The Experts below are selected from a list of 7215 Experts worldwide ranked by ideXlab platform
Namjin Kim - One of the best experts on this subject based on the ideXlab platform.
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real time digital signal processing based on the tms320c6000
2004Co-Authors: Nasser Kehtarnavaz, Namjin KimAbstract:Introduction: Examples of DSP systems, Organization of Chapters, Required Software/Hardware Analog to Digital Signal Conversion: Sampling, Quantization, Signal Reconstruction TMS320C6x Architecture: CPU Operation (Dot Product Example, Pipelined CPU, VelociTI, C64x DSP Software Tools: DSK Target DSP Board, Assembly File, Memory Management, Compiler Utility, Code Initialization, Lab 1: Getting Familiar with Code Composer Studio Interrupt Data Processing, Lab 2: Audio Signal Sampling Fixed-Point vs Floating-Point:Q-Format Number Representation on Fixed-Point DSPs, Finite Word Length Effects on Fixed-Point DSPs, Floating-Point Number Representation, Overflow and Scaling, Some Useful Arithmetic Operations, Lab 3: Integer Arithmetic Code Optimization: Word Wide Optimization, Mixing C and Assembly, Software Pipelining, Lab 4: Real-Time Filtering Circular Buffering: Lab 5: Adaptive Filtering Frame Processing: Direct Memory Access, DSP-Host Communication, Lab 6: Fast Fourier Transform Real-Time Analysis and Scheduling: Real-Time Analysis and Instrumentation, Real-Time Scheduling, Real-Time Data Exchange, Lab 7: DSP/BIOS, Lab 8: Data Synchronization and Communication.
Mario Stanke - One of the best experts on this subject based on the ideXlab platform.
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braker1 unsupervised rna seq based genome annotation with genemark et and augustus
Bioinformatics, 2016Co-Authors: Katharina J Hoff, Simone Lange, Alexandre Lomsadze, Mark Borodovsky, Mario StankeAbstract:MOTIVATION Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. RESULTS We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome Assembly File and a File in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. AVAILABILITY AND IMPLEMENTATION BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ CONTACT katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Nasser Kehtarnavaz - One of the best experts on this subject based on the ideXlab platform.
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real time digital signal processing based on the tms320c6000
2004Co-Authors: Nasser Kehtarnavaz, Namjin KimAbstract:Introduction: Examples of DSP systems, Organization of Chapters, Required Software/Hardware Analog to Digital Signal Conversion: Sampling, Quantization, Signal Reconstruction TMS320C6x Architecture: CPU Operation (Dot Product Example, Pipelined CPU, VelociTI, C64x DSP Software Tools: DSK Target DSP Board, Assembly File, Memory Management, Compiler Utility, Code Initialization, Lab 1: Getting Familiar with Code Composer Studio Interrupt Data Processing, Lab 2: Audio Signal Sampling Fixed-Point vs Floating-Point:Q-Format Number Representation on Fixed-Point DSPs, Finite Word Length Effects on Fixed-Point DSPs, Floating-Point Number Representation, Overflow and Scaling, Some Useful Arithmetic Operations, Lab 3: Integer Arithmetic Code Optimization: Word Wide Optimization, Mixing C and Assembly, Software Pipelining, Lab 4: Real-Time Filtering Circular Buffering: Lab 5: Adaptive Filtering Frame Processing: Direct Memory Access, DSP-Host Communication, Lab 6: Fast Fourier Transform Real-Time Analysis and Scheduling: Real-Time Analysis and Instrumentation, Real-Time Scheduling, Real-Time Data Exchange, Lab 7: DSP/BIOS, Lab 8: Data Synchronization and Communication.
Mark Borodovsky - One of the best experts on this subject based on the ideXlab platform.
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braker1 unsupervised rna seq based genome annotation with genemark et and augustus
Bioinformatics, 2016Co-Authors: Katharina J Hoff, Simone Lange, Alexandre Lomsadze, Mark Borodovsky, Mario StankeAbstract:MOTIVATION Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. RESULTS We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome Assembly File and a File in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. AVAILABILITY AND IMPLEMENTATION BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ CONTACT katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Katharina J Hoff - One of the best experts on this subject based on the ideXlab platform.
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braker1 unsupervised rna seq based genome annotation with genemark et and augustus
Bioinformatics, 2016Co-Authors: Katharina J Hoff, Simone Lange, Alexandre Lomsadze, Mark Borodovsky, Mario StankeAbstract:MOTIVATION Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. RESULTS We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome Assembly File and a File in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. AVAILABILITY AND IMPLEMENTATION BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ CONTACT katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
