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Lecture 1
Lecture 1
- Introduction to Computational Genomics
Biological data sciences in genome research (Schatz, 2015, Genome Research)
Big Data: Astronomical or Genomical? (Stephens et al, 2015, PLOS Biology)
Genomics and data science: an application within an umbrella (Navarro et al, 2019, Genome Biology)
- Primer Lecture: Basic Cell Biology
- Primer Lecture: Introduction to Molecular Biology
Chapter 1 of ZB Book
A Biology Primer for Computer Scientists (Franco P. Preparata)
Scitable definition of DNA Transcription
Lecture 2
Lecture 2
- DNA Sequencing
DNA sequencing at 40: past, present and future (Shendure et al. 2017 Nature)
Must Watch Video (How DNA is copied)
Must Watch Video (Sequencing by Synthesis)
Lecture 4
Lecture 4
Lecture 6
Lecture 6
- RNA-seq: Introduction
RNA sequencing data: hitchhiker's guide to expression analysis (Van Den Berge, Koen, et al. 2019)
RNA-Seq: a revolutionary tool for transcriptomics (Wang et al. 2009, Nature Reviews Genetics)
RNA sequencing: the teenage years (Stark et al. 2019, Nature Reviews Genetics)
- RNA-seq Alignment
TopHat: discovering splice junctions with RNA-Seq (Trapnell et al. 2009, Bioinformatics)
STAR: ultrafast universal RNA-seq aligner (Dobin et al. 2013, Bioinformatics)
Lecture 7
Lecture 7
- RNA-seq: Transcript Quantification
RNA sequencing data: hitchhiker's guide to expression analysis (Van Den Berge, Koen, et al. 2019)
RNA-Seq gene expression estimation with read mapping uncertainty (Li et al. 2010, Bioinformatics)
Near-optimal probabilistic RNA-seq quantification (Bray et al. 2016, Nature Biotech)
Lecture 9
Lecture 9
- RNA-seq: Differential Expression Analysis
Differential expression analysis for sequence count data (Anders & Huber 2010, Genome Biology)
Lecture 10
Lecture 10
- Single-cell Sequencing: Introduction
Advances and applications of single-cell sequencing technologies (Wang & Navin 2015, Molecular Cell)
Power analysis of single-cell RNA-sequencing experiments (Svensson et al. 2017, Nature Methods)
Droplet-based single cell RNAseq tools: a practical guide (Saloman et al. 2019, Lab on a chip)
Lecture 11
Lecture 11
- Single-cell Sequencing: Pre-processing
Current best practices in single-cell RNA-seq analysis: a tutorial (Luecken and Theis 2019)
Computational Methods for Single-Cell RNA Sequencing (Brian et al. 2020)
- Single-cell Sequencing: Cell type Clustering
A periodic table of cell types (Xia and Yanai 2019, Development)
Defining cell types and states with single-cell genomics (Trapnell 2015, Genome Research)
Challenges in unsupervised clustering of single-cell RNA-seq data (Kiselev et al. 2019)
Lecture 16
Lecture 16
Comprehensive Integration of Single-Cell Data (Stuart et al. 2019)
A test metric for assessing single-cell RNA-seq batch correction (Buttner et al. 2019)
Fast, sensitive and accurate integration of single-cell data with Harmony (Korsunsky et al. 2019)
Benchmarking atlas-level data integration in single-cell genomics (Luecken et al. 2022)
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