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BME 599 (3 credits)Computational Tools for Genomic TechnologiesI. Logistics Days/Times:Tuesday/Thursday 9:00 AM-10:30 AM Classroom:FXB 1032Instructors:Carlos Aguilar, Ph.D., Department of Biomedical EngineeringOffice Hours:Thursdays right after classE-mail:caguilar@umich.edu Prerequisites:This class requires programming experience and some background in biology (Introductory Biology course). The object-oriented language Python and the Bioconductor package within R will be utilized. If possible, students should try to bring a laptop to participate in class. Laptops generally require 8 GB of RAM to run some of the programs.Required Textbook:Assigned readings will be from the primary literature. However, the following texts may be useful as references:Zvelebil, Marketa, and Jeremy O. Baum. Understanding Bioinformatics. New York, NY: Garland Science, 2007. ISBN: 9780815340249.Alon, Uri. An Introduction to Systems Biology: Design Principles of Biological Circuits. Boca Raton, FL: Chapman & Hall, 2006. ISBN: 9781584886426. (A systems biology look from a physicist's perspective.).Klipp, Edda, Ralf Herwig, et al. Systems Biology in Practice: Concepts, Implementation and Application. Wiley Blackwell, 2005. ISBN: 9783527310784.Alberts, Bruce, Alexander Johnson, et al. Molecular Biology of the Cell. 4th ed. Garland Science, 2002. ISBN: 9780849371615.II. Course Description The dramatic reductions in cost and accessibility of next-generation sequencing technologies has facilitated new approaches to understand disease and cellular biology. Understanding how to read sequencing datasets is not only incredibly useful for researchers seeking to glean insights into their own experiments but also the capacity to generate data-driven hypotheses. The overarching goal of this class is to develop an understanding of foundational methods in bioinformatics to enable engineering students to process, contextualize and visualize basic high-throughput sequencing experiments with a focus on RNA and gene expression. III. Instruction Format and Grading A typical class will include two modules. The first module will focus on lecture of the specific topic, provide relevant examples, and question/answer periods. The second module will focus on in class programming and data interpretation. Active participation is suggested, in the form of pre-lecture reading and asking/answering questions during class. The grading scheme for class is distributed between 4 homeworks (12.5 pts each x 4 = 50 total) and a final project (30 pts for written report, 20 pts for presentation = 50 points total). IIIa. HomeworksNo late homework will be accepted. Four HWs will be assigned. These are designed to promote deeper understanding of the principles and algorithms discussed in class and to provide hands-on experience with bioinformatics tools. The scripting language for the homeworks on single cell RNA sequencing datasets will be implemented in R—which is widely used for bioinformatics and computational analysis of single cells. IIIb. Project The project is designed to give students practice in applying computational methods to contemporary problems in genomics. Students design and carry out projects working in a group (2 or 3). There is grading information below the steps of the project.Project TeamsBy 1/14: Submit a list of your team members, and team name. By 1/23: Submit a one paragraph summary of the research problem your team will investigate. We suggest that you choose people to work with who have skills complementary to yours. You are encouraged to choose an area that is relevant to your current or planned research. If you do not have data available to use for this project, a suitable publicly available dataset may be found. Project Specific AimsBy 2/11: Each team submits a one-page set of NIH grant-style specific aims that describe your project. Your aims should correspond with goals you can reasonably achieve during the term. Your aims will be evaluated and returned with comments to give you initial feedback on your formulation of your problem area. A sample specific Aims page and some advice for each part of a Specific Aims is listed here.Project Research StrategyBy 2/25: Each team submits a one-page (single spaced) research strategy describing the specific approach that will be used and the specific data that will be analyzed, and a paragraph on anticipated findings and the sequence of research activities that will validate the findings.Project Written ReportBy 4/14: Each team submits a minimum of three-page (single spaced) written report that summarizes your findings. (Figures are included in the page limit, but references are not.) Each member of your group should author a clearly identified section of the report with their specific contribution. This report will be graded as described below.Your research strategy and results section should be structured to include the following sections, with the bulk of the text in the Approach and Results section. (If you have multiple Specific Aims, then you may address Significance, Innovation and Approach for each Specific Aim individually, or may address Significance, Innovation and Approach for all of the Specific Aims collectively.)Significance Explain the importance of the problem or critical barrier to progress in the field that the proposed project addresses.Explain how the proposed project will improve scientific knowledge, technical capability, and/or clinical practice in one or more broad fields.Describe how the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field will be changed if the proposed aims are achieved.Innovation Explain how the work challenges and seeks to shift current research or clinical practice paradigms.Describe any novel theoretical concepts, approaches or methodologies, instrumentation or interventions developed or used, and any advantage over existing methodologies, instrumentation, or interventions.Explain any refinements, improvements, or new applications of theoretical concepts, approaches or methodologies, instrumentation, or interventions.Approach and Results Provide an introduction to the area, describing previous work.Describe the overall strategy, methodology, and analyses to be used to accomplish the specific aims of the project. Include how the data was collected, analyzed, and interpreted.Describe the results of your project in terms of your aim(s).The page limit is to ensure that the report focuses on the key aspects of the problem method. Section (C) 1 should allow a colleague who is not expert in the specific research area to understand the report. Figures or tables may be helpful.Formatting: Use 11 or 12-point font with at least one-inch margins, page numbers at the bottom, single spacing. All figures must have legends.IV. Other PoliciesRegrades: Requests for regrades on homework must be made within 3 days of the release of your graded materials. Formal regrade requests must be sent to Prof. Aguilar in writing and explain exactly where points were deducted and why you believe your answer deserves more points. Prof. Aguilar reserves the right to refuse any requests that don’t meet these requirements. Academic Integrity: Students enrolled in this course are bound by the Michigan Engineering Honor Code. Students are allowed (and encouraged) to discuss how to approach homework problems and work together on group projects. However, deliberately copying another student’s work on homework such as exact code is plagiarism and constitutes a violation of the Honor Code. Any suspected violations of the Honor Code will be formally submitted to the Honor Council and handled at the CoE level. Complete details of the Honor Code and student expectations can be read here: Services: If you require any academic accommodations please reach out to the instructor as soon as possible so we can discuss your needs. You may be required to register with the Office of Services for Students with Disabilities (SSD). The mission of the SSD is to support the University’s commitment to equity and diversity by providing support services and academic accommodations to students with disabilities. The SSD office is located at G-664 Haven Hall and can be reached at (734) 763-3000 or ssdoffice@umich.edu. Please be aware that a delay in getting SSD accommodation letters for the current semester may hinder the availability or facilitation of those accommodations in a timely manner. Therefore, it is in your best interest to get your accommodation letters as early in the semester as possible. To learn more about your rights and responsibilities please visit: Mental Health and Well-Being: If you are experiencing stress, anxiety, depression, etc. and think you may need assistance, counseling and psychological services (CAPS) are freely available through the University. Follow the link below for more information and for a complete list of health and wellness resources and services provided by the University’s Office of Student Support and Accountability. Observances: The University of Michigan, as an institution, doesnot observe religious holidays. However, every reasonable effort will be made to help students avoid negative academic consequences when their religious obligations conflict with academic requirements. If you find that an exam or assignment due date conflicts with a religious observance, it is your obligation to let Prof. Aguilar know at least 2 weeks in advance of the conflict and before the drop/add deadline for the semester (whichever comes first). You will be given every opportunity to make up the work without penalty, unless it interferes unreasonably with course delivery. Read the University’s full policy here: is recommended that you review all other general UMich CoE policies, academic rules, information and more in the online bulletin, which can be found at . Tentative Outline (course topics and schedule may change based on progress) TopicDateHWsProjectProgramIntroduction to Sequencing Methods1/09Principles of Sequencing by Synthesis1/14HW1-AForm teamReading Sequencing Data Syntax1/16FastQCDownloading & Transforming datasets & Library Complexity1/21 Short Read Alignment1/23 HW1-DParagraphShort Read Alignment Using Indexing, Suffix Arrays & Burrow-Wheeler1/28 Quantification of Expression & Isoforms with Pseudo-Alignment1/30 KallistoAssessment of Differential Expression & Isoforms2/4HW2-ADESEQ2Quantitation of Differential Expression 2/6 Tour & Working w/ UofM DNA Sequencing Core 2/11 Clustering & Principal Component Analysis2/13 HW2-DIGVVisualizing Data with Sequencing Browsers2/18Aims PageGGplot Pathway Analysis of Differential Expression2/20 DAVIDCase Analysis of RNA Sequencing Datasets2/25 HW3-ACase Analysis of RNA Sequencing Datasets2/27 Mid-Winter Break3/3 & 3/5Introduction to Single Cell RNA-Seq3/10 Class Cancelled from COVID-193/12 SeuratIntroduction to Single Cell RNA-Seq Cont’d3/17SeuratRNA Sequence Analysis from Single Cells Bootcamp - Preprocessing3/19HW3-DStrategySeuratRNA Sequence Analysis from Single Cells Bootcamp – Normalizing the Data 3/24 SeuratGuest Lecture on Single Cell Sequencing (JW)3/26 In Class Coding Session3/31 HW4-ASeuratRNA Sequence Analysis from Single Cells Bootcamp – Dimensionality Reduction & Clustering4/2 SeuratRNA Sequence Analysis from Single Cells Bootcamp – Integration Step 14/7 SeuratIn Class Coding Session4/9RNA Sequence Analysis from Single Cells Bootcamp – Differential Expression & Cell Type Annotation4/14 HW4-DReportVI. Requested Readings TopicRequested ReadingsIntroduction to High-Throughput Sequencing MethodsShendure, J. et al. “DNA Sequencing at 40: past, present and future.” Nature 550, 345-355 (2017).Shendure, J, Lieberman-Aiden, E.”The expanding scope of DNA sequencing.” Nature Biotechnology 30, no. 11, 1084-1094 (2012).Goodwin, S. et al. "Coming of age: ten years of next-generation sequencing technologies." Nature Reviews Genetics 17, no. 6 (2016): 333–351.Part I: Principles of Sequencing by SynthesisMetzker, Michael L. "Sequencing Technologies—The Next Generation." Nature Reviews Genetics 11, no. 1 (2010): 31–46.Maxam, A. M., and W. Gilbert. "A New Method for Sequencing DNA." PNAS 74, no. 2 (1977): 560–4.Sanger, F., S. Nicklen, et al. "DNA Sequencing with Chain-Terminating Inhibitors." PNAS 74, no. 12 (1977): 5463–7.Ju, J., et al. "4-color DNA sequencing by synthesis using cleavable fluorescent nucleotide reversible terminators." PNAS 103, no. 52 (2006): 19635-19640.Bentley, D.R., et al. "Accurate whole human genome sequencing using reversible terminator chemistry." Nature 456, (2008): 53-59.Reading Sequencing Data SyntaxHandouts Library GenerationHead, Stephen. et al. “Library construction for next-generation sequencing: Overviews and challenges.” Biotechniques 56(2) (2014): 61-77.Library Complexity & Short Read AlignmentDaley, T., Smith, A.D. Predicting the molecular complexity of sequencing libraries. Nature Methods 10 (2013): 325-327.Langmead, Ben, Cole Trapnell, et al. "Ultrafast and Memory–efficient Alignment of Short DNA Sequences to the Human Genome." Genome Biology 10, no. 3 (2009): R25.Li, Heng, and Richard Durbin. "Fast and Accurate Short Read Alignment with Burrows–wheeler Transform." Bioinformatics 25, no. 14 (2009): 1754–60.Bray, N.L., Pimentel, H., Melsted, P., Pachter, L. “Near-optimal probabilistic RNA-Seq quantification" Nature Biotechnology 34, (2016) 525-527.Trapnell, Cole, and Steven L. Salzberg. "How to Map Billions of Short Reads onto Genomes." Nature Biotechnology 27, no. 5 (2009): 455.Bowtie: An ultrafast memory–efficient short read aligner.RNA-Sequence Analysis – Expression & IsoformsTrapnell, Cole, Brian A. Williams, et al. "Transcript Assembly and Quantification by RNA–seq Reveals Unannotated Transcripts and Isoform Switching during Cell Differentiation." Nature Biotechnology 28, no. 5 (2010): 511–5.Anders, Simon, and Wolfgang Huber. "Differential Expression Analysis for Sequence Count Data." Genome Biology 11, no. 10 (2010): R106.Love, Michael, Huber, Wolfgang. Anders, Simon "Moderated estimation of fold change and dispersion for RNA-Seq data with DESEQ2" Genome Biology 15, no. 12 (2014): 550.Conesa, A., Madrigal, P. et al "A survey of best practices for RNA-Seq analysis" Genome Biology 17, no. 13 (2016): 181.Wang, Zhong, Mark Gerstein, et al. "RNA–Seq: a Revolutionary Tool for Transcriptomics." Nature Reviews Genetics 10, no. 1 (2009): 57–63.Shalek, Alex K., Rahul Satija, et al. "Single–cell Transcriptomics Reveals Bimodality in Expression and Splicing in Immune Cells." Nature 498 (2013): 236–40.Clustering & Principal Component AnalysisSmith, Lindsay I. "A Tutorial on Principal Components Analysis." (PDF) February 26, 2002.Ramoni, M.F., P. Sebastiani, and I.S. Kohane. "Cluster analysis of gene expression dynamics." Proc Natl Acad Sci U S A 99, no. 14 (2002): 9121-6.Goh, K. I., et al. "Classification of scale-free networks." Proc Natl Acad Sci U S A 99, no. 20 (2002): 12583-8.Eddy, S. R. ”What is Bayesian statistics" Nature Biotech. 22, (2004): 1177-1178.Pathway Analysis of Differential ExpressionSubramanian, Aravind, et al. “Gene set enrichment analysis: A knowledge based approach for interpreting genome-wide expression profiles.” Proc Natl Acad Sci U S A 102, no. 43 (2005): 15545-15550.Visualizing Data with Sequencing BrowsersLi, Daofeng. Et al. “WashU Epigenome browser update 2019.” Nucleic Acid Res. 47:1 (2019), W158-W165.Robinson, J.T. et al. “Integrative genomics viewer.” Nature Biotech. 29 (2011) 24-26.Case Analysis of RNA Sequencing DatasetsCacchiarelli, D. et al. “Integrative analysis of human reprogramming reveal dynamic nature of induced pluripotency.” Cell 162 (2015), 412-424. ................
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