Summary
MicroRNAs (miRNAs) are a class of highly conserved small non-coding RNAs that regulate the expression of most protein coding genes. MiRNAs have been extensively studied, not only to understand their molecular function but also due to their potential as therapeutic and diagnostic tools. Most current studies rely on small RNA sequencing (sRNA-seq), recently extended to single cells, because it allows to measure all miRNAs present in a sample even at low input amounts. However, state-of-the-art statistical analysis of sRNA-seq data is still performed using standard mRNA-seq software. This is inappropriate because miRNA counts violate several assumptions of these methods. In extreme cases, upregulated miRNAs will falsely seem down-/upregulated because of technical effects. Technical bias is even more severe in low input applications, e.g., single-cell, so decreasing the RNA input lowers quantification accuracy. By developing statistical methods specifically tailored to model sRNA-seq complexities, miRAQEL aims to dramatically improve the accuracy of miRNA quantification through sRNA-seq, contributing to solve challenges in the field. Appropriate statistical methodology is urgently needed to fully elucidate the role miRNAs play in many diseases and improve their biomarker potential.
The fellow will receive specialized training in standard single-cell RNA-seq and statistical analysis of miRNA data from a leading research team in the field. In turn, he will bring his pipeline development and miRNA genomics expertise to implement and evaluate sRNA-seq modelling software using ground truth data and simulations. A placement will ensure transfer of this knowledge to an European start-up. The emerging unique skill set will provide him with a privileged interdisciplinary view that he can leverage to kick-start an independent academic career in the analysis of small RNA molecules at the single cell level, getting ahead in a field that is expected to develop in the coming years.
The fellow will receive specialized training in standard single-cell RNA-seq and statistical analysis of miRNA data from a leading research team in the field. In turn, he will bring his pipeline development and miRNA genomics expertise to implement and evaluate sRNA-seq modelling software using ground truth data and simulations. A placement will ensure transfer of this knowledge to an European start-up. The emerging unique skill set will provide him with a privileged interdisciplinary view that he can leverage to kick-start an independent academic career in the analysis of small RNA molecules at the single cell level, getting ahead in a field that is expected to develop in the coming years.
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| Web resources: | https://cordis.europa.eu/project/id/101153849 |
| Start date: | 01-01-2025 |
| End date: | 30-04-2028 |
| Total budget - Public funding: | - 315 332,00 Euro |
Cordis data
Original description
MicroRNAs (miRNAs) are a class of highly conserved small non-coding RNAs that regulate the expression of most protein coding genes. MiRNAs have been extensively studied, not only to understand their molecular function but also due to their potential as therapeutic and diagnostic tools. Most current studies rely on small RNA sequencing (sRNA-seq), recently extended to single cells, because it allows to measure all miRNAs present in a sample even at low input amounts. However, state-of-the-art statistical analysis of sRNA-seq data is still performed using standard mRNA-seq software. This is inappropriate because miRNA counts violate several assumptions of these methods. In extreme cases, upregulated miRNAs will falsely seem down-/upregulated because of technical effects. Technical bias is even more severe in low input applications, e.g., single-cell, so decreasing the RNA input lowers quantification accuracy. By developing statistical methods specifically tailored to model sRNA-seq complexities, miRAQEL aims to dramatically improve the accuracy of miRNA quantification through sRNA-seq, contributing to solve challenges in the field. Appropriate statistical methodology is urgently needed to fully elucidate the role miRNAs play in many diseases and improve their biomarker potential.The fellow will receive specialized training in standard single-cell RNA-seq and statistical analysis of miRNA data from a leading research team in the field. In turn, he will bring his pipeline development and miRNA genomics expertise to implement and evaluate sRNA-seq modelling software using ground truth data and simulations. A placement will ensure transfer of this knowledge to an European start-up. The emerging unique skill set will provide him with a privileged interdisciplinary view that he can leverage to kick-start an independent academic career in the analysis of small RNA molecules at the single cell level, getting ahead in a field that is expected to develop in the coming years.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
15-11-2024
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