Projects / Programmes
Modeling the transcriptome
| Code |
Science |
Field |
Subfield |
| 7.00.00 |
Interdisciplinary research |
|
|
| Code |
Science |
Field |
| B110 |
Biomedical sciences |
Bioinformatics, medical informatics, biomathematics biometrics |
| Code |
Science |
Field |
| 1.02 |
Natural Sciences |
Computer and information sciences |
artificial intelligence
bioinformatics
data mining
intelligent user interfaces
high-throughput RNA sequencing
transkriptome
Researchers (1)
| no. |
Code |
Name and surname |
Research area |
Role |
Period |
No. of publicationsNo. of publications |
| 1. |
23399 |
PhD Tomaž Curk |
Computer science and informatics |
Head |
2010 - 2012 |
253 |
Organisations (1)
Abstract
New experimental approaches based on high-throughput sequencing are revolutionizing transcriptome research. They allow a genome-wide insight into cells at a single nucleotide level and are altering our understanding of the structure and dynamics of the transcriptome. Even in a single experiment, high-throughput sequencing can gather a large quantity (few GB) of short sequence data. This wealth of information can only be explored by new computational analytics tools, of which design and availability plays a central role in modern biomedical scientific discovery. The general lack of such tools represents a major bottleneck in the scientific workflows. Their development may have a great impact on the genomics and biomedical research community.
The project will develop a set of computational and visualization methods for the identification and quantification of transcriptome elements based on RNA high-throughput sequencing and transcription factor binding preference data. Methods from data mining and artificial intelligence will be used for modeling relations among the transcriptome elements and. We will embed them an intelligent assistant for explorative analysis of transcriptome data.
The expected main results of the project are 1) a computational pipeline for mapping high-throughput sequencing data (RNA-Seq and iCLIP), including methods for the identification and quantification of transcriptome elements, 2) computational methods, a descriptive language and efficient heuristics for modeling relations among transcriptome elements and inference of new hypotheses on transcription regulation, 3) implementation of an intelligent, web-based interface for explorative analysis that will direct the researcher to the most interesting results, and 4) application of the developed tools to model the transcriptome of the social soil amoeba Dictyostelium discoideum during multi-cellular development, intracellular recognition and interaction with bacteria, and in human and mouse model neurodegenerative diseases.
The software tools developed in the project will be actively used by the six supporting biomedical research institutes, two of which from Slovenia (see letters of support in the attachment: Centre for Functional Genomics and Bio-Chips, University of Ljubljana and Jožef Stefan Institute), and will also be freely available to the wide research community.
Significance for science
In this project we have developed computational methods and software tools for the analysis of next-generation sequencing data, which will improve the efficiency of transcriptome research. Over 30 research groups from research institutes around the world are currently using the iCount computational pipeline and web server for iCLIP and other RNA-Seq data analysis. There is a strong interest in iCLIP method by many RNA laboratories worldwide. The current number of users indicates that iCount is filling an important gap in the general lack of computational tools for such analyses and thus significantly contributing to the progress of transcriptome research and the advancement of bioinformatics.
Significance for the country
With this project Slovenia is playing an active role in the development of analytical methods and bioinformatics tools for the analysis of high-throughput sequencing data. The current use of iCount by over 30 research groups from research institutes worldwide indicate that Slovenia is acquiring important expertise and significantly contributing to the leading research and development of RNA-Seq data analysis. This also leads to a wider recognition and promotion of Slovene science and its innovative applications on this young yet very active transcriptome research field.
Most important scientific results
Annual report
2010,
2011,
final report,
complete report on dLib.si
Most important socioeconomically and culturally relevant results
Annual report
2010,
2011,
final report,
complete report on dLib.si
