RNA Analysis

Bologna University, September 2020

Cedric Notredame





OBJECTIVES

In this course, I will start by introducing mainstream next generation sequencing methods. I will then discuss how these methods can be used todo RNA-Seq for the systematic sequencing of cell transcriptomes, along with the many challenges it entails, such as gene modeling and isoform quantification. This introduction will include recent developments in single cell sequencing. The second part of the course will focus on more algorithmic aspects of RNA analysis including BWT mapping and the prediction of RNA secondary structures using the Zuker algorithm. The last part of the course will be focused on the computation of RNA multiple sequence alignment methods. I will first introduce the standard multiple sequence alignment method and show how these methods can be applied to nucleotide sequences. I will finish this series of lectures by showing how multiple sequence alignment methods can be adapted to deal with RNA secondary structure analysis, and how they can possibly be used to combine structural and sequence information.



Send your Questions to: cedric.notredame@crg.eu



DateLocationSessionTitleLinks
1 BolognaLECTURENext Generation Sequencing Slides Voiceover
BolognaBIBLIOStructural variation in the human genome Biblio
2 BolognaLECTURERNASeq Slides Voiceover
3 BolognaLECTURESingle Cell Transcriptomics Slides Voiceover
BolognaBIBLIOSingle Cell Transcriptomics Review Biblio
4 BolognaLECTUREShort Reads Mapping Slides Voiceover
BolognaBIBLIOShort Reads Mapping Biblio Biblio
5 BolognaLECTUREPairwise Dynamic Programming Slides Voiceover
6 BolognaLECTURERNA Secondary Structure Prediction Slides Voiceover
BolognaBIBLIORNA Secondary Structure Prediction Biblio Biblio
7BolognaLECTUREBlast Algorithm Slides
8BolognaLECTURESearching Non Coding RNAs Slides
9BolognaLECTUREMultiple Sequence Alignment Algorithms Slides
10BolognaLECTUREThe T-Coffee Algorithm Slides
11BolognaLECTUREMultiply Aligning Non Coding RNAs. Slides


This Entire Course Was Automatically Generated Using BED, the Bioinformatics Exercise Database. BED is a freeware available on request Cedric Notredame