Takustr. 9, Room 005

Wednesday, 12 am-14 pm



Wednesday 14th of October (Introduction slides to RNA Bioinformatics topics - paper assignment)



There is no special prerequisite required. Statistical knowledge from the Statistics Master course and Algorithms for Sequence Analysis would be helpful.

Notes: The number of participants will be limited to 14. In case the demand exceeds the seminar capacity, students from the Master's programme in Bioinformatics (FU) will have priority.

Noncoding RNAs (ncRNAs) are transcripts that are not translated into proteins but act as functional RNAs. In the past few years. a few key discoveries have been recognized that ncRNAs have a much wider spectrum of functions than anticipated. For example, the discovery of microRNAs has changed our view of how genes are regulated at post-transcriptional level. The vast amount of transcripts produced by high-throughput technologies suggests that many important ncRNA functions are yet to be discovered. Given the involvement of ncRNAs in almost every biological process, in the last few years increasing attention has been given to RNA bioinformatics, the discipline devoted to the development of computational tools and methodologies for the analysis of RNA sequence, structure and function.This seminar is meant for master students and the goal is to look deeper at some selected papers describing algorithms and statistical methods for ncRNA structure and function prediction, but also new technolgies (e.g. CLIP methods) which, coupled with proper computational pipelines, enable a better characterization of RNA properties genome-wide. The aim will be to use the articles as a starting point to critically assess the methodologies, and understand how they can be improved or extended to other contexts. The proposed journal articles are listed below and this list can be extended or changed depending on the number of participants.

In the following some additional guidelines for the seminar are given.
Completing the seminar

The language of the seminar is English and to pass the seminar you need to do the following:
- attend almost all the classes (if you miss more than one class you will be asked to write a report about a topic of your choice presented in the seminar class)
- give an oral presentation about a paper of your choice selected from the proposed list below. Sudents can propose a topic or paper which is not in the list, but they should discuss their choice with me first, in order to ansure the relevance and scientific value of the selected paper. More details about the presentation format and duration can be found here.
- Each students is expected to participate actively in the discussion following the presentation by asking at least two questions regarding the presented topic and reviewing the other students' work (e.g. feedback on the talk and quality of the presentation)


link to the Doodle

Important! Please register in the Campus Management so we can plan the talks! If you are not able to give the talk on the planned day please find someone to switch with or communicate it at least one week in advance. This will ensure continuity to the seminar class and will give us time to re-arrange the schedule accordingly.

First session - Introduction to RNA Bioinformatics here



Evolution of long-non coding RNAs and implication for their functional classification (largely unknown so far!)

1) Evolution of lon non-coding RNAs via splicing site comparison


2) Method for tracking the evolution of long non-coding RNAs



Models and databases to represent RNA structure and sequence consensus

3) Covariance models to identify RNA homologs


4) What is new in the Rfam database?


 Sequence-structure alignment and folding of non-coding RNA: a step forward to understand their function and funcitonal similarities

5) BlockClust: Clustering of non-coding RNAs from RNA-seq profiles


6) SPARSE: alignment and folding of RNAs in quadratic time


7) GraphClust: alignment-free clustering of RNA secondary structure


Investigation of RNA function through binding with RNA Binding Proteins - CLIP-seq  and hiCLIP-seq data analysis

8) Motif discovery algorithm for RNA-binding protein sites


9) PIPEClip: a pipeline for CLIP-seq data analysis


10) hiCLIP technology to asses RNA secondary structure in-vivo


11) BackCLIP: identifying the background in RNA-binding protein data



 Modeling and prediction of RNA-protein Binding Sites

12) GraphProt: Modeling of RNA binding sites with graph-kernel Support Vector Machines



Accurate annotation of microRNA genes from high-throughput data

13) MicroRNA promoter identification with Support vector Machines



RNA post-transcriptional modifications imnportant for RNA functional studies (new technologies to study such properties genome-wide from high-throughput sequencing data)

14) Identification of RNA editing sites from RNA-seq data

15) G4HMM: Hidden Markov Models to identify G-quadruplexes motifs



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