Gibas, Cynthia, and Per Jambeck. Developing Bioinformatics Computer Skills. O’ Reilly R Computer Lab. 4 Developing Your Computer Skills for Bioinformatics Liu L, Pearl DK: Species trees from gene trees: reconstructing Bayesian. Introduction to Bioinformatics Sequence Alignment 1 Outline Introduction to sequence Compare the two sequences, see if they are similar • Example: pear and tear . Developing Bioinformatics Computer Skills – Cynthia Gibas, Per Jambeck. Computer Science and Robotics,Artificial Intelligence,Neural Networks,IT 12 Essential Skills for Software Architects. An Introduction to Bioinformatics Algorithms (Computational Molecular Biology) .. Android Wireless Application Development, 2nd Edition (Developer’s Library) Cynthia Gibas, Per Jambeck.
|Published (Last):||4 January 2016|
|PDF File Size:||10.29 Mb|
|ePub File Size:||17.24 Mb|
|Price:||Free* [*Free Regsitration Required]|
Course Description This is a course on the fundamentals of bioinformatics geared towards second-year undergraduate students, who are interested in the principle knowledge in bioinformatics and biostatistics.
The course will cover a lot of research fields in both bioinformatics and biostatistics but won t go too detail, with a number of interesting topics including biological side of bioinformatics, mathematics in bioinformatics, computer techniques for bioinformatians, biological sequence analysis, structural analysis, microarray analysis, systems biology.
There will also be some additional topics such as parameter estimation, hypothesis testing, survival analysis, multivariate analysis, etc. The course aims at guiding you into the kingdom of bioinformatics and biostatistics, with ease step and great comfort.
Prerequisites Knowledge of Calculus and Probability is preferred but not a must. Textbooks and References 1. Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press 2. Andreas Baxevanis and Francis Ouellett eds.
Skills.cynthix Bioinformatics Computer Skills. There will be no midterm exam and final exam. Policy on Collaboration You may discuss with your peers when preparing your homework solutions.
Developing Bioinformatics Computer Skills – O’Reilly Media
However, duplicating is not acceptable. If you do collaborate on homework, you must cite, in your solution, your partners. Additionally, if you consult an expert, do not forget to cite the source in your solutions. Pairwise statistical significance and empirical determination of effective gap opening penalties for protein local sequence alignment. Agrawal A, Huang X: Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty.
BMC Bioinformatics10 Suppl 3: The estimation of statistical parameters for local alignment gibax distributions. Nucleic Acids Res29 2: Mitrophanov AY, Borodovsky M: Statistical significance in biological sequence analysis.
Brief Bioinform7 1: Bayesian selection of continuous-time Markov chain evolutionary models.
Mol Biol Evol18 6: PCA and clustering reveal alternate mtdna phylogeny of N and M clades. J Mol Evol67 Gambin A, Slonimski PP: Hierarchical clustering based bioinforrmatics contextual alignment of proteins: C R Biol1: Cotta C, Moscato P: A memetic-aided approach to hierarchical clustering from distance matrices: Biosystems72 Bioinformatics23 2: Distance-based genome rearrangement phylogeny.
J Mol Evol63 4: Cheon S, Liang F: Bayesian phylogeny analysis via stochastic approximation Monte Carlo. Mol Phylogenet Evol53 2: Mitochondrial sequence data and Dipsacales phylogeny: Mol Phylogenet Evol46 3: Liu L, Pearl DK: Species trees from gene trees: Syst Biol56 3: Bayesian logistic regression using a perfect phylogeny. Biostatistics8 1: Skills.cyntbia mixed models and the phylogeny of pitvipers Viperidae: Mol Phylogenet Skills.cynthis39 1: Maximum-likelihood methods for phylogeny estimation.
Methods Enzymol Zhang H, Gu X: Maximum likelihood for genome phylogeny on gene content.
Developing Bioinformatics Computer Skills
Stat Appl Genet Mol Biol3: Compuyer algorithm-based maximum-likelihood analysis for molecular phylogeny. J Mol Evol53 Computational advances in maximum likelihood methods for molecular phylogeny.
Genome Res8 3: Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Mol Biol Evol10 6: Hasegawa M, Fujiwara M: Relative efficiencies of the maximum likelihood, maximum parsimony, and neighbor-joining methods for estimating protein phylogeny.
Clmputer Phylogenet Evol2 1: Aggarwal G, Ramaswamy R: Ab initio gene identification: J Biosci27 1 Suppl 1: Genome Res17 9: Burset M, Guigo R: Evaluation of gene structure prediction programs. Genomics34 3: Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources.
BMC Bioinformatics7: Stanke M, Waack S: Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics19 Suppl 2: Comparative ab initio prediction of gene structures using pair HMMs. Bioinformatics18 Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training. Genome Res18 A brief review of computational gene prediction methods.
Genomics Proteomics Bioinformatics2 J Theor Biol3: Juan V, Wilson C: RNA secondary structure prediction based on free energy and phylogenetic cimputer.
J Mol Biol4: Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Secondary structure prediction of interacting RNA molecules. J Mol Biol5: Statistical and Bayesian approaches to RNA secondary structure prediction. Rna12 3: Prediction of RNA secondary structure by free energy minimization.
Curr Opin Struct Biol16 3: RNA secondary structure prediction. Unit Zhao Y, Wang Z: Prediction of RNA secondary structure with pseudoknots using integer programming.
BMC Bioinformatics10 Suppl 1: Supervised principal component analysis for gene set enrichment of microarray data with continuous or survival outcomes. Bioinformatics24 Zhao Q, Sun J: