing of long sequences effectively. We present an implementation of the design on the Convey supercomputing platform. Experimental results show that the FPGA implementation of the pair-HMMs Forward Algorithm is up to 67x faster, compared to software-only execution.