Browse: Keyword: Sequence Analysis

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Bioinformatics and Proteomics, January (IAP) 2005

Abstract:: This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.

"Computational Biology: Genomes, Networks, Evolution, Fall 2008"

Abstract:: " This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include: Genomes: biological sequence analysis, hidden Markov models, gene finding, RNA folding, sequence alignment, genome assembly Networks: gene expression analysis, regulatory motifs, graph algorithms, scale-free networks, network motifs, network evolution Evolution: comparative genomics, phylogenetics, genome duplication, genome rearrangements, evolutionary theory, rapid evolution "

Abstract:: Introduction to computational biology including the fundamentals of protein and nucleic acid sequence analysis, phylogenetic analysis, motif finding, hidden Markov models, and 3D structure prediction and modeling. An overview of emerging fields including expression profiling, quantitative image analysis and the modeling of cellular signal transduction networks are also included. Subject designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science but not necessarily both. Two self-study tracks are offered, introducing either basic statistical methods and programming (to biologists) or the fundamentals of molecular biology (to computer scientists). Lectures combine both perspectives to illustrate how computation is having a significant impact on modern biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

No Strings Attached

Abstract:: Trong Bioinformatics, sắp gióng cột trình tự được dùng để sắp xếp các trình tự DNA, RNA hoặc protein để xác định những vùng tương đồng có thể có sự bảo tồn về cấu trúc, chức năng hoặc có mối quan hệ tiến hóa giữa chúng với nhau. Trong quá trình sắp gióng cột, các khoảng trống (gap, được kí hiệu bằng dấu –) được chèn vào giữa vị trí các nucleotide hay amino acid để các trình tự có sự tương tự nhau nhiều nhất trong mỗi cột (Hình 1.1.) .

No Strings Attached

Abstract:: This is a tour of several bioinformatics web sites that contain a nice variety of tools for data mining. This is an introductory tour that does not include any tutorials for using these tools, but is only intended to acquaint the novice with this type of