Schedule

All course delivery for Spring 2020 will be online via this public facing website. New class content will be posted on a weekly basis throughout the quarter. Clicking on the topics below will take you to corresponding video lectures, hands-on “lab sessions” supporting walk-through screencasts, required reading material and homework assignments.


#WeekTopics for Spring 2020
103/30/20Getting Oriented
Course introduction, Learning goals & expectations, Meet the instructional team. Setup your computer with required software.
204/06/20Welcome to Bioinformatics
Biology is an information science, History of Bioinformatics, Types of data, Application areas and introduction to upcoming course segments, Hands on with major Bioinformatics databases and key online NCBI and EBI resources
304/13/20Sequence alignment fundamentals, algorithms and applications
Homology, Sequence similarity, Local and global alignment, classic Needleman-Wunsch, Smith-Waterman and BLAST heuristic approaches, Hands on with dot plots, Needleman-Wunsch and BLAST algorithms highlighting their utility and limitations
*04/20/20Project: Find a gene project assignment
(Part 1) Principles of database searching, due in 3 weeks. (Part 2) Sequence analysis, structure analysis and general data analysis with R due at the end of the quarter.
404/20/20Bioinformatics data analysis with R
Why do we use R for bioinformatics? R language basics and the RStudio IDE, Major R data structures and functions, Using R interactively from the RStudio console
504/27/20Data exploration and visualization in R
The exploratory data analysis mindset, Data visualization best practices, Simple base graphics (including scatterplots, histograms, bar graphs, dot chats, boxplots and heatmaps), Building more complex charts with ggplot.
605/04/20Writing your own R functions and using packages from CRAN, BioConductor and GitHub
The basics of writing your own functions that promote code robustness, reduce duplication and facilitate code re-use. Extending functionality and utility with R packages from CRAN and BioConductor, Working with Bio3D for molecular data.
Project: Pt 1. Q1-Q4 due!
705/11/20Machine learning for Bioinformatics
Unsupervised learning, K-means clustering, Hierarchical clustering, Heatmap representations. Dimensionality reduction, Principal Component Analysis (PCA)
805/18/20Genome informatics and high throughput sequencing
Searching genes and gene functions, Genome databases, Variation in the Genome, High-throughput sequencing technologies, biological applications, bioinformatics analysis methods; The Galaxy platform along with resources from the EBI & UCSC
905/25/20Transcriptomics, RNA-Seq analysis, and the interpretation of gene lists
RNA-Seq aligners, Differential expression tests, RNA-Seq statistics, Counts and FPKMs and avoiding P-value misuse, Hands-on analysis of RNA-Seq data with R. Gene function annotation, Functional databases KEGG, InterPro, GO ontologies and functional enrichment analysis.
1006/01/20Course summary
Summary of learning goals, Student course evaluation time;
Project: Find a gene assignment due!

Class material

Week 1: Getting oriented

Topics:
Course introduction, Learning goals & expectations, Meet the instructional team. Setup your computer with required software.

Goals:

  • Understand course scope, expectations, logistics and ethics code.
  • Complete the pre-course questionnaire.
  • Setup your computer for this course.

Videos:

Supporting material:

Week 2: Welcome to Bioinformatics

Topics: Biology is an information science, History of Bioinformatics, Types of data, Application areas and introduction to upcoming course segments, Introduction to NCBI & EBI resources for the molecular domain of bioinformatics, Hands-on session using NCBI-BLAST, Entrez, GENE, UniProt, Muscle and PDB bioinformatics tools and databases.

Goals:

  • Understand the increasing necessity for computation in modern life sciences research.
  • Get introduced to how bioinformatics is practiced.
  • Be able to query, search, compare and contrast the data contained in major bioinformatics databases (GenBank, GENE, UniProt, PFAM, OMIM, PDB) and describe how these databases intersect.
  • The goals of the hands-on session is to introduce a range of core bioinformatics databases and associated online services whilst actively investigating the molecular basis of several common human disease.

Videos:

Supporting Material:

Homework:

Week 3: Sequence alignment fundamentals, algorithms and applications

Topics: Sequence Alignment and Database Searching: Homology, Sequence similarity, Local and global alignment, Heuristic approaches, Database searching with BLAST, E-values and evaluating alignment scores and statistics.

Goals:

  • Be able to describe how dynamic programming works for pairwise sequence alignment.
  • Appreciate the differences between global and local alignment along with their major application areas.
  • Understand how aligning novel sequences with previously characterized genes or proteins provides important insights into their common attributes and evolutionary origins.
  • The goals of the hands-on session are to explore the principles underlying the computational tools that can be used to compute and evaluate sequence alignments.

Videos:

Supporting Material:

Homework:

Readings:


(Project:) Find a Gene Assignment Part 1

The find-a-gene project is a required assignment for BIMM-143. The objective with this assignment is for you to demonstrate your grasp of database searching, sequence analysis, structure analysis and the R environment that we have covered to date in class.

You may wish to consult the scoring rubric at the end of the above linked project description and the example report for format and content guidance.

Your responses to questions Q1-Q4 are due Tuesday May 5th (05/05/20) at 12pm San Diego time.

The complete assignment, including responses to all questions, is due Friday June 5th (06/05/20) at 12pm San Diego time.

In both instances your PDF format report should be submitted to GradeScope. Late responses will not be accepted under any circumstances.

Week 4: Bioinformatics data analysis with R

Topics: Why do we use R for bioinformatics? R language basics and the RStudio IDE, Major R data structures and functions, Using R interactively from the RStudio console.

Goal:

  • Understand why we use R for bioinformatics
  • Familiarity with R’s basic syntax,
  • Familiarity with major R data structures (vectors, data.frames and lists),
  • Understand the basics of using functions (arguments, vectorizion and re-cycling).

Videos:

Supporting Material:

Homework:

  • Questions,
  • DataCamp: Sign-up to our BIMM143_S20 group/organization via the link in your UCSD email and complete Introduction to R! (4hrs).

Week 5: Data exploration and visualization in R

Topics: The exploratory data analysis mindset, Data visualization best practices, Simple base graphics (including scatterplots, histograms, bar graphs, dot chats, boxplots and heatmaps), Building more complex charts with ggplot.

Goal:

  • Appreciate the major elements of exploratory data analysis and why it is important to visualize data.
  • Be conversant with data visualization best practices and understand how good visualizations optimize for the human visual system.
  • Be able to generate informative graphical displays including scatterplots, histograms, bar graphs, boxplots, dendrograms and heatmaps and thereby gain exposure to the extensive graphical capabilities of R.
  • Appreciate that you can build even more complex charts with ggplot and additional R packages.
  • Be able to write and (re)use basic R scripts to aid with reproducibility.

Videos:

Supporting Material:

Homework:

Week 6: R functions and R packages from CRAN and BioConductor

Topics: The why, when and how of writing your own R functions with worked examples. Further extending functionality and utility with R packages, Obtaining R packages from CRAN and Bioconductor, Working with Bio3D for molecular data, Managing genome-scale data with bioconductor.

Goals:

  • Understand the structure and syntax of R functions and how to view the code of any R function,
  • Be able to follow a step by step process of going from a working code snippet to a more robust function that reduces duplication and facilitate code re-use,
  • Be able to find and install R packages from CRAN and bioconductor,
  • Understand how to find and use package vignettes, demos, documentation, tutorials and source code repository where available.

Videos:

Supporting material:

Homework:

  • See Q6 of the hands-on lab supplement above. This entails turning a supplied code snippet into a more robust and re-usable function that will take any of the three listed input proteins and plot the effect of drug binding. Note assessment rubric and submission instructions within document. (Submission deadline: 12:00pm next Tuesday, 05/12/20).
  • DataCamp: Intermediate R compltete chapters 1-3 only (~4hrs).

Other:

Week 7: Introduction to machine learning for Bioinformatics

Topics: Unsupervised learning, supervised learning and reinforcement learning; Focus on unsupervised learning, K-means clustering, Hierarchical clustering, Dimensionality reduction, visualization and analysis, Principal Component Analysis (PCA) Practical considerations and best practices for the analysis of high dimensional datasets.

Goal:

  • Understand the major differences between unsupervised and supervised learning.
  • Be able to create k-means and hierarchical cluster models in R
  • Be able to describe how the k-means and bottom-up hierarchical cluster algorithms work.
  • Know how to visualize and integrate clustering results and select good cluster models.
  • Be able to describe in general terms how PCA works and its major objectives.
  • Be able to apply PCA to high dimensional datasets and visualize and integrate PCA results (e.g identify outliers, find structure in features and aid in complex dataset visualization).

Videos:

Supporting material:

Homework:

Other Material:

Week 8: Genome informatics

Topics: Genome sequencing technologies past, present and future (Sanger, Shotgun, PacBio, Illumina, toward the $500 human genome), Biological applications of sequencing, Variation in the genome, RNA-Sequencing for gene expression analysis; Major genomic databases, tools and visualization resources from the EBI & UCSC, The Galaxy platform for quality control and analysis; Sample Galaxy RNA-Seq workflow with FastQC and Bowtie2

Goals:

  • Appreciate and describe in general terms the rapid advances in sequencing technologies and the new areas of investigation that these advances have made accessible.
  • Understand the process by which genomes are currently sequenced and the bioinformatics processing and analysis required for their interpretation.
  • For a genomic region of interest (e.g. the neighborhood of a particular SNP), use a genome browser to view nearby genes, transcription factor binding regions, epigenetic information, etc.
  • Be able to use the Galaxy platform for basic RNA-Seq analysis from raw reads to expression value determination.
  • Understand the FASTQ file format and the information it holds.
  • Understand the SAM/BAM file format and the information it holds.

Videos:

Supporting material:

IPs

  • nt1 IP: http://3.212.78.120/galaxy
  • nt2 IP: http://3.231.195.172/galaxy

Week 9: Transcriptomics and the analysis of RNA-Seq data

Topics: Analysis of RNA-Seq data with R, Differential expression tests, RNA-Seq statistics, Counts and FPKMs, Normalizing for sequencing depth, DESeq2 analysis. Gene finding and functional annotation from high throughput sequencing data, Functional databases KEGG, InterPro, GO ontologies and functional enrichment.

Goals:

  • Given an RNA-Seq dataset, find the set of significantly differentially expressed genes and their annotations.
  • Gain competency with data import, processing and analysis with DESeq2 and other bioconductor packages.
  • Understand the structure of count data and metadata required for running analysis.
  • Be able to extract, explore, visualize and export results.
  • Perform a GO analysis to identify the pathways relevant to a set of genes (e.g. identified by transcriptomic study or a proteomic experiment). Use both Bioconductor packages and online tools to interpret gene lists and annotate potential gene functions.

Videos:

Supporting material:

Readings:

Optional extension exercises:

R Knowledge Check: Quiz Assessment.

Week 10: Course wrap up, project completion

Topics: Summary of learning goals, Student course evaluation time; Find a gene assignment due. Open study. Student selected topic:

Bioinformatics and genomics in industry and beyond

  • Video live stream interview with leading bioinformatics and genomics scientists from industry including Dr Ali Crawford (Associate Director, Scientific Research, Illumina Inc.), Dr. Bjoern Peters (Full Professor and Principal Investigator, La Jolla Institute) and Dr. Ana Grant (Director of Research Informatics, Synthetic Genomics Inc.).

  • Submit your questions here.

Side-note: Student topic of choice was selected from those below:

  • Biological network analysis
  • Cancer genomics
  • Unix for Bioinformatics
  • Structural Bioinformatics and computational drug design
  • Introduction to the tidyverse
  • Bioinformatics and genomics in industry and beyond