Development of user-friendly tools for advanced statistical analyses of RNA sequencing cancer data
Development of user-friendly tools for advanced statistical analyses of RNA sequencing cancer data
RNA sequencing (RNA-seq) is a widely used technology that has transformed the field of genomics, allowing researchers to gain a deeper understanding of gene expression and regulation in various biological environment. RNA-Seq is used in different contexts such as quantification of the expression levels of genes, identification of up and down expressed genes (Differential Gene Expression), annotation of the gene functions (Gene Ontology) and functional implications of gene expression changes (Functional Pathway Analysis). In developmental biology, RNA-Seq is employed to study gene expression changes during development, helping researchers understand the molecular mechanisms underlying tissue differentiation.
The decreasing cost of RNA-Seq technologies during the last decade and its growing power and reliability have resulted in a marked increase in the number of RNA-Seq dataset worldwide.
To perform rigorous analyses of the RNA-Seq data, computational algorithms and bioinformatics programs have been developed during the last years including clustering algorithms for gene regulatory networks and expression, dimensionality reduction methods, principal component analysis (PCA) and artificial intelligence techniques such as supervised classification and machine learning techniques.
Support of open data by funding agencies and scientific journals increased the number of datasets availability. This has provided the scientific community with a vast repository of RNA-Seq datasets coming from patient samples of a variety of diseases and tissues, animal models and cell lines. These datasets can be explored and analyzed by researchers and requiring implication of bioinformaticians and biostatisticians.
User-friendly applications for RNA-Seq analyses are welcome for biomedical researchers with limited bioinformatics, biostatistics and programming experience.
The aim of the present project is to develop a tool (user-friendly), which allows physicians and biologists to perform differentially expressed gene, gene ontology, pathway signaling and advanced statistical analyses with a focus on cancer diseases.
The PhD candidate will must have good informatic programming, statistical and bioinformatics skills.
Procédure d'inscription
- Dossier de candidature : Curriculum vitae, lettre de motivation, projet de fin d’études, diplômes et relevés de notes
- Entretien
Dossier à envoyer à a.marfak@ueuromed.org avant le 09 Novembre 2023