RNA-Seq "Boot Camp" Resources
Alex Lachmann during his presentation to the RNA-Seq "boot camp."
In June 2015, the Columbia University Department of Systems Biology held a five-part lecture series focusing on advanced applications of RNA-Seq in biological research. The talks covered topics such as the use of RNA-Seq for studying heterogeneity among single cells, RNA-Seq experimental design, statistical approaches for analyzing RNA-Seq data, and the utilization of RNA-Seq for the prediction of molecular interaction networks. The speakers and organizers have compiled a list of lecture notes and study materials for those wishing to learn more. Click on the links below for more information.
Some classic papers
Smith GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.
Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008 Jul;5(7):621-8.
Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63.
For more, see Jeff Leek's genomic reading list.
Useful RNA-Seq resources
An Introduction to Next-Generation Sequencing Technology, from Illumina.
Rodriguez-Ezpeleta N, Hackenberg M, Aransay AM. Bioinformatics for High Throughput Sequencing. New York: Springer, 2012. See particularly chapter 10, MicroRNA Expression Profling and Discovery.
Casella G. Statistical Design. New York: Springer, 2008.
Lecture 1: Introduction to RNA-Seq
Lecture 2: Under the Hood of Sequencing Technology and Single-Cell Analysis — Part 1
Lecture 3: Reverse Engineering of Transcriptional and Regulatory Networks Using Gene Expression and RNA-Seq
Alexander Lachmann (Califano Lab)
Reverse Engineering of Transcriptional and Regulatory Networks Using Gene Expression (slides)
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A. ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006 Mar 20;7 Suppl 1:S7.
Giorgi FM, Lopez G, Woo JH, Bisikirska B, Califano A, Bansal M. Inferring protein modulation from gene expression data using conditional mutual information. PLoS One. 2014 Oct 14;9(10):e109569.
Lecture 4: Under the Hood of Sequencing Technology and Single-Cell Analysis — Part 2
Lecture 5: Design of RNA-Seq Experiments
Albert Lee (Rabadan Lab)
Primer on Negative Binomial in the Context of RNA-Seq Analysis (slides)