Biological Data Sciences (BDS)
BDS 211, USE AND ABUSE OF DATA: CRITICAL THINKING IN SCIENCE, 3 Credits
Critically examine how data analysis can support legitimate conclusions from biological datasets and also how deceptive visualizations, 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.
Prerequisite: (MTH 251 (may be taken concurrently) with C- or better or MTH 251H (may be taken concurrently) with C- or better) or MTH 227 with C- or better or MTH 241 with C- or better or MTH 245 with C- or better
BDS 311, COMPUTATIONAL APPROACHES FOR BIOLOGICAL DATA, 3 Credits
The theory and practice underlying widely used computational methods for biological data analysis. Focuses on the analysis and visualization of large data sets using Python, with broad applications to genomics, ecology, and other disciplines. Topics may include image processing, time series analysis, dimensionality reduction, and resampling methods. Develops student expertise in designing and implementing algorithms in the Python programming language.
Prerequisite: (BI 223 with C- or better or BI 223H with C- or better) and (MTH 252 [C-] or MTH 252H [C-] or MTH 228 [C-]) and (CS 161 [C-] or BDS 470 [C-] or BOT 470 [C-] or BOT 476 [C-])
BDS 406, SPECIAL PROJECTS, 1-99 Credits
This course is repeatable for 99 credits.
BDS 411, ^ANALYSIS OF BIOLOGICAL DATA: CASE STUDIES, 3 Credits
Case studies; synthesize previously acquired knowledge and skills in biology, mathematics, statistics, and computer science to implement, in writing, an analysis strategy. (Writing Intensive Course)
Attributes: CWIC – Core, Skills, WIC
Prerequisite: ((BI 311 with C- or better or BI 311H with C- or better) or (BB 314 with C- or better or BB 314H with C- or better) or MB 310 with C- or better) and ((MTH 252 with C- or better or MTH 252H with C- or better) or MTH 228 with C- or better) and CS 261 [C-] and (ST 352 [C-] or ST 412 [C-])
BDS 446, NETWORKS IN COMPUTATIONAL BIOLOGY, 3 Credits
Emphasizes computational and applied mathematical methods for modeling and analyzing biological networks. Covers various network centralities, topological measures, clustering algorithms, probabilistic annotation models and inference methods. Introduces those concepts in the context of protein interaction, gene regulatory, and metabolic networks. Uses graph frameworks, data frames (and related data structures for data science), and programming in Python or R. CROSSLISTED as BDS 446/CS 446 and CS 546.
Prerequisite: CS 161 with C or better or BDS 470 with C or better or BOT 470 with C or better or BOT 476 with C or better
Equivalent to: CS 446
Recommended: Completion or concurrent enrollment in CS 325
BDS 470, INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES, 3 Credits
Covers the basics of writing a well-organized computer program to perform tasks that are commonly needed for effective data analysis in the life sciences. Incorporates reading data from a variety of file formats, parsing relevant information from data which comes in as text, putting this information into storage structures that make sense for the task at hand, applying basic mathematical functions to the data, and writing results to an output file. Provides students with the foundation to rapidly expand their knowledge of Python and other programming languages as needed in the future. CROSSLISTED as BDS 470/BOT 470 and BDS 570.
Equivalent to: BOT 470, BOT 476
Recommended: CS 161 or exposure to programming logic
BDS 472X, ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS, 3 Credits
Provides a broad overview of machine learning or “pattern recognition” approaches to problems in biological data analysis. Focuses on understanding the basic concepts necessary to effectively apply several popular ‘supervised learning’ techniques. Develops skills in biological applications that center on recognizing useful patterns in genome-scale datasets, 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. CROSSLISTED as BDS 472X/BOT 472X and BDS 572X/BOT 572X.
Prerequisite: BOT 476 with C- or better or BOT 470 with C- or better or CS 162 with C- or better
Equivalent to: BOT 472X
Recommended: BDS 311 and BDS 474/BOT 474
BDS 474X, INTRODUCTION TO GENOME BIOLOGY, 3 Credits
Explores how genomes underlie and influence biological phenomena, across the diversity of life, from prokaryotic microbes to eukaryotic multicellular organisms. Covers genome organization: the structure of chromosomes and chromatin; genes and gene families; and mechanisms that remodel genomes, such as mutation, recombination and transposable elements in the first part of the course. Focuses on genome expression and regulation: gene expression, cellular functions and biochemical pathways; transcriptional and post-transcriptional regulatory mechanisms; and genotype-to-phenotype relationships in the second part of the course. Emphasizes the use of recent technological advances and genome-wide assays that enable investigation of these topics.
Prerequisite: BI 311 (may be taken concurrently) with C- or better or BB 314 (may be taken concurrently) with C- or better
Equivalent to: BOT 474X
BDS 475, COMPARATIVE GENOMICS, 4 Credits
Explores principles of comparative genomics. Examines methods for genome assembly and annotation. Discusses genomic approaches for the study of structural change, whole genome duplication, gene family evolution, gene networks, gene regulation and epigenetics. Lab topics include the analysis of next generation sequencing data and conducting comparative genomic analyses. CROSSLISTED as BDS 475/BOT 475 and BDS 575/BOT 575/MCB 575.
Prerequisite: (BB 314 with D- or better or BB 314H with D- or better) and (BI 311 [D-] or BI 311H [D-] or PBG 430 [D-])
Equivalent to: BOT 475
Recommended: Basic working knowledge of cell and molecular biology and genetics
BDS 477X, POPULATION GENOMICS, 3 Credits
Enables translation of fundamental knowledge in genetics and genomics to the study of evolution and gene function in populations. Applies skills in computational biology to process, analyze, and draw conclusions from genomic datasets at the population and ecosystem level. CROSSLISTED as BDS 477X/BOT 477X and BDS 577X/BOT 577X.
Prerequisite: (BI 311 with C- or better or BI 311H with C- or better or PBG 430 with C- or better) and (BDS 474 [C-] or BOT 474 [C-] or BOT 474X [C-] or BDS 474X [C-] or BB 314 [C-]) and (BDS 470 [C-] or BOT 470 [C-] or BOT 476 [C-] or CS 161 [C-])
Equivalent to: BOT 477X
BDS 478, FUNCTIONAL GENOMICS, 3 Credits
Introduces conceptual approaches and associated laboratory techniques that rely on genome-scale datasets to investigate the function of, and interactions between, genes as well as their RNA/protein products. Examples include: predicting protein function based on nucleotide and amino acid sequence analysis; large-scale genetic approaches to identifying novel genotype-phenotype associations; and analysis of transcriptomic, proteomic and metabolomic datasets, which measure changes in RNA transcripts, proteins and metabolites, respectively, to explore gene function and cellular/organismal networks. Provides a conceptual framework for understanding how the wide range of available large-scale technologies can be applied to solve biological problems. CROSSLISTED as BDS 478/BOT 478 and BDS 578/BOT 578.
Prerequisite: BB 314 with C- or better or BB 314H with C- or better
BDS 491, CAPSTONE PROJECTS IN BIOLOGICAL DATA SCIENCE I, 3 Credits
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. Draws on skills in mathematics, statistics, computer science, and biology.
Prerequisite: (ST 352 with C- or better or ST 412 with C- or better) and (CS 162 [C-] or BDS 470 [C-] or BOT 470 [C-] or BOT 476 [C-] or BB 485 [C-] or MTH 427 [C-])
BDS 492, CAPSTONE PROJECTS IN BIOLOGICAL DATA SCIENCE II, 3 Credits
Quantitative skills and biological thinking will be used to analyze and draw conclusions from biological datasets retrieved in BDS 412. 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.
Prerequisite: BDS 491 with C- or better
BDS 570, INTRODUCTION TO COMPUTING IN THE LIFE SCIENCES, 3 Credits
Covers the basics of writing a well-organized computer program to perform tasks that are commonly needed for effective data analysis in the life sciences. Incorporates reading data from a variety of file formats, parsing relevant information from data which comes in as text, putting this information into storage structures that make sense for the task at hand, applying basic mathematical functions to the data, and writing results to an output file. Provides students with the foundation to rapidly expand their knowledge of Python and other programming languages as needed in the future. CROSSLISTED as BDS 470/BOT 470 and BDS 570.
BDS 572X, ADVANCED COMPUTING FOR BIOLOGICAL DATA ANALYSIS, 3 Credits
Provides a broad overview of machine learning or “pattern recognition” approaches to problems in biological data analysis. Focuses on understanding the basic concepts necessary to effectively apply several popular ‘supervised learning’ techniques. Develops skills in biological applications that center on recognizing useful patterns in genome-scale datasets, 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. CROSSLISTED as BDS 472X/BOT 472X and BDS 572X/BOT 572X.
Equivalent to: BOT 572X
BDS 574X, INTRODUCTION TO GENOME BIOLOGY, 3 Credits
Explores how genomes underlie and influence biological phenomena, across the diversity of life, from prokaryotic microbes to eukaryotic multicellular organisms. Covers genome organization: the structure of chromosomes and chromatin; genes and gene families; and mechanisms that remodel genomes, such as mutation, recombination and transposable elements in the first part of the course. Focuses on genome expression and regulation: gene expression, cellular functions and biochemical pathways; transcriptional and post-transcriptional regulatory mechanisms; and genotype-to-phenotype relationships in the second part of the course. Emphasizes the use of recent technological advances and genome-wide assays that enable investigation of these topics.
Equivalent to: BOT 574X
BDS 575, COMPARATIVE GENOMICS, 4 Credits
Explores principles of comparative genomics. Examines methods for genome assembly and annotation. Discusses genomic approaches for the study of structural change, whole genome duplication, gene family evolution, gene networks, gene regulation and epigenetics. Lab topics include the analysis of next generation sequencing data and conducting comparative genomic analyses. CROSSLISTED as BDS 475/BOT 475 and BDS 575/BOT 575/MCB 575.
Equivalent to: BOT 575, MCB 575
Recommended: BB 314 and (BI 311 or PBG 430) and basic working knowledge of cell and molecular biology and genetics
BDS 577X, POPULATION GENOMICS, 3 Credits
Enables translation of fundamental knowledge in genetics and genomics to the study of evolution and gene function in populations. Applies skills in computational biology to process, analyze, and draw conclusions from genomic datasets at the population and ecosystem level. CROSSLISTED as BDS 477X/BOT 477X and BDS 577X/BOT 577X.
Equivalent to: BOT 577X
BDS 578, FUNCTIONAL GENOMICS, 3 Credits
Introduces conceptual approaches and associated laboratory techniques that rely on genome-scale datasets to investigate the function of, and interactions between, genes as well as their RNA/protein products. Examples include: predicting protein function based on nucleotide and amino acid sequence analysis; large-scale genetic approaches to identifying novel genotype-phenotype associations; and analysis of transcriptomic, proteomic and metabolomic datasets, which measure changes in RNA transcripts, proteins and metabolites, respectively, to explore gene function and cellular/organismal networks. Provides a conceptual framework for understanding how the wide range of available large-scale technologies can be applied to solve biological problems. CROSSLISTED as BDS 478/BOT 478 and BDS 578/BOT 578.
Equivalent to: BOT 560, BOT 578
BDS 599, SPECIAL TOPICS, 1-4 Credits
This course is repeatable for 99 credits.