Hands-on training in Bioinformatics and Biomedical Data Science

October 12, 2018 – December 30, 2018

3 month program that will provide an in-depth overview of standard tools and data types when studying Next generation Sequencing (NGS) datasets in oncology, neuroscience and agriculture. Each week, participants will meet online to discuss various aspects of NGS analysis and interpretation and work on independent projects throughout this course.

• Introduction: Next Generation Sequencing, it’s application and techniques of data preparation. Review of NCBI and other databases to identify datasets of interest for independent study and preparation of data for analysis. Friday, October 12, 2018

1. 1. 1. From DNA to Proteins: Genes, Isoforms and Proteins
      2. Alternative sequencing methods to NGS
      3. Use of NGS in basic and translational research

• Finding a dataset and planning your analysis: Wednesday, October 17, 2018.

1. 1. 1. NCBI and other publicly-available databases with datasets for analysis
      2. Assignment: select a dataset and submit for review (submit by Friday, September 21, 2018)

• Processing NGS data: Part 1. Wednesday, October 24, 2018

1. 1. 1. What are pipelines? Combining tools into sequential processes
      2. Role of pre-processing in standard processing pipelines (Trimmomatic and PCR-clean)
      3. Mapping techniques: mapping on transcriptome, mapping on genome and combined strategies (Bowtie, BWA and TopHat/HiSat)

• Processing NGS data: Part 2. Wednesday, October 31, 2018

1. 1. 1. Generating a table of expression: RSEM, HTSeq and Sailfish
      2. Differential Gene Expression: T-Test and P-values, Differential Gene Expression using DESEQ2 and EdgeR

• Exploratory Data Analysis: Part 1. Wednesday, November 7, 2018

1. 1. 1. Mapping Statistics, Artifacts and Metadata
      2. Filtering, removing noise and Normalization Techniques

• Exploratory Data Analysis: Part 2. Wednesday, November 14, 2018

1. 1. 1. Exploring multi-dimensional data using PCA visualization
      2. Principal Components and variance – outliers, filtering, normalization

• Analysis of Gene Expression: Part 1. Wednesday, November 21, 2018

1. 1. 1. Mining big data – understanding data patterns and structures using unsupervised data mining methods
      2. Correlation – detecting correlation of features and factors

• Analysis of Gene Expression: Part 2. Wednesday, November 28, 2018

1. 1. 1. Clustering of samples using gene expression profiles
      2. Clustering of genes by expression profiles across samples

• Introduction to Supervised Data Mining. Wednesday, December 5, 2018

1. 1. 1. What is machine learning, categories of methods and associated challenges
      2. Regression, factors and features – Factor Regression Analysis

• Using Machine Learning for Expression Data: Wednesday, December 12, 2018

1. 1. 1. Decision Trees, Discriminant Analysis and Support Vector Machines
      2. Feature Selection and expanding the list of features

• Interpretation: Monday, December 17, 2018

1. 1. 1. Annotation using Gene Ontology
      2. Human GAGE: Gene Set Enrichment Analysis
      3. Statistical Significance and Reproducibility
      4. Data Visualization

• December 17 – December 30th: independent project work and submission.