Cornell University Registrar

This class is designed to inspire students in the life sciences to see the power of Computational Biology in advancing fundamental research. The course will initially be team taught by experts in diverse applications of Computational Biology, with each sharing the excitement of making clear to students how the introduction of computational approaches has been transformative in their field. The course will also teach many fundamental skills in manipulating large data sets, including genome sequences, functional genomic data, images, protein structures, etc.. The course will consist of two lectures each week and one practical exercise session each week. In the practicals, students will learn the use of many of the latest software tools, and will develop some basic programming skills. Students will learn essential problem solving skills in several aspects of genome analysis, and modeling in neurobiology, ecology, physiology and evolution. This will prepare them for future courses in these more focused biological areas covering specific applications of computer programming as well as use of specialized software tools. Students will be able to explore their own specific interests in greater depth on a term project.

Life is dynamic and ever-changing. Because of this, a central tool used to study living systems are dynamic models. This course provides an introductory survey of the development, computer implementation, and applications of dynamic models in biology, as well as statistical and machine learning methods for building such models from biological data. Case-study format covering broad range of biological applications including gene regulation, neurobiology, physiology, behavior, epidemiology and ecology. Students learn mathematical methods for interpreting and building biological systems models, as well as computational methods for simulating models on the computer using a scripting and graphics environment.

A biomedical data science course using Python and available bioinformatics tools and techniques for the analysis of molecular biological data, including biosequences, microarrays, and networks. This course emphasizes practical skills rather than theory. Topics include advanced Python programming, R and Bioconductor, sequence alignment, MySQL database (DBI), web programming and services (CGI), genomics and proteomics data mining and analysis, machine learning, and methods for inferring and analyzing regulatory, protein-protein interaction, and metabolite networks.

Population genetics is the study of the transmission of genetic variation through time and space. This course will provide a comprehensive introduction to the fundamental concepts and methods in population genetics, with a focus on exploring how patterns of genetic variation are connected to the underlying evolutionary processes. Topics include genetic drift, mutation, coalescence theory, demography, population structure, selection, fitness, quantitative traits, selective sweeps, and adaptation at the molecular level. Emphasis is placed on the interplay between theory, computer simulations, and the analysis of genetic data from natural as well as experimental populations. We will also discuss efforts to connect genotype with phenotype and ultimately fitness. Specific case studies will include the evolution of drug resistance, genetic ancestry mapping, experimental evolution of microbes, and the genetic structure and demographic history of human populations.

A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference. Prior knowledge of biology is not necessary to complete this course.Grad students must do a final project that involves original research and that in most circumstances will involve programming and real data.Undergrads will not need to do research but will do a final project that involves a sizeable amount of programming, comprehensive literature review of a topic, or similar.

This course covers the latest development and cutting-edge research topics in population genetics, aiming to enable students to perform research in population genetics. The first part will cover coalescent theory and inference involving complex demography. The second part will discuss natural selection and methods for inferring selection. We will allude to the complexity of demographic history and natural selection and their importance in explaining genomic patterns. The third part will introduce new data types and the challenges and opportunities with these data. We will dive into genotype likelihood and will emphasize the importance of simulation in population genetics. The course will be mostly delivered through lecturing, each interspersed with short conversations about reading assignments. Coursework involves reading literature, solving problem sets, and a course project.

This class is designed to inspire students in the life sciences to see the power of Computational Biology in advancing fundamental research. The course will initially be team taught by experts in diverse applications of Computational Biology, with each sharing the excitement of making clear to students how the introduction of computational approaches has been transformative in their field. The course will also teach many fundamental skills in manipulating large data sets, including genome sequences, functional genomic data, images, protein structures, etc.. The course will consist of two lectures each week and one practical exercise session each week. In the practicals, students will learn the use of many of the latest software tools, and will develop some basic programming skills. Students will learn essential problem solving skills in several aspects of genome analysis, and modeling in neurobiology, ecology, physiology and evolution. This will prepare them for future courses in these more focused biological areas covering specific applications of computer programming as well as use of specialized software tools. Students will be able to explore their own specific interests in greater depth on a term project.

A biomedical data science course using Python and available bioinformatics tools and techniques for the analysis of molecular biological data, including biosequences, microarrays, and networks. This course emphasizes practical skills rather than theory. Topics include advanced Python programming, R and Bioconductor, sequence alignment, MySQL database (DBI), web programming and services (CGI), genomics and proteomics data mining and analysis, machine learning, and methods for inferring and analyzing regulatory, protein-protein interaction, and metabolite networks.

Life is dynamic and ever-changing. Because of this, a central tool used to study living systems are dynamic models. This course provides an introductory survey of the development, computer implementation, and applications of dynamic models in biology, as well as statistical and machine learning methods for building such models from biological data. Case-study format covering broad range of biological applications including gene regulation, neurobiology, physiology, behavior, epidemiology and ecology. Students learn mathematical methods for interpreting and building biological systems models, as well as computational methods for simulating models on the computer using a scripting and graphics environment.

Population genetics is the study of the transmission of genetic variation through time and space. This course will provide a comprehensive introduction to the fundamental concepts and methods in population genetics, with a focus on exploring how patterns of genetic variation are connected to the underlying evolutionary processes. Topics include genetic drift, mutation, coalescence theory, demography, population structure, selection, fitness, quantitative traits, selective sweeps, and adaptation at the molecular level. Emphasis is placed on the interplay between theory, computer simulations, and the analysis of genetic data from natural as well as experimental populations. We will also discuss efforts to connect genotype with phenotype and ultimately fitness. Specific case studies will include the evolution of drug resistance, genetic ancestry mapping, experimental evolution of microbes, and the genetic structure and demographic history of human populations.

A rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology used to analyze disease and agriculturally relevant and evolutionarily important phenotypes. Topics include mapping quantitative trait loci (QTLs), application of microarray and related genomic data to gene mapping, and evolutionary quantitative genetics. Analysis techniques include association mapping, interval mapping, and analysis of pedigrees for both single and multiple QTL models. Application of classical inference and Bayesian analysis approaches is covered and there is an emphasis on computational methods.

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference. Prior knowledge of biology is not necessary to complete this course.Grad students must do a final project that involves original research and that in most circumstances will involve programming and real data.

Graduate seminar on current topics in population genetics. Readings are chosen primarily from current scientific literature. Participation in discussion and presentation of at least one paper required for course credit.

This course covers the latest development and cutting-edge research topics in population genetics, aiming to enable students to perform research in population genetics. The first part will cover coalescent theory and inference involving complex demography. The second part will discuss natural selection and methods for inferring selection. We will allude to the complexity of demographic history and natural selection and their importance in explaining genomic patterns. The third part will introduce new data types and the challenges and opportunities with these data. We will dive into genotype likelihood and will emphasize the importance of simulation in population genetics. The course will be mostly delivered through lecturing, each interspersed with short conversations about reading assignments. Coursework involves reading literature, solving problem sets, and a course project.

Weekly seminar series on recent advances in computational genomics. A selection of the latest papers in the field are read and discussed. Methods are stressed, but biological results and their significance are also addressed.

Weekly seminar series on recent advances in quantitative genomics. A selection of the latest papers in the field is read and discussed. Methods are stressed, but biological results and their significance are also addressed.

Graduate seminar on methods for building models of biological systems using data, with an emphasis on recent methods including machine learning tools. Students will read and discuss recent literature in this area and, through group discussions, develop strategies for applying methods within their own research domains. Participation in discussion and presentation of at least one paper required for course credit.

Weekly seminar series on recent advances in statistical genetics. A selection of the latest papers in the field are read and discussed. Methods are stressed, but biological results and their significance are also addressed.