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 -- 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.

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.

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.