/appliedgenomics2022

Materials for EN.601.449/EN.601.649 Computational Genomics: Applied Comparative Genomics

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JHU EN.601.449/EN.601.649: Computational Genomics: Applied Comparative Genomics

Prof: Michael Schatz (mschatz @ cs.jhu.edu)
TA: Bohan Ni (bni1 @ jhu.edu)
Class Hours: Monday + Wednesday @ 1:30p - 2:45p Gilman 17
Schatz Office Hours: By appointment
Ni Office Hours: TBD and by appointment

The primary goal of the course is for students to be grounded in theory and leave the course empowered to conduct independent genomic analyses. We will study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. There are no formal course prerequisites, although the course will require familiarity with UNIX scripting and/or programming to complete the assignments and course project.

Prerequisites

Course Resources:

Related Courses & Readings

Related Textbooks

Schedule

Class Date Day Topic Readings Assignments
1 29-Aug Mon Introduction * Biological data sciences in genome research (Schatz, 2015, Genome Research)
* Big Data: Astronomical or Genomical? (Stephens et al, 2015, PLOS Biology)		 Sign Up for Piazza
2 31-Aug Wed Genomic Technologies * Molecular Structure of Nucleic Acid (Watson and Crick, 1953, Nature)
* Coming of age: ten years of next-generation sequencing technologies (Goodwin et al, 2016, Nature Reviews Genetics)
* Piercing the dark matter: bioinformatics of long-range sequencing and mapping (Sedlazeck et al, 2018, Nature Reviews Genetics)		 Assignment 1: Genomic Fundamentals
* 5-Sep Mon Labor Day
3 7-Sep Wed Whole Genome Assembly * Velvet: Algorithms for de novo short read assembly using de Bruijn graphs (Zerbino and Birney, 2008, Genome Research)
* Quake: quality-aware detection and correction of sequencing errors (Kelley et al, 2010, Genome Biology)
* Allpaths-LG: High-quality draft assemblies of mammalian genomes from massively parallel sequence data (Gnerre et al, 2011, PNAS)
* FALCON-unzip: Phased diploid genome assembly with single-molecule real-time sequencing (Chin et al, 2016, Nature Methods)
4 12-Sep Mon Whole Genome Assembly and Alignment * Toward simplifying and accurately formulating fragment assembly. (Myers, 1995, J. Comp. Bio.)
* MHAP: Assembling large genomes with single-molecule sequencing and locality-sensitive hashing (Berlin et al, 2015, Nature Biotech)
* Genome assembly forensics: finding the elusive mis-assembly (Phillippy et al, 2008, Genome Biology)
* MUMmer: Alignment of Whole Genomes (Delcher et al, 1999, NAR)		 Assignment 2: Assembly
5 14-Sep Wed The human genome and intro to long reads * Piercing the dark matter: bioinformatics of long- range sequencing and mapping (Sedlazeck et al, 2018, Nature Reviews Genetics)
* Nanopore sequencing and assembly of a human genome with ultra-long reads (Jain et al, 2018, Nature Biotech)
* The complete sequence of a human genome (Nurk et al, 2022, Science)
6 19-Sep Mon Genomics in the Cloud * Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) (Schatz et al, 2022, Cell Genomics)
7 21-Sep Wed Read Mapping * How to map billions of short reads onto genomes (Trapnell and Salzberg, 2009, Nature Biotech)
* Bowtie: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome (Langmead et al, 2009, Genome Biology)
* BWA-MEM: Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM (Li, 2013, arXiv)
* Sapling: Accelerating Suffix Array Queries with Learned Data Models (Kirsche et al, 2020, bioRxiv
8 26-Sep Mon Variant Analysis * Haplotype-based variant detection from short-read sequencing (Garrison and Marth, arXiv, 2012)
* The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data (McKenna et al, 2010, Genome Research)
* A universal SNP and small-indel variant caller using deep neural networks (Poplin et al, 2018, Nature Biotechnology
* SAM/BAM/Samtools: The Sequence Alignment/Map format and SAMtools (Li et al, 2009, Bioinformatics)
* IGV: Integrative genomics viewer (Robinson et al, 2011, Nature Biotech)		 Assignment 3: Mappability and Mapping
9 28-Sep Wed Structural Variant Analysis and Pangenomics * Accurate detection of complex structural variations using single-molecule sequencing (Sedlazeck et al, 2018, Nature Methods)
* Characterizing the Major Structural Variant Alleles of the Human Genome (Audano et al, 2019, Cell)
* Resolving the complexity of the human genome using single-molecule sequencing (Chaisson et al, 2015, Nature)
10 3-Oct Mon Genome Arithmetic and Plane Sweep * BEDTools: a flexible suite of utilities for comparing genomic features (Quinlan & Hall, 2010, Bioinformatics)
* A Parallel Algorithm for N-Way Interval Set Intersection (Layer & Quinlan, 2016, IEEE Proceedings)
11 5-Oct Wed Machine Learning Primer * What are decision trees? (Kingsford and Salzberg, 2008, Nature Biotechnology)
* What is a hidden Markov model? (Eddy, 2004, Nature Biotechnology)
* Deep learning in biomedicine (Wainberg et al, 2018, Nature Biotechnology)
* Visualizing Data Using t-SNE
12 10-Oct Mon Functional Analysis 1: Annotation * BLAST: Basic Local Alignment Search Tool
* Glimmer: Microbial gene identification using interpolated Markov models
* MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects
Assignment 4: Variants and Bedtools
13 12-Oct Wed Functional Analysis 2: RNA-seq * RNA-Seq: a revolutionary tool for transcriptomics (Wang et al, 2009. Nature Reviews Genetics)
* Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (Trapnell et al, 2012, Nature Protocols)
* Salmon provides fast and bias-aware quantification of transcript expression (Patro et al, 2017, Nature Methods)
* Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications (Krueger and Andrews, 2011, Bioinformatics)
14 17-Oct Mon Functional Analysis 3: Methyl-seq, Chip-seq, and Hi-C * ChIP-seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions (Furey, 2012, Nature Reviews Genetics)
* PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls (Rozowsky et al. 2009. Nature Biotech)
* Comprehensive Mapping of Long-Range Interactions Reveals Folding Principles of the Human Genome (Lieberman-Aiden et al, 2009, Science)		 Project Proposal
15 19-Oct Wed Functional Analysis 4: Regulatory States, ENCODE, GTEx, RoadMap * An integrated encyclopedia of DNA elements in the human genome (The ENCODE Project Consortium, Nature, 2012)
* Genetic effects on gene expression across human tissues (GTEx Consortium, Nature, 2017)
* Integrative analysis of 111 reference human epigenomes (Roadmap Epigenome Consortium, Nature, 2015)
* ChromHMM: automating chromatin-state discovery and characterization (Ernst & Kellis, 2012, Nature Methods)
* Segway: Unsupervised pattern discovery in human chromatin structure through genomic segmentation (Hoffman et al, 2012, Nature Methods)
16 24-Oct Mon Functional Analysis 5: Single Cell Genomics * Ginkgo: Interactive analysis and assessment of single-cell copy-number variations (Garvin et al, 2015, Nature Methods)
* The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells (Trapnell et al, Nature Biotech, 2014)
* Eleven grand challenges in single-cell data science (Lahnemann et al, Genome Biology, 2020)		 Assignment 5: Functional Genomics
17 26-Oct Wed Human Evolution * An integrated map of genetic variation from 1,092 human genomes (1000 Genomes Consortium, 2012, Nature)
* Analysis of protein-coding genetic variation in 60,706 humans (Let et al, 2016, Nature)
* A Draft Sequence of the Neandertal Genome (Green et al. 2010, Science)
* Excavating Neandertal and Denisovan DNA from the genomes of Melanesian individuals (Vernot et al. 2016. Science)
18 31-Oct Mon Midterm review
19 2-Nov Wed Midterm Take home exam
20 7-Nov Mon Human Genetic Diseases * Genome-Wide Association Studies (Bush & Moore, 2012, PLOS Comp Bio)
* The contribution of de novo coding mutations to autism spectrum disorder (Iossifov et al, 2014, Nature)		 Preliminary Project Report
21 9-Nov Wed Cancer Genomics * The Hallmarks of Cancer (Hanahan & Weinberg, 2000, Cell)
* Evolution of Cancer Genomes (Yates & Campbell, 2012, Nature Reviews Genetics)
* Comprehensive molecular portraits of human breast tumours (TCGA, 2012, Nature)
22 14-Nov Mon Microbiome and Metagenomics * Kraken: ultrafast metagenomic sequence classification using exact alignments (Wood and Salzberg, 2014, Genome Biology)
* Chapter 12: Human Microbiome Analysis (Morgan and Huttenhower)
23 16-Nov Wed Genomics Futures * Snyderome: Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes (Chen et al, 2012, Cell)
* Identifying Personal Genomes by Surname Inference (Gymrek et al, 2013, Science)
* 21-Nov Mon Thanksgiving Break
* 23-Nov Wed Thanksgiving Break
24 28-Nov Mon In class project presentation Project presentation
25 30-Nov Wed In class project presentation Project presentation
26 5-Dec Mon In class project presentation Project presentation
27 7-Dec Wed In class project presentation Project presentation
* 21-Dec Wed Final Report Due Final Report