Contents
Where can I get RNA-Seq data?
We compiled a list of resources where you can find RNA-seq data to start your oncology bioinformatics project:
- Elixir’s Expression Atlas.
- NCBI – National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/bioproject)
- TCGA – The Cancer Genome Atlas.
Can RNA be sequenced?
RNA-seq (RNA-sequencing) is a technique that can examine the quantity and sequences of RNA in a sample using next generation sequencing (NGS). It analyzes the transcriptome of gene expression patterns encoded within our RNA.
Does RNA have genes?
Each class of functional RNA is encoded by a relatively small number of genes (a few tens to a few hundred at most). The main classes of functional RNAs contribute to various steps in the informational processing of DNA to protein.
What can I do with RNA-seq data?
In addition to mRNA transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as miRNA, tRNA, and ribosomal profiling. RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5′ and 3′ gene boundaries.
How long does it take to analyze RNA-Seq data?
The sequencing reactions can take between 1.5 and 12 d to complete, depending on the total read length of the library. Even more recently, Illumina released the MiSeq, a desktop sequencer with lower throughput but faster turnaround (generates ∼30 million paired-end reads in 24 h).
How do I create a data frame in R?
To combine a number of vectors into a data frame, you simple add all vectors as arguments to the data. frame() function, separated by commas. R will create a data frame with the variables that are named the same as the vectors used.
How do you present gene expression data?
A common method of visualising gene expression data is to display it as a heatmap (Figure 12). The heatmap may also be combined with clustering methods which group genes and/or samples together based on the similarity of their gene expression pattern.
How is an RNA Seq analysis performed in R?
There are many steps involved in analysing an RNA-Seq experiment. Analysing an RNAseq experiment begins with sequencing reads. These are aligned to a reference genome, then the number of reads mapped to each gene can be counted. This results in a table of counts, which is what we perform statistical analyses on in R.
When to use CPM of 0.5 for RNA Seq?
A CPM of 0.5 is used as it corresponds to a count of 10-15 for the library sizes in this data set. If the count is any smaller, it is considered to be very low, indicating that the associated gene is not expressed in that sample. A requirement for expression in two or more libraries is used as each group contains two replicates.
How to do FASTQ analysis in your / Bioconductor?
Start the analysis by opening in your R session the Rrnaseq.R script Link. which contains the code shown in this slide show in pure text format. The FASTQ les are organized in the provided targets.txt le. This is the only le in this analysis work ow that needs to be generated manually, e.g. in a spreadsheet program.
Where are the gene identifiers stored in seqdata?
The first two columns in the seqdata dataframe contain annotation information. We need to make a new matrix containing only the counts, but we can store the gene identifiers (the EntrezGeneID column) as rownames. We will add more annotation information about each gene later on in the workshop.