Critically examine how data analysis can support legitimate conclusions from biological datasets and also how biased sampling, misleading comparisons, and spurious reasoning can lead to false conclusions. Analyze data to break down the logical flow of an argument and identify key assumptions, even when they are not stated explicitly.
Real-world biological datasets to implement fundamental concepts of efficient algorithm design. Synthesize previously acquired knowledge and skills in biology and computer science to analyze, implement, and apply algorithms that process biological datasets, including large-scale datasets.
Reflect upon experiential learning projects and build professional skills, including oral and a hard-copy written presentations, building a curriculum vitae or resumé, preparing job or graduate school application, listening and responding to other student presentations.
In BDS 411, we will explore the way that professional data scientists report biological data. From Science Magazine to 23andMe, this course will examine real biological data from world-class research labs and major corporations—and effective ways of communicating data science results. We will learn to identify the ‘Big Picture’ underlying data science investigations, generate testable hypotheses, and support analyses with eye-popping visuals. By the end of the quarter, students will have mastered several methods for communicating data science and learn to formulate research plans of their own. During the course, students will produce a data visualization dashboard that analyzes a real industry-produced dataset and an independent research proposal in the style of the NSF Graduate Research Fellowship Program.
This course fulfills the Baccalaureate Core requirement for the Writing Intensive Curriculum (WIC) category for students majoring in Biological Data Sciences majors.
Functional genomics describes a set of conceptual approaches and associated laboratory techniques that rely on large-scale DNA sequence datasets to investigate the function of, and interactions between, genes as well as their RNA/protein products. This course will provide an overview of these techniques, including a) approaches to predicting protein function based on sequence analysis, b) large-scale genetic approaches to identifying novel genotype-phenotype associations, and c) transcriptomic, proteomic and metabolomic approaches that reveal gene functions by measuring changes in abundance/modification of associated RNA transcripts, proteins and metabolites.
Introduction to management of large datasets (e.g., nucleic acids, protein), computer programming languages, application of basic mathematical functions, and assembly of computational pipelines pertinent to life sciences.
In this course, students will explore machine learning – the concept of using pattern recognition algorithms to answer biological data analysis questions. The course provides a broad overview of machine learning approaches to problems in biological data analysis, and will focus on understanding the basic concepts necessary to effectively apply several popular ‘supervised learning’ techniques. Students will expand their ability to analyze large datasets, and discover new computational methods to identify biologically meaningful elements in genome scale data. Skills in biological applications that center on recognizing useful patterns in genome-scale datasets will be developed, with emphasis on carefully considered scientific interpretation of machine learning model outcomes. Covers use of Python Scikit-Learn libraries for implementation of model-based analyses.
The genome - that is, the entirety of the genetic information (DNA) that lies at the center of every living organism - plays a major role in defining biological species, from prokaryotic microbes to eukaryotic multicellular organisms. Genomes underlie and influence all biological phenomena, from biochemistry and development to evolution and ecology. However, genomes are huge - for example, the human genome is ~3 billion nucleotides in size - and so, until recently, very difficult to investigate. Recent technological advances, particularly in DNA sequencing, are producing unprecedented quantities of genome-scale data and enabling new insights into how genomes work. Incorporating these new types of information, BOT/BDS 474 Introduction to Genome Biology will first provide an overview of the ways in which genomes are organized and evolve, for example, via mutation or via mobile genetic elements like transposons. The course will then cover the mechanisms (e.g., transcriptional and epigenetic regulation) that link genomes to the characteristics of organisms - i.e., to phenotypes. Students will expand their domain knowledge in basic genetic, biochemical and cellular mechanisms, and will begin to learn how to ‘think big’ about experimental approaches to understanding genomes.
Introduction to comparative genomics. Methods for genome assembly and annotation. Genomic approaches for the study of structural change, whole genome duplication, and gene family evolution. Lab topics include the analysis of next generation sequencing data and conducting comparative genomic analyses.
In this course, students will learn fundamentals in generating and using contemporary genomics data to study evolution of plant associated microbial communities as examples. Students are expected to have a fundamental understanding of genetic concepts and be familiar with evolutionary theory related to populations such as gene flow, genetic drift, selection, and mutation rates. In this course, students will learn genomic approaches including, but not limited to, comparative genomics, genome-wide association mapping, expression analysis, and metagenomics. These genomic approaches will be compared and contrasted and framed around fundamental concepts in genetics and evolution. Upon completion of the course, students will be proficient in formulating approaches for studying genetic and phenotypic diversity of populations. Further, students will be adept in analyzing and critiquing primary literature and developing related hypotheses testable with genomics data.
Quantitative skills and biological thinking will be used to analyze and draw conclusions from real-world biological datasets. Projects will be completed in the context of small groups. This is a synthesis course that draws skills in mathematics, statistics, computer science, and biology.
Quantitative skills and biological thinking will be used to analyze and draw conclusions from biological datasets retrieved in BDS 491. This is a synthesis course that draws skills in mathematics, statistics, computer science, and biology, in which the students will process their curated datasets and draw conclusions.
BDS 599: These are a series of courses offered by the Center for Genome Research and Biocomputing. Topics include:
Please note that courses are offered in different terms. Learn more about course workshops offered through CRGB.