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100 1  Elnitski, Laura 
245 10 Advances in Genomic Sequence Analysis and Pattern 
       Discovery 
264  1 Singapore :|bWorld Scientific Publishing Co Pte Ltd,|c2011
264  4 |c©2011 
300    1 online resource (236 pages) 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
490 1  Studies Of Nonlinear Phenomena In Life Science ;|vv.7 
505 0  Intro -- Contents -- Preface -- About the Editors -- Part 
       I: Pattern Discovery Methods -- Chapter 1: Large-Scale 
       Gene Regulatory Motif Discovery with NestedMICA Matias 
       Piipari, Thomas A. Down and Tim J. P. Hubbard -- 1. 
       Introduction -- 1.1. Assessment of motif inference tools -
       - 1.2. What is a motif? -- 1.3. Motif inference with 
       additional supporting data -- 1.4. The NestedMICA 
       algorithm -- 1.5. Nested sampling -- 1.6. Mosaic sequence 
       background model -- 2. Results -- 2.1. Choice of sequence 
       regions for motif inference -- 2.2. Finding significant 
       matches -- 2.3. Comparison of NestedMICA Drosophila motifs
       against reference motifs -- 2.4. Sequence conservation 
       analysis of motif matches -- 2.5. Positional bias analysis
       - finding motifs close to transcription start sites -- 
       2.6. Association of motifs with tissue-specific gene 
       expression pattern -- 3. Motif Inference Tutorial -- 3.1. 
       Sequence retrieval and preprocessing -- 3.2. Background 
       model estimation -- 3.3. Motif inference -- 3.4. 
       Visualizing NestedMICA motifs as sequence logos -- 3.5. 
       Motif overrepresentation analysis -- 3.6. Comparison of 
       sequence motifs with a reference motif set -- 4. 
       Conclusions -- References -- Chapter 2: R'MES: A Tool to 
       Find Motifs with a Significantly Unexpected Frequency in 
       Biological Sequences Sophie Schbath and Mark Hoebeke -- 1.
       Introduction -- 2. User Guide -- 2.1. Getting exceptional 
       frequency scores for words -- 2.2. Getting exceptional 
       frequency scores for word families -- 2.3. Analyzing 
       coding DNA sequences -- 2.4. Getting exceptional skew 
       scores -- 2.5. Utilities -- 2.6. Graphical user interface 
       -- 2.7. Implementation details -- 2.7.1. Main data 
       structures and algorithms -- 2.7.2. Space and time 
       complexity -- 2.7.3. Computation time and memory 
       requirements -- 3. Discussion -- 4. Conclusion -- 5. 
       Methods -- 5.1. Markov chain models 
505 8  5.2. Estimated expected counts -- 5.3. Gaussian 
       approximation -- 5.4. Clumping occurrences -- 5.5. 
       Compound Poisson approximation -- References -- Chapter 3:
       An Intricate Mosaic of Genomic Patterns at Mid-range Scale
       Alexei Fedorov and Larisa Fedorova -- 1. Introduction -- 
       2. Results and Discussion -- 2.1. DNA repeats - important 
       elements at genomic mid-range scale -- 2.2. Genomic Mid-
       Range Inhomogeneity (MRI): Nucleotide compositional 
       extremes and sequence nonrandomness -- 2.2.1. Genomic MRI 
       toolkit -- 2.2.2. (G+C)-rich and (A+T)-rich MRI regions 
       are associated with several unusual DNA structures -- 
       2.2.3. R-rich/Y-rich MRI regions are associated with H-DNA
       triplex -- 2.2.4. DNA and RNA properties of GT-rich/AC-
       rich MRI regions -- 2.2.5. Alternated R/Y MRI regions 
       adopt Z-DNA conformation -- 2.3. Weak periodicities and 
       loose patterns -- 2.3.1. Chromatin periodicities -- 2.3.2.
       Periodicities in protein-coding sequences -- 2.3.3. 
       Transcription-associated mutational asymmetry in mammals -
       - 2.4. A complex mosaic of MRI patterns and their 
       fundamental importance -- 2.4.1. Intricate arrangement of 
       genomic MRI patterns -- 2.4.2. The purpose of MRI regions 
       -- 3. Conclusions -- Acknowledgment -- References -- 
       Chapter 4: Motif Finding from Chips to ChIPs Giulio Pavesi
       -- 1. Introduction -- 2. Profile-Based Methods -The Basics
       -- 3. Profile-Based Methods - Modeling the Background -- 
       4. Consensus-Based Methods -The Basics -- 5. Other Methods
       -- 6. Does Motif FindingWork? -- 7. Chips vs ChIPs -- 8. 
       Conclusions -- References -- Chapter 5: A New Approach to 
       the Discovery of RNA Structural Elements in the Human 
       Genome Lei Hua, Miguel Cervantes-Cervantes and Jason T. L.
       Wang -- 1. Introduction -- 2. RelatedWork -- 3. Methods --
       4. Results -- 5. Conclusion -- References -- Part II: 
       Performance and Paradigms 
505 8  Chapter 6: Benchmarking of Methods for Motif Discovery in 
       DNA Kjetil Klepper, Geir Kjetil Sandve, Morten Beck Rye, 
       Kjersti Hysing Bolstad and Finn Drabløs -- 1. Introduction
       -- 2. Score Functions -- 2.1. Scoring by known binding 
       sites -- 2.2. The futility theorem -- 2.3. Alternative 
       scoring -- 3. Benchmark Datasets -- 3.1. Criteria for good
       benchmark datasets -- 3.2. Substring-based datasets -- 
       3.2.1. Synthetic datasets -- 3.2.2. Single motifs -- 
       3.2.3. Regulatory modules -- 3.3. Genome-wide datasets -- 
       4. Benchmarking Without a Benchmark Dataset -- 5. Related 
       Areas -- 6. Conclusion -- Abbreviations -- Acknowledgments
       -- References -- Chapter 7: Encyclopedias of DNA Elements 
       for Plant Genomes Jens Lichtenberg, Alper Yilmaz, Kyle 
       Kurz, Xiaoyu Liang, Chase Nelson, Thomas Bitterman, Eric 
       Stockinger, Erich Grotewold and Lonnie R. Welch -- 1. 
       Introduction -- 2. C-repeat Binding Factor Genes in 
       Triticeae -- 3. Analysis of the Non-coding Segments in 
       Arabidopsis thaliana -- 4. Enhancement of the Arabidopisis
       Gene Regulatory Information Server (AGRIS) -- 5. Methods -
       - 6. Conclusion -- Acknowledgments -- References -- 
       Chapter 8: Manycore High-Performance Computing in 
       Bioinformatics Jean-Stéphane Varré, Bertil Schmidt, 
       Stéphane Janot and Mathieu Giraud -- 1. Introduction -- 
       1.1. A small history of processors -- 1.1.1. Moore's law -
       - 1.1.2. Frequencies and the "power wall" -- 1.1.3. 
       Multicore processors -- 1.1.4. Data-parallelism and SIMD -
       - 1.2. Towards manycore processors -- 1.2.1. GPU 
       processors -- 1.2.2. The CPU/GPU convergence -- 1.2.3. 
       General purpose computation on GPU -- 2. Methods -- 2.1. 
       From GPU tweaks to OpenCL -- 2.2. Programming SIMD work-
       items -- 2.3. Branches and divergence -- 2.4. Work-groups 
       -- 3. Results -- 3.1. Smith-Waterman sequence alignments -
       - 3.1.1. Smith-waterman algorithm -- 3.1.2. Mapping onto 
       SIMD registers 
505 8  3.1.3. Implementation on Cell/BE -- 3.1.4. Intra-task or 
       inter-task parallelization on GPUs -- 3.1.5. Memory 
       optimization on GPUs -- 3.1.6. Performance comparison -- 
       3.2. Algorithms on sequence data -- 3.2.1. RNA folding -- 
       3.2.2. Generic dynamic programming -- 3.2.3. Position 
       Weight Matrices algorithms -- 3.3. Other applications -- 
       3.3.1. Indexing structures -- 3.3.2. Phylogeny -- 3.3.3. 
       Multiple sequence alignment -- 3.3.4. Motif finding -- 
       3.3.5. Hidden Markov Models profiles -- 3.3.6. Cell 
       molecules simulation -- 4. Discussion -- 4.1. Challenges 
       in parallel algorithmics -- 4.2. Challenges for 
       bioinformatics analysis -- 5. Conclusion -- References -- 
       Chapter 9: Natural Selection and the Genome Austin L. 
       Hughes -- 1. Introduction -- 2. The Molecular Revolution -
       - 3. The Neutral Theory -- 4. Positive Selection:The MHC 
       Case -- 5. Codon-Based Methods -- 6. The McDonald-Kreitman
       Test -- 7. Single Nucleotide Polymorphisms -- 8. 
       Conclusions:The Importance of Purifying Selection -- 
       References -- Index 
520    Mapping the genomic landscapes is one of the most exciting
       frontiers of science. We have the opportunity to reverse 
       engineer the blueprints and the control systems of living 
       organisms. Computational tools are key enablers in the 
       deciphering process. This book provides an in-depth 
       presentation of some of the important computational 
       biology approaches to genomic sequence analysis. The first
       section of the book discusses methods for discovering 
       patterns in DNA and RNA. This is followed by the second 
       section that reflects on methods in various ways, 
       including performance, usage and paradigms 
588    Description based on publisher supplied metadata and other
       sources 
590    Electronic reproduction. Ann Arbor, Michigan : ProQuest 
       Ebook Central, 2020. Available via World Wide Web. Access 
       may be limited to ProQuest Ebook Central affiliated 
       libraries 
650  0 Genomes -- Analysis.;Gene mapping -- Methodology.;Gene 
       mapping -- Data processing 
655  4 Electronic books 
700 1  Piontkivska, Helen 
700 1  Welch, Lonnie R 
776 08 |iPrint version:|aElnitski, Laura|tAdvances in Genomic 
       Sequence Analysis and Pattern Discovery|dSingapore : World
       Scientific Publishing Co Pte Ltd,c2011|z9789814327725 
830  0 Studies Of Nonlinear Phenomena In Life Science 
856 40 |uhttps://ebookcentral.proquest.com/lib/sinciatw/
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