RNA- seq is used to study the mechanism of complex disease, identify potential biomarkers for clinical indications and infer gene pathways. It is a useful method for studying processes such as differentiation, proliferation, and tumorigenesis.
RNA-seq can be applied to a broad range of scientific questions such as gene expression profiling between samples. By using RNA-seq we can discover more about which genes are expressed or suppressed at different times in different types of cells.
Transcriptome-wide association studies aim to find genetic relationships between the expressions of genes and specific human traits of interest. This can be achieved by using large-scale genetic association results from existing studies of those traits.
These are some of the most common analyses we perform on RNA-seq data. If you are planning an RNA-seq experiment and you need help in getting the most out of your data, leave us a message and we will get back to you.
Exploratory gene expression analysis
Exploratory gene expression analysis is a powerful tool to investigate the molecular basis of phenotypic and biological differences across groups of biological samples, and for correlating a pharmacodynamic marker with the dose-dependent cellular responses to exposure of a drug. At KonAnBio we perform exploratory data analysis for quality assessment and to explore the relationship between samples, perform differential gene expression analysis, and visually explore the results.
Differential expression analysis
Differential gene expression analysis is one of the most common applications of RNA-sequencing data and enables genome-wide analysis of gene expression changes associated with biological conditions of interest.
Differential expression analysis is commonly used in the field of genomics, transcriptomics, and proteomics to study the molecular mechanisms underlying different biological processes, such as disease development, response to treatment, or adaptation to different environments.
Transcriptome assembly
A transcriptome is the full range of messenger RNA, or mRNA, molecules expressed by an organism. Transcriptome assembly using next-generation sequencing data is an important step in a wide range of biological studies at the molecular level. Transcriptome assembly is a process of reconstructing the complete set of full-length transcripts from RNA-seq data, which often include tens of millions of short-read sequences.
Pathway analysis
Pathway analysis is a set of tools used for research in life sciences intended to give meaning to high-throughput biological data. The aim is to help put genes from a differential expression analysis into a broader biological context. More specifically, pathway analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments.
Single cell expression analysis
Single-cell analysis allows the study of cell-to-cell variation within a cell population. It is the state‐of‐the‐art approach used particularly for studying the composition and development of complex tissues, scRNA-seq data sets typically comprise thousands of individual cells. Single-cell can unravel the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organised tissues/organs/organisms.
Other analysis
- MicroRNA data analysis
- Alternative splicing analysis
- Fusion gene detection
- Integrating RNA-seq and epigenomic data