In this chapter, we introduce the concept of RNA-Seq analyses. First, we start to provide an overview of a typical RNA-Seq experiment that includes extraction of sample RNA, enrichment, and cDNA library preparation. Next, we review tools for quality control and data pre-processing followed by a standard workflow to perform RNA-Seq analyses. For this purpose, we discuss two common RNA-Seq strategies, that is a reference-based alignment and a de novo assembly approach. We learn how to do basic downstream analyses of RNA-Seq data, including quantification of expressed genes, differential gene expression (DE) between different groups as well as functional gene analysis. Eventually, we provide a best-practice example for a reference-based RNA-Seq analysis from beginning to end, including all necessary tools and steps on GitHub: https://github.com/grimmlab/BookChapter-RNA-Seq-Analyses.
We are seeking highly motivated candidates for a scientific research position as Postdoctoral Researcher, to further advance the recently launched Synthetic Biology Foundry at TUM Campus Straubing (SynBiofoundry@TUM).
Maura will work on novel bioinformatics and machine learning techniques to gain a better understanding of genotype-phenotype relationships.
We are happy to share an interview Richa gave for the portal “Research in Germany”. In this interview Richa gave insights about her research in Germany, and what she likes about living here.
New publication about “Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops” just got published in Plant Methods.
Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
New publication about “Network-guided search for genetic heterogeneity between gene pairs” just got published in Bioinformatics: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa581/5861532
We propose a novel method for finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple A. thaliana phenotypes, and for a study of rare variants in migraine patients.