Molecular Biomedicine

Welcome to the (unusually long named)

Fundamental principals in Bioinformatics, Computational Biology and Biostatistics

A class that is part of the Molecular Biomedicine MSc Program.

Course Structure

• 9 Lectures on topics related to computational and statistical approaches of molecular/biomedical problems
• Readings to be performed BEFORE each class (You have been warned)
• Topics and questions to be discussed in class
• Final evaluation will consist of written essay(s) and an (old-fashioned) in-class oral test.

Course Objectives

• Broad Picture: To remind you stuff you have chosen to forget after convincing you they were relevant all along. [Analytical thinking, quantitative approaches, statistical inference]
• Concepts: To give you an overview of the problems solved by computational and algorithmic approaches in modern biomedicine.
• Skillset: To show you how to perform (but more importantly how to interpret) standard bioinformatics analyses on genomic data.[Gene Expression, Functional Analysis, Modeling of biological data]

1. Introduction (Oct 25th 2019)

This weeks class is an introduction to the concepts we will be discussing throughout the semester. Namely:

• The "informatics" in Bioinformatics
  • What do we need it for
  • What it may refer to
  • What it actually is
• Remembering stuff (from high-school)
  • How to measure things
  • How to assess/compare/interpret measurements

Students are all advised to access the Reading material (to be read BEFORE class) that you may find below:

• "The median isn't the message" by Stephen Jay Gould
• Intuitive Biostatistics. Chapter 1. by Harvey Motulsky
• Υπολογιστική Βιολογία. Κεφάλαιο 1. Χριστόφορος Νικολάου
• Lecture 1. Introduction

2. "-omics" Approaches to Biology (Nov 1st 2019)

• A. Omes and Omics.
  • NGS applications in Biology
  • The problems
  • The solutions
• B. After NGS
• Types of data
• Data retrieval
• Preparation for analysis

Reading Material:

• Goodwin et al. (2016) Coming of Age. Ten years of NGS technologies. Nat Rev Genetics
• Marx. (2013) The Big Challenges of Big Data. Nature
• Downloading Genes from Databases (https://www.youtube.com/watch?v=EdY0Vt4xXjk) and (https://www.youtube.com/watch?v=KsgFMoTXu-g)
• Lecture 2. "Omics" approaches to Biology

3. First steps in NGS data analysis (Nov 8th 2019)

• A. Quality Control
  • Before QC
  • Analysis of sequencing quality
  • Resolving QC problems
• B. Mapping
• The problem of sequence alignment
• Sequence Alignment algorithms
• Alignment of big data

Reading Material:

• Points of Significance. Visualizing samples with boxplots. Nat Methods 11, 119-120 (2014)
• More on boxplots Nat. Methods, 11, 121-122 (2014)
• Στοιχίσεις και Ταχείες Αναζητήσεις. (Υπολογιστική Βιολογία, Κεφ 4), Χ. Νικολάου
• Integrative Genomics Viewer (IGV):high-performance genomics data visualization and exploration. Brief Bioinf (2012)
• Build your own Suffix Tree
• Lecture 3. NGS QC and mapping

4. Analysis of Gene Expression with RNASeq (Nov 15th 2019)

• A. The theory
  • Transcriptome complexity
  • Reconstruction of transcripts
  • Assessing Expression
  • Understanding trends in expression patterns
• B. The practice
• Transcript reconstruction
• RPKM quantification of expression
• Differential Expression statistics
• Multiple Comparisons
• Clustering of gene expression profiles

Reading Material:

• A survey of best practices for RNASeq analysis. Conesa et al, Genome Biology (2016)
• Points of Significance. Significance, p-values and t-tests. Nat Methods 10, 1041-1042 (2013)
• Ανάλυση της Γονιδιακής Έκφρασης. (Υπολογιστική Βιολογία, Κεφ 7), Χ. Νικολάου (2015)
• Points of Significance. Comparing Samples pt II. Nat Methods 11, 355-356 (2014)
• Cluster analysis and display of genome-wide expression patterns, Eisen et al. PNAS (1998)
• Lecture 4. Gene Expression with RNASeq

5. Gene Regulation through ChIPSeq approaches (Nov 22nd 2019)

• A. Analysis of ChIPSeq data
  • Normalization
  • Creation of abundance profiles
  • Localization of peaks of enrichment
• B. Analysis of Peaks of enrichment
  • Clustering of peaks
  • Discovery/Inference of DNA-binding sites
  • Assigning peaks to genomic regions of interest

Reading Material:

• Design and analysis of ChIP-seq experiments for DNA-binding proteins. Kharchenko et al., Nat. Biotech (2008)
• Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins. Teytelman et al, PNAS (2013)
• ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Landt et al. Genome Res (2012)
• Computation for ChIP-seq and RNA-seq studies. Pepke et al. Nat Methods (2009)
• Lecture 5. Analysis of ChIPSeq experiments

6. NGS Approaches to Genomic Variability (Nov 29th 2019)

• Genomic Variability Analysis
  • Whole-Genome or Whole-Exome Sequencing
  • Calling of variants
  • Annotation of Variants

Reading Material:

• Ανάλυση της Γενετικής Ποικιλομορφίας (Υπολογιστική Βιολογία, Κεφάλαιο 10). Χ. Νικολάου
• Bamshad et al. (2011). Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genetics 12, 745-755
• Khurana et al. (2013). Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics. Science 342
• Understanding Odds-Ratios
• Lecture 6. Analysis of Genomic Variation

7. NGS Approaches to Chromatin Structure (Dec 6th 2019)

• B. Chromatin Structure Analysis
  • Epigenetics and Histone-modifications
  • Open Chromatin Approaches
  • Chromosomal Conformation

Reading Material:

• Δομή της Χρωματίνης (Υπολογιστική Βιολογία, Κεφάλαιο 5). Χ. Νικολάου
• Valouev et al. (2011). Determinants of Nucleosome Positioning in primary human cells. Nature 474, 516-520
• Marti-Renom and Mirny. (2011). Bridging the resolution gap in structural modeling of 3D genome organization. PLoS Computational Biology, 7, 7, e1002125
• Lecture 7. Analysis of Chromatin Structure

8. Hands-on practical (Dec 13th 2019)

The class will focus on the application of web-based bioinformatics tools for simple, straightforward analyses of datasets

• Analyzing the quality of a fastq dataset with FASTQC through Galaxy (use.galaxy.org). Get the fastq file here.
• Map a fastq file against a reference genome using Galaxy (use.galaxy.org). The file comes from a S. cerevisiae dataset and should be mapped against the SacCer2 genome index. (Note: You will need to use FASTQ-Groomer to convert to Sanger/Illumina before mapping with BWA).
• Use MACS through Galaxy to call peaks of ChIP enrichment for a yeast dataset. Find the ChIP SacCer2 bamfiles here (control) and here (condition). Use a user-define Genome Size of 13000000 (approximate for yeast).
• Create average plots of a ChIPSeq dataset around the TSS of yeast genes using Galaxy. Find the TSS coordinate files here and the ChIP SacCer2 bamfiles here (control) and here (condition).
• Use MEME to discover new motifs from a sequence file originating from a peak caller. Find the sequence file here.
• Use a fasta file to extract a logo using Weblogo. Get the fasta file here.
• Use the GREAT server to analyze preferences of any type of genomic coordinate files (it can be used for a few organisms). Get a mouse genome coordinates file here.
• Use a differential gene expression profile to extract differentially expressed genes. Get two files here and here. You may use Excel or a similar program to call differential expression, in which case you may find this link useful.
• Use gprofiler to perform a functional analysis of differentially expressed genes.
• Use STRING to visualize networks of Protein-Protein interactions.
• Help: install Notepad++ on your computer for easier access of text data.

9. What the papers say. A critical reading of bioinformatics papers (Dec 20th 2019)

• Get the list of Figures to be discussed here.
• Check the original papers here.
• Prepare to discuss the Figures.